NEW INSIGHTS INTO THE PHYSIOLOGY, BIOSYNTHESIS, AND MOLECULAR 

CONTROLS OF ORGANIC ACIDS AND POLYPHENOLS IN CIDER APPLES  

 

 

 

 

 

 

A Dissertation 

Presented to the Faculty of the Graduate School 

of Cornell University 

In Partial Fulfillment of the Requirements for the Degree of 

Doctor of Philosophy 

 

 

 

 

 

 

by 

Shanthanu Krishna Kumar 

December 2023



 

 

 

 

 

 

 

 

 

 

 

 

 

 

© 2023 Shanthanu Krishna Kumar



 

 

 

NEW INSIGHTS INTO THE PHYSIOLOGY, BIOSYNTHESIS, AND MOLECULAR 

CONTROLS OF ORGANIC ACIDS AND POLYPHENOLS IN CIDER APPLES  

 

 

 

Shanthanu Krishna Kumar, Ph. D.  

Cornell University 2023 

 

Abstract 

The organic acids and polyphenols in apple (Malus ×domestica) juice are responsible 

for hard cider flavor, aroma, color, and microbial stability. The second chapter of this dissertation 

describes how the malic acid marker Ma1 was able to categorize 217 cider apple cultivars into 

low (<2.4 g·L-1), medium (2.4-5.8 g·L-1), and high (>5.8 g·L-1) acidity groups. Triploid cultivars 

had a significant 0.36 g·L-1 greater titratable acidity than diploid cultivars (P = 0.0111). The third 

and fourth chapters focused on the effect of crop density and early tree shading on polyphenol 

development in cider apples respectively to understand source-sink relationships and explain the 

year-to-year variation in polyphenol content. There was a significant increase in the 

concentrations of most polyphenol compounds, including monomeric and oligomeric 

proanthocyanidin compounds in the low crop density treatment (5 fruit/cm2 trunk cross-sectional 

area) compared to the unthinned control (P < 0.0100). Transcriptome profiling through RNA 

sequencing indicated the critical genes involved in hydroxylation, methylation, and glycosylation 

in the phenylpropanoid pathway were upregulated in the low crop density treatment at 27 DAFB 

and 81 DAFB, which corresponded with increased concentration of phenylpropanoids. 



 

 

Specifically, there was a significant increase in the expression of the gene encoding 

anthocyanidin reductase (catalyzes the production of epicatechin) in the low crop density 

treatment at 27 and 81 days after full bloom (P < 0.0100). In Chapter 4, carbohydrate stress 

applied through early tree shading (1-5 weeks after full bloom) reduced phenolic acids and 

quercetin glycoside concentrations at harvest in the 60-tree shade treatment (60% of 

photosynthetically active radiation blocked) in comparison to the unshaded control, with minimal 

impact on production of procyanidin monomers and oligomers in cider apples. Fruit shaded trees 

were not significantly different from the control. This dissertation elucidates a new marker-based 

acidity classification system allowing for cultivar comparisons across geographical, seasonal, 

and horticultural considerations; illustrates the differing polyphenol accumulation patterns in the 

peel and flesh tissue; provides compelling evidence for a positive source-sink relationship with 

polyphenol development in cider apples; and lists transcription factors that could possibly be 

involved in the polyphenol production in cider apples.



 

v 

 

Biographical sketch 

Shanthanu Krishna Kumar was born to Krishnakumar Rangaswamy Naidu and 

Vijayalakshmi Krishnakumar in Jan 1994 and raised in Coimbatore, Tamil Nadu, India. 

Shanthanu did most of his schooling at Gopalswamy Doraiswamy Naidu matriculation higher 

secondary school before heading to Tamil Nadu Agricultural University, Coimbatore to pursue 

a Bachelor of Technology in Horticulture in 2011. After three years of study there, he moved to 

Truro, Nova Scotia, Canada to finish the last year and a half of an undergraduate dual degree 

program in Environmental Landscape Horticulture at the Agricultural Campus of Dalhousie 

University, where he worked on weed management in blueberry fields and nutraceutical 

properties of plant secondary metabolites. He then moved to the University of Guelph in 2016 to 

pursue a Master’s degree in Plant Agriculture working on enhancing shelf life of peaches and 

nectarines, before being admitted to Cornell in 2018 to pursue his doctorate in Horticulture where 

he studied cider apple acidity and polyphenols. In January 2024, Shanthanu will start a tenure 

track faculty position at Pennsylvania State University as an Assistant Professor of Tree Fruit at 

the University Park campus. 

In addition to his academic pursuits, Shanthanu was actively involved with the Indian 

Classical Music group `Society for the Promotion of Indian Classical Music and Culture 

Amongst Youth’ and performed at many venues across campus and the state of New York. He 

also worked as a Graduate Community Advisor at the Hasbrouck complex for three years during 

his PhD conducting different events for the graduate and postdoc communities at Cornell.   



 

vi 

 

 

 

 

 

 

 

 

 

 

 

To my parents who have worked hard all their life to provide for everything that I have today. 

To all my teachers who have really taken the time to mold me into who I am today.  

 



 

vii 

Acknowledgments 

 

I would like to thank my advisor Dr. Gregory Peck for making my PhD an enjoyable, 

exciting, and thought-provoking journey. Thank you for your positive reinforcement, 

encouragement, and knowledgeable support during critical times in my PhD. I appreciate the 

time you took to help me out in the field, help with career planning, advice, and directing me to 

the right resources.  

I would like to thank the members of my special committee for their help with my 

research and our many fruitful conversations. Thanks is due to Dr. Lailiang Cheng for being 

extremely accessible, helpful with any of my research questions, and his vast array of knowledge 

from which I could draw at any point of time. Thanks also to Dr. Kenong Xu and his lab members 

Drs. Laura Dougherty and Seunghyun Ban for assisting with acidity marker genotyping work 

and serving as a de facto base for our work in Geneva, NY. Thanks to Dr. Zhangjun Fei who 

helped with the bioinformatics portion of my RNA sequencing work.  

I would be remiss if I did not thank members of the Peck Lab who have been invaluable 

to my research work here at Cornell. Dr. Kamal Tyagi was a great resource, colleague, and friend 

who helped with a lot of the HPLC work. I owe a lot to Mike Brown whose efficiency could be 

matched by no one, for helping me out with field and lab work, and being patient with me for 

pestering him with a gazillion questions. David Zakalik, Aly Mashek, and I spent multiple hours 

together doing field work and processing apples, and I really appreciate their help. Thanks, is 

also due to Peck lab members Brittany, Jules, and all the summer interns who helped with field 

work and sample processing from 2018-2022. I would also like to thank Cornell Agricultural 

Experiment Station staff at the Cornell Orchards for maintaining the orchards.   

I would also like to thank Drs. Ian Merwin, Gayle Volk, and Thomas Chao for sharing 



 

viii 

their lists of cider apple accessions. Thank you to collaborators Drs. Nicholas Howard, Caroline 

Denancé, Thomas Chao, and Benjamin Gutierrez who helped navigate various aspects of my 

research work. Dr. Lynn Johnson from the Cornell Statistical Consulting Unit provided critical 

statistical advice. 

I would like to thank the following funding agencies for supporting my doctoral work. 

They include the National Institute of Food and Agriculture, U.S. Department of Agriculture - 

Hatch Funds, Cornell University’s Atkinson Centre for Sustainability—Sustainable Biodiversity 

Fund, and Cornell University’s Arthur Boller Research Fund. The Indian Council of Agricultural 

Research supported my study at Cornell under the Netaji Subhas International Fellowship.  

I could not have thrived during my PhD at Cornell without my Ithaca family. Thank you 

to Kasim, who was patient, kind, and helpful during my time at Cornell. Thank you also to 

Vikram, Kunal, Shriya, Sourbh, Sri, Aditya, Katherine, Devesh, Shantanu, Visveshwar, Karishni, 

Karthik, Alap, Justin, Savanna, Jenny, Adam, and David for cheering me up and for making my 

time in Ithaca a very sweet, memorable, and fun experience. 

I would like to thank my previous research mentors Drs. Jay Subramanian, Alan 

Sullivan, and Gopi Paliyath at the University of Guelph, Scott White and Vasantha Rupasinghe 

at Dalhousie University, and Nalina L at Tamil Nadu Agricultural University who kindled the 

research curiosity in me and encouraged me to continue in the academic track. I would like to 

end the acknowledgements by thanking my extended family including my grandparents, uncle, 

aunt, cousins, brother, sister-in-law, and finally my parents who believed in me, encouraged me 

to push myself, and have sacrificed a lot to see me succeed.  

 



 

ix 

TABLE OF CONTENTS 

Abstract ........................................................................................................................ iii 

Biographical sketch ...................................................................................................... v 

Acknowledgments ....................................................................................................... vii 

Abbreviations ............................................................................................................. xiii 

Literature review .......................................................................................................... 1 

Hard cider and cider apple industry in the United States ................................................................... 1 

Challenges and opportunities for cider apple and hard cider industry in the United States ............ 2 

Cider apple acidity classification systems .............................................................................................. 4 

Acidity pathways in Malus sp. ................................................................................................................ 4 

Polyphenols in cider apple ...................................................................................................................... 5 

Proanthocyanidins/Tannins .................................................................................................................... 6 

Methods to estimate total polyphenol content ....................................................................................... 9 

Phenylpropanoid pathways in Malus species ...................................................................................... 10 

Classification of polyphenol compounds ............................................................................................. 13 

Genetic controls of the phenylpropanoid pathway ............................................................................. 13 

Molecular mechanisms of abiotic stress influence on the phenylpropanoid pathway ..................... 14 
Water Relations .................................................................................................................................. 16 
Light ................................................................................................................................................... 19 
Temperature ........................................................................................................................................ 22 

RNA Sequencing as a technique to understand molecular controls of the phenylpropanoid 

pathway .................................................................................................................................................. 23 

Research objectives - developing a new genetic based acidity classification system and enhancing 

understanding of polyphenol synthesis in response to carbohydrate availability. ........................... 24 

References .............................................................................................................................................. 26 

Classifying Cider Apple Germplasm using Genetic Markers for Fruit Acidity .. 37 

Abstract .................................................................................................................................................. 37 



 

x 

Introduction ........................................................................................................................................... 38 

Materials and Methods ......................................................................................................................... 41 
Study Location and Accession Selection ............................................................................................ 41 
DNA Extraction and Accession Genotyping ...................................................................................... 42 
Fruit Sampling and Processing Procedures ......................................................................................... 43 
Juice Titratable Acidity and pH .......................................................................................................... 44 
Statistical Analysis.............................................................................................................................. 44 

Results .................................................................................................................................................... 45 
Fruit Maturity, Titratable Acidity, and pH .......................................................................................... 45 
Allelic Demographics ......................................................................................................................... 46 
Relationships among the Genotypes and Phenotypes ......................................................................... 47 
Classifying Cider Apples by Phenotype, Genotype, and Region of Origin ........................................ 52 

Discussion ............................................................................................................................................... 55 
Marker-based System for Categorizing Cider Apples ........................................................................ 55 
Within and Among Year Variability................................................................................................... 58 
Diploid versus Triploid Accessions .................................................................................................... 59 
Evaluated Germplasm ......................................................................................................................... 59 
Future Cider Apple Classification Recommendations ........................................................................ 60 

References .............................................................................................................................................. 62 

Reduced crop density enhances total polyphenol content to improve cider apple 

quality .......................................................................................................................... 67 

Abstract .................................................................................................................................................. 67 

Introduction ........................................................................................................................................... 68 
The hard cider industry ....................................................................................................................... 68 
Classification of apple polyphenols .................................................................................................... 69 
Proanthocyanidin production in apples ............................................................................................... 69 
Recent research on the effect of orchard management practices on cider apple polyphenols ............ 71 
Research hypothesis and objectives .................................................................................................... 72 

Materials and Methods ......................................................................................................................... 72 
Trial Location and Experimental Design ............................................................................................ 72 
Fruitlet and harvest sampling, and return bloom assessment .............................................................. 74 
Fruit Quality Analyses ........................................................................................................................ 75 
Juice Chemistry Analysis ................................................................................................................... 76 
High Performance Liquid Chromatography Analysis......................................................................... 77 
Proanthocyanidin extraction and phloroglucinol analysis .................................................................. 79 
RNA Extraction and Library Preparation ........................................................................................... 81 
RNA Sequencing, Differential Gene Expression, and Gene Enrichment Analysis ............................ 82 
Statistical Analysis.............................................................................................................................. 83 

Results .................................................................................................................................................... 84 
Yield and return bloom ....................................................................................................................... 84 
Fruit mass and circumference ............................................................................................................. 84 
Harvest ripeness and quality parameters ............................................................................................. 86 
Juice Quality Parameters .................................................................................................................... 86 
Juice polyphenol monomers ............................................................................................................... 89 



 

xi 

Porter’s Perfection flesh and peel tannin and polyphenols ................................................................. 90 
RNA sequencing and differential gene expression ............................................................................. 94 
Gene enrichment analyses .................................................................................................................. 95 
Phenylpropanoid pathway genes ...................................................................................................... 100 
Transcription factor DEGs ................................................................................................................ 102 

Discussion ............................................................................................................................................. 104 
Polyphenol concentrations are negatively correlated with crop density ........................................... 104 
Carbohydrate availability has a role to play in accumulation of polyphenols in cider apple ............ 105 
RNA Sequencing, gene enrichment analysis and differential gene expression ................................ 106 
Pre-harvest and harvest characteristics ............................................................................................. 107 
Crop density significantly influences polyphenol production in only high polyphenol cultivars ..... 108 
Unique accumulation trends of phloridzin and proanthocyanidins in flesh and peel tissue.............. 109 
Tannin accumulation trends in cider apples ...................................................................................... 110 
Tannins breakdown into proanthocyanidin monomers and oligomers during the growing season .. 111 
Transcription factors possibly involved in regulation of the phenylpropanoid pathway .................. 112 

Conclusion ............................................................................................................................................ 113 

References ............................................................................................................................................ 114 

Early season tree shading decreases phenolic acids and quercetin glycosides, but 

not proanthocyanidin monomer and oligomer concentrations in cider apples at 

harvest ....................................................................................................................... 122 

Abstract ................................................................................................................................................ 122 

Introduction ......................................................................................................................................... 123 

Materials and Methods ....................................................................................................................... 126 
Trial Location and Experimental Design .......................................................................................... 126 
Fruitlet and harvest sampling ............................................................................................................ 128 
Fruit Quality Analyses ...................................................................................................................... 128 
Juice Chemistry Analysis ................................................................................................................. 129 
High Performance Liquid Chromatography Analysis....................................................................... 130 
Statistical Analysis............................................................................................................................ 132 

Results .................................................................................................................................................. 132 
Fruit harvest characteristics .............................................................................................................. 132 
Fruit juice characteristics .................................................................................................................. 135 
Juice polyphenol compounds ............................................................................................................ 137 
Total polyphenols in the peel and flesh - Folin-Ciocalteau assay ..................................................... 140 
Flesh and peel polyphenol compounds measured by HPLC ............................................................. 142 

Discussion ............................................................................................................................................. 145 
Effect of sunlight exposure on cider fruit quality ............................................................................. 145 
Early tree shading results in reduced polyphenols in cider apple juice ............................................ 146 
Most polyphenol compounds are produced during the 1-5 WAFB period ....................................... 148 
Accumulation patterns of different polyphenol classes in cider apple juice, flesh, and peel ............ 149 
Polyphenol measurement methods ................................................................................................... 151 
Implications for future research ........................................................................................................ 152 

References ............................................................................................................................................ 153 



 

xii 

Concluding remarks and reflections ....................................................................... 159 

References ............................................................................................................................................ 164 

Appendix i ................................................................................................................. 165 

Appendix ii ................................................................................................................ 178 

Appendix iii ............................................................................................................... 182 



 

xiii 

 

Abbreviations 

 

ABA Abscisic acid 

ANR Anthocyanidin reductase 

bHLH Basic helix loop helix 

CTAB Cetyltrimethyl ammonium bromide 

DA Degree of absorbance 

DAD Diode array detector 

DAFB  Days after full bloom 

DEG Differentially expressed gene 

EDTA Ethylenediaminetetraacetic acid 

ERF Ethylene response factor 

FC  Folin-Ciocalteu  

GAE  Gallic acid equivalent 

HCl Hydrochloric acid 

HPLC                                 High performance liquid chromatography 

LAR Leucoanthocyanidin reductase 

LARS Long Ashton Research Station 

LiCl Lithium chloride 

Md  Malus domestica 

MYB Myeloblastosis 

N  Newtons 

NaCl Sodium chloride 

PA Proanthocyanidin 

PVP Polyvinyl pyrrolidone 



 

xiv 

SPI  Starch pattern index 

SSC  Soluble solids concentration 

TA  Titratable acidity 

TCSA  Trunk cross-sectional area 

TPC Total polyphenol content 

RNA-Seq                            RNA sequencing 

WAFB                                Weeks after full bloom 

UTC                                    Unthinned Control 

 

 



 

1 

 

Chapter 1 

Literature review 

Hard cider and cider apple industry in the United States 

The hard cider industry in the United States has been experiencing a renaissance in 

production and consumption in the past decade (Miles et al., 2020). Hard cider refers to the 

alcoholic beverage made from fermenting apple cider or juice. Total cider sales in the United States 

topped $537 million in 2021 (NielsonIQ, 2022). The per capita consumption of specialty drinks 

including cider increased by 487% between 2010 and 2018. (Degenhard, 2019). The hard cider 

industry is projected to grow with a compounded annual growth rate (CAGR) of 3.5% at least until 

2027 (North America Cider Market, 2022), despite a decreasing trend of growth in overall alcohol 

sales (NielsonIQ, 2022). Regional cider brand sales have witnessed a spectacular CAGR of 20.3% 

between 2018 and 2021, while national cider brand sales have decreased by an average of 10% in 

the past three years. The share of regional cider sales topped 50% in the first quarter of 2022, 

indicating a significant demand and potential for diverse and locally produced hard cider 

(NielsonIQ, 2022). As of January 2018, the United States had a total of 820 cidermakers, with New 

York leading the states with the greatest number of cideries (Conway, 2020). There are currently 

more than 125 licensed cideries in NY state (New York Cider Association, 2020). An economic 

impact study commissioned by the New York Cider Association indicated that the hard cider 

industry generated $1.7 billion in economic activity in the State (New York Cider Association, 

2020). Hard cider has the distinction of being the most gender balanced alcohol drink with an 

almost equal split between men and women drinkers (NielsonIQ, 2019). The consumption skews 

towards the younger generation, with Americans aged 18-29, having a 3-fold increase in 



 

2 

consumption in comparison to their older counterparts aged between 50-64 (Kuntz, 2023). With 

all the above-mentioned trends, the hard cider industry has the potential to establish itself as a 

standard drink of choice for Americans in the coming decades. 

Arguably the most important ingredient in hard cider, cider apples have unique 

characteristics that are valued in the hard cider production process – high sugars, high acid, and a 

level of polyphenols that provides both bitterness and astringency (mouth feel) to enhance and 

maintain flavor right through fermentation and storage. With the high demand for cider apples 

across the United States, apple growers have been cautiously planting cider orchards since the 

early 2010’s to cater to the burgeoning hard cider industry. Even still, demand for cider apples far 

outstrips current supply (Becot et al., 2016; Pashow, 2018; Zakalik and Peck, 2023). The growing 

of cider apples does not emphasize characteristics required of high-quality fresh market apples 

including cosmetic appearance, size, and storability, thus requiring the development of their own 

set of production and orchard management practices.   

Challenges and opportunities for cider apple and hard cider industry in the United States 

 The biggest challenge for cider makers is the availability of local specialized cider apple 

cultivars in their region. Once planted, it can take anywhere from four to six years to reach 

commercial production levels, and hence, supply cannot be increased quickly and there is a lag 

between demand and supply of these apples (Ostrom et al., 2022). Becot et al. (2016) assessed that 

cideries in Vermont were looking to expand and many of those cideries were interested in obtaining 

locally grown specialized cider apples. Gottschalk et al. (2017) and Peck and Miles (2015) found 

that cideries were interested in sourcing apples locally in Michigan and New York, respectively. 

Ostrom et al. (2022) found that limited availability of specialty cider cultivars acted as a barrier to 

cidery expansion. 



 

3 

 There are many horticultural challenges with growing cider apple cultivars and an 

important one is their biennial bearing tendency (Zakalik et al., 2023). While biennial bearing is 

not restricted to cider apples, some of the cider apple cultivars such as ‘Kingston Black’ are heavily 

prone to biennial bearing irrespective of management practices such as pruning, thinning, and crop 

load management (Zakalik et al., 2023). Many of these cultivars are from European regions such 

as England, France, and Spain, and they are not adapted to the climatic conditions in the US (Miles 

et al., 2020). Cider apples tend to bloom later and have an extended bloom time, which can 

complicate thinning efforts (Miles et al., 2020) and make them more susceptible to diseases such 

as fire blight caused by Erwinia amylovora, whose inoculum can enter the plant via an open bloom 

(Wallis et al., 2021). Furthermore, in NY and other states, streptomycin resistant Erwinia 

amylovora is of major concern (Wallis et al., 2021). Growers are reluctant to bring in nonresistant 

cider cultivars into their orchards (Pashow, 2018). As Peck and Knickerbocker (2018) summarized, 

“there is still ambiguity with regards to which cultivars will be the most productive and profitable 

in the US”.  

 There are also economic unknowns to the long-term profitability of cider apples. As Miles 

et al (2020) mentions, “growing cider apples is a 20-30-year time commitment.” Economic studies 

have produced variable outcomes depending on the production systems (Peck and Knickerbocker, 

2018). Furthermore, there is only a single market for cider apples, increasing risk for growers in 

case the demand cools for cider apples (Becot et al., 2016). However, research on mechanical 

harvesting has shown reduced production costs and increased profitability under any farm scale 

(Karl et al., 2022). 

 

 



 

4 

Cider apple acidity classification systems 

 Three different classification systems have primarily been used to classify cider apples 

across the world. They are the Long Ashton Research Station classification system, The French 

classification system, and the Spanish classification system. More detailed information about 

these systems is presented in the introduction section and Figure 2.3. 

Acidity pathways in Malus sp. 

The genetic underpinnings of apple acidity were first described in 1959 and subsequent 

studies have led to the identification and characterization of malic acid (Ma) locus on linkage 

group 16 (Maliepaard et al., 1998; Nybom, 1959; Visser and Verhaegh, 1978; Yao et al., 2008; Xu 

et al., 2012). This locus has been reported to control 17% to 42.3% of the variation in acid 

concentration in apple fruit (Xu et al., 2012). The gene underlying Ma, named Ma1, has since been 

identified to encode an aluminum-activated malate transporter-like protein (Bai et al., 2012, Khan 

et al., 2013). A single nucleotide mutation from the guanine (G) to adenine (A) at position 1,455 

in the coding sequence of Ma1 results in a premature stop codon that truncates 84 amino acids at 

the C-terminus, causing low acidity (Bai et al., 2012; Li et al., 2020). Therefore, the Ma1 allele, 

with “G” at position 1,455 is associated with high acid (Ma), and the Ma1 allele with “A” at 

position 1,455 is associated with low acid (ma). This distinction defines the difference between the 

dominant Ma and recessive ma alleles. However, the dominance of the Ma1 allele is incomplete 

which was indicated by the wide range of TA values for heterozygous Mama allele, suggesting 

that both additive and dominant effects of the Ma1 allele exist (Verma et al., 2019; Xu et al., 2012). 

Recently, it was found that in response to excess nitrate accumulation, the MdBT2 protein 

modulated and downregulated the expression of MdCIbHLH1 and MdMYB73, which are involved 

in regulation of malate related genes, thus reducing acidity in apples (Zhang et al., 2020). 



 

5 

Linkage group 8 also contains an important quantitative trait locus (QTL), named Ma3 

(Verma et al., 2019) that regulates apple acidity (Kumar et al., 2013; Liebhard et al., 2003; Ma et 

al., 2015; Sun et al., 2015). The Ma3 locus has recently been shown to have an incomplete 

dominance effect on apple acidity (Rymenants et al., 2020). Jia et al. (2018) identified two natural 

variations in hierarchical epistatic genes MdSAUR37 and MdPP2CH that affect fruit acidity in the 

Ma3 region. Additionally, three more QTLs Ma4, Ma5 and Ma6 located on chromosomes 6, 1, and 

4, respectively, were found to be relevant for fruit acidity levels (Ban and Xu, 2020; Rymenants et 

al., 2020). Ma4, Ma5, and Ma6 explain more variation within the background of the Mama allele 

and for accessions with high acidity levels (>10 g·L-1) (Ban and Xu, 2020). 

With the development of genetic markers for apple acidity, especially markers Ma1 and 

Ma3, there has been some interest to understand whether a genetic marker-based classification 

could be feasible for cider apples to aid cider apple breeding, cultivar selection, and cider 

production. Hence, we decided to genotype cider apple cultivars to develop a genetic based 

classification system for cider apples. 

Polyphenols in cider apple 

Abundant in diversity and possessing an extensive history of domestication and breeding 

(Duan et al., 2017), apples are rich in many vitamins, minerals, fiber content, and polyphenol 

compounds (Tsao et al., 2005; van der Sluis et al., 2001). In the United States, apples are a major 

source of nutrition and represent 22% of the total polyphenols consumed in an average US diet 

(Vinson et al., 2001). Apple polyphenols are attributed to improve flavor, texture, color, and 

microbial stability. Apple polyphenols such as phloridzin, catechin, epicatechin, chlorogenic acid 

and proanthocyanins possess antioxidant properties epidemiologically associated with reduced 

cardiovascular diseases and certain cancers (Boyer and Liu, 2004; Sun and Liu, 2008). Apart from 



 

6 

dietary benefits of phenylpropanoids, they are also necessary for contributing bitterness and 

astringency to hard ciders through short and long chain proanthocyanidins respectively (Lea and 

Arnold, 1978). The oxidation of proanthocyanidins, phloridzin, and phenolic acids give the hard 

cider its characteristic color (Janovitz-Klapp et al., 1990). Some red and pink-fleshed cultivars are 

becoming popular in making rose ciders (van Nocker and Gottschalk, 2017). Hydroxycinnamic 

acids play an important role in providing unique smoky flavor and aroma in cultivars such as 

‘Kingston Black’ (Whiting, 1975), however, greater concentrations of hydroxycinnamic acids 

slows fermentation rates (Cairns et al., 2021) and can also lead to accumulation of ethylphenol 

volatile organic compounds which are produced by Brettanomyces bruxellensis and give an 

unfavorable “barnyard” or “leather” aroma to cider (Chatonnet et al., 1992). However, these 

aromas are preferred in some cidermaking styles. 

Proanthocyanidins/Tannins   

  Proanthocyanidins building blocks such as catechin and epicatechin, and their tannins 

polymers are a diverse group of water-soluble phenolic compounds, and are known for their ability 

to precipitate alkaloids, gelatins, and other proteins (Bate-Smith, 1962). In the context of cider, 

these compounds play a crucial role in providing mouthfeel by interacting with saliva proteins, 

causing their precipitation, and reducing saliva's lubricating properties. This leads to the perception 

of "dryness" as the coefficient of friction in the mouth increases (Prinz and Lucas, 2000). 

In apples, the formation of procyanidins, also known as condensed tannins, is facilitated 

by the polymerization of catechin and epicatechin flavan-3-ol monomers with catechin initiators 

and epicatechin elongation units (Delage et al., 1991). While hydrolysable tannins are not naturally 

present in apple fruit, exogenous tannins of this class can be introduced to ciders during production 

when using wood barrels, staves, or chips (Puech et al., 1999). Although catechins, epicatechins, 



 

7 

and their polymers are found in both the peel and flesh of all apple varieties, commercial fresh-

market and processing cultivars typically have less concentrations of these compounds 

(Thompson-Witrick et al., 2014; Zhang et al., 2010). In contrast, cider-specific cultivars often 

exhibit juice flavan-3-ol concentrations exceeding 2000 mg·L-1, while those in fresh-market and 

processing cultivars are usually below 100 mg·L-1 (Guyot et al., 2003; Kahle et al., 2005). The 

length of the procyanidin polymers influences their taste and aroma; monomers and polymers with 

fewer than four elongation units, such as epicatechins and catechins, are bitter tasting, while longer 

polymers are more reactive with proteins and contribute to the astringency of ciders (Lea & Arnold, 

1978). Notably, the mean degree of procyanidin polymerization varies among apple cultivars, 

resulting in distinct organoleptic sensations of bitterness and astringency, with apple varieties 

having anywhere from two to a maximum of approximately 70 subunits (Bourvellec et al., 2015; 

Sanoner et al., 1999). Moreover, the presence of either 4–8 or 4–6 carbon linkages between 

elongation units in procyanidins further diversifies the potential structures of these compounds. In 

apples, the 4–8 carbon linkage is the predominant form (Yeap Foo and Lu, 1999). 

The synthesis of flavan-3-ols in apples primarily occurs during the cell division phase of 

fruit growth, typically within the first 6 weeks after anthesis (Henry-Kirk et al., 2012; Ju et al., 

1995; Renard et al., 2007; Zhang et al., 2010). Subsequently, the flavan-3-ol monomers undergo 

polymerization, with mean polymer length increasing throughout the remaining growing season 

(Renard et al., 2007; Zhang et al., 2010). The greatest concentrations of polyphenol-synthesizing 

enzymes, including phenylalanine ammonia-lyase, chalcone-synthase, and dihydroflavonol 

reductase, are found in young fruit (Ju et al., 1995, 1997). While ultraviolet light exposure, 

particularly during fruit ripening, stimulates anthocyanin and flavonol synthesis, its impact on 

flavan-3-ol synthesis earlier in the season appears to be limited (Awad et al., 2000; Ju et al., 1997; 



 

8 

Takos et al., 2006). As a result, tannin concentrations remain relatively stable during fruit ripening, 

and there seems to be no advantage in early or late harvesting to optimize their concentrations. 

However, stored fruit may experience an increase in polyphenol levels, likely due to water loss 

rather than de novo production (Ewing et al., 2019). 

Despite the significance of tannins in cider quality, the factors influencing tannin 

development and accumulation in apples remain incompletely understood. Tannin concentrations 

in apples from the same orchard have been found to vary dramatically among growing seasons. 

For example, a study spanning a 10-year period in Long Ashton, England, investigated 'Dabinett' 

and 'Tremlett's Bitter' juice tannin concentrations, revealing fluctuations of ±50% among years 

(Lea, unpublished data). Similarly, in a study of high-tannin cider apples in coastal and eastern 

Washington, a variation of ±25% was observed over four years, with the variation among years 

having a more significant effect on apple tannin concentration than the geographic region of fruit 

production (Alexander et al., 2016). Environmental and seasonal fluctuations are further 

compounded by a lack of understanding regarding how horticultural practices, such as orchard 

design, nutrient management, tree spacing, crop density, and pruning, may influence polyphenol 

concentrations in cider apples. 

Addressing this knowledge gap, Karl (2020) conducted several experiments exploring the 

relationship between carbohydrate availability and polyphenol concentrations in cider fruit. 

Findings from these studies suggested that total polyphenol synthesis in fruit follows a source-sink 

relationship with carbohydrate availability, aligning with the cell division period of fruit 

development. During this stage, typically within the first 45 days after petal fall, the carbohydrate 

balance in apples becomes tenuous, with growing extension shoots exporting minimal to no 

carbohydrates to the fruit. Consequently, developing fruit heavily relies on carbohydrate resources 



 

9 

from the still not fully grown adjacent spur-leaf canopy (Grappadelli et al., 1994). Metabolic 

demands during this period are influenced by factors such as crop density, canopy size, and day 

and night temperatures, which may lead to carbohydrate demand exceeding carbon fixation rates, 

particularly under conditions of low photosynthetic active radiation, potentially resulting in fruit 

abscission or reduced fruit cell division rates (Bepete and Lakso, 1998; Lakso et al., 2001). 

Limiting carbohydrate availability during this critical phase, either due to reduced net 

photosynthesis (e.g., less light availability) or competition with other sinks (e.g., fruit), can lead to 

reduced cell number and growth, ultimately impacting the maximum fruit size at harvest (Wünsche 

et al., 1996). Several studies have indicated that crop density negatively correlates with polyphenol 

content in apple flesh and juice, both in fresh-market and cider-specific cultivars (Awad et al., 

2001; Guillermin et al., 2015; Stopar et al., 2002; Zakalik et al., 2023). Furthermore, the location 

within the tree canopy can influence flavan-3-ol concentrations, with apples from more exposed 

areas exhibiting greater flavan-3-ol concentrations (Awad et al., 2001; Feng et al., 2014). Karl 

(2020) reported that juice from fruit in the top of tree canopies had 33% greater total polyphenol 

concentrations than juice from fruit in the interior, suggesting localized differences in carbohydrate 

availability during fruit development may be responsible for these variations. 

However, there are still gaps in the literature with regards to the accumulation patterns of 

individual polyphenol compounds throughout the growing season and how they react to differing 

crop densities and carbohydrate availability. Exploring these topics further is necessary to 

understand the effect of carbohydrate status on polyphenol production in cider apples. 

Methods to estimate total polyphenol content  

Many assays have been used to quantify polyphenol content in cider apples including the 

Löwenthal permanganate titration, Folin-Ciocalteau, 4-dimethylaminocinnamaldehyde, and the 



 

10 

bovine serum albumen precipitation methods (Ma et al., 2019). The Löwenthal permanganate 

method has been used by the LARS system to classify cider apples according to their tannin 

concentrations into bitter and sweet categories. The Löwenthal permanganate method has a wider 

range of detection and is repeatable; however, it is not suitable for high throughput analysis of total 

polyphenol content for a large number of samples (Ma et al. 2019). The Folin-Ciocalteau method 

is a reliable, repeatable, and consistent method for polyphenol measurement while also being time 

and cost effective, however, it does not measure concentrations of individual polyphenol 

compounds, and also measures other ferric reducing compounds such as ascorbic acid, sulfur 

dioxide and reducing sugars (Everette et al., 2010). However, the Folin method is more consistent 

and precise than the Löwenthal permanganate method that was used for many decades in prior 

cider apple research studies (Ma et al., 2019). A conversion factor between the Löwenthal 

permanganate test and the Folin-Ciocalteau method was developed by the Peck Lab (Peck et al. 

2021) by comparing standard curves for both the methods and identifying the linear relationships 

between these two assays. High performance liquid chromatography (HPLC) measurements of 

individual polyphenols with standards is the most precise for polyphenol measurements and gives 

us an accurate snapshot of the polyphenol profiles by providing accurate concentrations of 

individual polyphenol compounds. 

Phenylpropanoid pathways in Malus species 

Malus x domestica is the most used hybrid in fresh apple production. However other 

species of Malus such as M. orientalis, M. sieversii, M. sylvestris, M. floribunda etc. are being 

used for various purposes around the world including for specialized hard cider production (Duan 

et al., 2017; Miles et al., 2020). These Malus species have a wide variation in anthocyanin, 

proanthocyanidin, flavonol, and other phenylpropanoid pathway products (Beach et al., 1905; 



 

11 

Thompson-Witrick et al., 2014; Wang et al., 2018a). Due to their importance to human needs and 

plant protection mechanisms, there has been consistent efforts to identify the molecular 

mechanisms underlying the control and regulation of the phenylpropanoid pathway. Numerous 

research studies have unearthed a trove of mechanisms involving control of anthocyanin 

production and to a lower extent, flavonoid and proanthocyanidin production (Ramakrishna and 

Ravishankar, 2011; Li et al., 2015; Liu et al., 2015).  

Phenylpropanoids are an important part of secondary metabolism in plants, and they have 

essential physiological responses such as adaptation to abiotic stress. Drought (Nakabayashi et al., 

2014a, b), temperature (Xie et al., 2012), salinity (An et al., 2017a), UV light (Henry-Kirk et al., 

2018), wounding (An et al., 2019b), and other abiotic factors play in role in activating the 

phenylpropanoid pathway to stabilize and adapt the plant to the new environment (Ramakrishna 

and Ravishankar, 2011). In most cases, abiotic stress leads to an enhanced biosynthesis on 

anthocyanin and other polyphenols, although it can activate mechanisms involving repression of 

certain genes in the phenylpropanoid pathway. 

Figure 1.1 provides the progression of the phenylpropanoid pathway with phenylalanine as 

the precursor for various phenylpropanoids. Phenylalanine is mainly produced through the 

shikimic acid pathway with 3-deoxy-d-arabino-heptulosonate-7-phosphate (DAHP), erythrose-4-

phosphate (E-4-P), and phosphoenol pyruvate (PEP) as the important precursors in the pathway.  

 

 

 

 



 

12 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1.1 The phenylpropanoid pathway in apple fruit (Malus х domestica) Adapted from 

(Henry-Kirk et al., 2012). PAL - Phenyl ammonia lyase (PAL), Cinnamate-4-hydroxymate (C4H), 

4-coumarate:coenzyme A ligase (4CL), Chalcone synthase (CHS), Chalcone Isomerase (CHI), 

Flavanone 3-hydroxylase (F3H), Flavanone 3´-hydroxylase (F3´H), Dihydroflavonol 4-reductase 

(DFR), Anthocyanidin reductase (ANR), UDP-glucose flavonoid 3-O-glucosyl transferase 

(UFGT), Flavonol synthase (FLS), Leucoanthocyanidin reductase (LAR), Glycosyl transferases 

(GT), Hydroxycinnamoyl/hydroxycinnamoyl CoA shikimate (HQT/HCT), p-coumarate 3-

hydroxylase (C3H). 

 

 

3 Malonyl-CoA 

C3H 

CHS 

PAL 

Flavanones 

Flavonols 

Anthocyanidin 

Anthocyanin 

Leucoanthocyanidins 

Dihydroflavonol

s 

Catechin 

Proanthocyanidins (condensed tannins) 

Epicatechin 

Phloridzin 

Phenylalanine 

Cinnimate 

p-Coumarate 

p-Coumaroyl-CoA 

Chalcones 

p-Dihydrocoumaroyl-CoA 

Phloretin 

p-Coumaroyl Quinic Acid Chlorogenic Acid 

C4H 

Shikimate 

Pathway 

HCT/HQT 

ANR 

F3H 

F3´H 

UDP-glucose, GT1/GT2 

LAR1/LAR2 

DFR 

CHI 

LDOX/ANS 

FLS 

CHS 

4CL 

UFGT 



 

13 

Classification of polyphenol compounds 

 Polyphenols could be grouped into five categories – dihydrochalcones (phloridzin), 

phenolic acids (Chlorogenic acid and hydroxycinnamic acids such as caffeoylquinic and coumaric 

acid), flavonols (quercetin glycosides), proanthocyanidins (catechin, epicatechin, and their 

oligomers, polymers of catechin and epicatechin otherwise called as tannins), and anthocyanins 

(cyanidin glycosides) (Table 1.1; McGhie et al., 2005; Henry-Kirk et al., 2012). Among them, 

flavonols and anthocyanins are present exclusively in peel tissue except for red fleshed apples 

(Takos et al., 2006; Renard et al., 2007; Ban et al., 2007; Henry-Kirk et al., 2012). 

Hydroxycinnamic acids such as phlorizin (phloretin glycoside) are highly abundant in cider apple 

flesh, peel, as well as in leaves and bark (Gutierrez et al., 2018). Phenolic acids such as chlorogenic 

acid are highly abundant in apple fruit flesh and peel (Renard et al., 2007). 

 

Table 1.1 Classification of polyphenol compounds into five classes with examples. 

Dihydrochalcones Proanthocyanidins Phenolic Acids Flavonols Anthocyanins 
Phloretin 

Phloridzin 

Catechin 

Epicatechin 

Procyanidin A1, A2, 

B1, B2, C1, C2 

Tannin polymers 

Chlorogenic acid 

Caffeoylquinic 

acid 

Coumaric acid 

Quercetin 

glycoside 

Quercetin 

rutinoside 

Quercetin 

glucoside 

Rutin 

Avicularin 

Cyanidin-3-

glucoside 

 

Genetic controls of the phenylpropanoid pathway 

Considerable research has been undertaken to identify the regulatory mechanisms of 

phenylpropanoid production in apple fruit. While the genes involved in the production of 

phenylpropanoids have been elucidated to a great extent, the transcriptional and epigenetic control 

of these genes is far from fully studied and many research studies have focused on transcription 

factors and their role in regulating the phenylpropanoid pathway. The myeloblastosis viral 



 

14 

oncogene homolog (MYB) family of genes have been extensively studied and they play a major 

role in regulation of the phenylpropanoid pathway through transcriptional activation, degradation, 

and gene promoter activation (Li et al., 2015). In apple, about 229 MYB genes have been identified 

although only a fraction of those have been biosynthesized (Ban et al., 2007; Takos et al., 2006). 

They work in conjunction with other transcription factors (TF’s) such as the basic helix loop helix 

protein (bHLH) and beta-transducin repeat proteins (WDR) (Allan et al., 2008). Ethylene response 

factors (ERF’s) also play a critical role in the phenylpropanoid pathway. MdERF1B and MdERF3 

have been identified to promote proanthocyanidin and anthocyanin synthesis in collaboration with 

MdMYB genes such as MdMYB1 and MdMYB11 (An et al., 2018b; Zhang et al., 2018). 

Molecular mechanisms of abiotic stress influence on the phenylpropanoid pathway 

As far as the influence of abiotic factors on apple polyphenols is concerned, there seems to 

be a 3-stage control of phenylpropanoid biosynthesis. The MYB genes are responsive to most 

abiotic stress factors and mainly regulate anthocyanin production apart from affecting other classes 

of polyphenols as well. While the phenylpropanoid pathway genes form the first level of 

regulation, they are in turn regulated by MdMYB1, the master regulator and other TF’s directly 

(Figure 1.2). The third level of regulation involves the manipulation of MdMYB1 itself by other 

TF’s to enhance or decrease its activity which has downstream effects on the phenylpropanoid 

synthesis genes and their products (Figure 1.2). 

There are different abiotic factors affecting the phenylpropanoid pathway including water, 

light, temperature, salinity, heavy metals, and radiation. I am primarily going to focus on the three 

factors – water, light, and temperature. As far as water is concerned, both flooding and drought can 

affect polyphenol production, however droughts are more common in the US. Light is a complex 

factor affecting cider apples either directly through fruit exposure to sunlight or through regulation 



 

15 

of carbohydrate production through photosynthesis. While photosynthetically active radiation is 

important for photosynthesis and carbohydrate production, UV light could be detrimental to 

polyphenol production. Detailed molecular mechanisms on these abiotic factors influencing 

polyphenol production are presented below.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1.2 Regulation of the phenylpropanoid metabolism by abiotic stress factors through 

transcriptional, epigenetic and genetic means. Basic helix-loop-helix (bHLH), Basic leucine zipper 

(bZIP), Ethylene response factors (ERF) 

 

 

Abiotic Stress Factors  

Other MYB, bHLH, bZIP, ERF, WRKY, BT2, 

COP, UVR8 Transcription factors  

Regulation of phenylpropanoid content  

Phenylpropanoid pathway genes 

MdMYB1 – Central 

transcriptional 

regulation of 

phenylpropanoid 

pathway 



 

16 

Water Relations 

Drought is a common environmental factor that adversely affects the productivity of the 

apple tree (Anjum et al., 2011). When the available water in the soil decreases below the threshold 

of water required for optimal functioning of the tree, the onset of drought occurs. Usually, droughts 

are caused due to water or rainfall shortages and high temperatures where the transpiration rate is 

greater than the available soil water (Anjum et al., 2011). Drought results in a cascade of signaling 

changes in the physiological status and response of the plant such as weakened photosynthesis, 

membrane permeability, and excess reactive oxygen species accumulation. Drought also plays a 

profound role in regulation of the phenylpropanoid metabolism in apples. It is well known that 

drought induces anthocyanin biosynthesis in apple fruit (Ramakrishna and Ravishankar, 2011; 

Allan et al., 2008; Kovinich et al., 2014; Sperdouli and Moustakas, 2012).  

The MdMYB1 gene plays an important regulatory role in polyphenol production as it has 

been shown to directly enhance the activity of chalcone synthase and dihydroflavanol reductase 

(Ban et al., 2007; Espley et al., 2007; Takos et al., 2006). While the MdMYB1 gene plays an 

important role in the flesh of the fruit, its allele MdMYB10 and MdMYBA play a crucial role in 

modulation of the anthocyanin biosynthesis pathway in the peel of the apple fruit (Espley et al., 

2007; Takos et al., 2006). Further, the complex involving MYB genes, bHLH, and WDR proteins 

play an important signaling role in epigenetic regulation of the anthocyanin biosynthesis pathway 

and the phenylpropanoid pathway in general (Allan et al., 2008; Jaakola, 2013).  

There are a few mechanisms of anthocyanin increase in fruit in response to drought stress. 

The first one involves a direct increase in transcription of anthocyanin biosynthesis genes such as 

phenyl ammonia lyase (PAL), chalcone synthase (CHS), dihydroflavanol reductase (DFR), and 

UDP-glucose flavonoid 3-O-glucosyl transferase (UFGT) in fruit tissue in response to drought. 



 

17 

The second mechanism involved stimulation through intermediary compounds such as abscisic 

acid (ABA), proline and sugars. During drought, there is an accumulation of ABA, proline and 

sugars, which play key roles in the induction of anthocyanin biosynthesis in fruit through the 

natural progression of the phenylpropanoid pathway (González-Villagra et al., 2018; Sperdouli 

and Moustakas, 2012). Sugars are the precursors for many phenylpropanoid pathway products 

such as phenylalanine and chalcones. 

A third mechanism involving a microRNA 156, 14-3-3 lambda protein and UDP-

glycosyltransferases was recently identified to be a modulator of anthocyanin biosynthesis in fruit 

(Li et al., 2017; Peethambaran et al., 2012). An et al. (2020) identified two mechanisms by which 

the central regulator MdMYB1 is activated during drought. MdERF3 and 38 (ethylene response 

factors) were seen to enhance the transcriptional activity of MdMYB1 in apple fruit in response to 

drought stress (An et al., 2018b; An et al., 2020). MdMYB121 was found to be a modulator of 

phenylpropanoid metabolism in fruit through activation by multiple abiotic stressors and would be 

an ideal candidate gene for enhancing apple stress tolerance through marker assisted selection (Cao 

et al., 2013). 

Mechanisms of repression of anthocyanin synthesis during drought stress have also been 

found. MdBT2 inhibits anthocyanin biosynthesis in fruit by stimulating degradation of MdMYB1 

and MdMYB9 (An et al., 2018a; Wang et al., 2018b) (Figure 1.3). One of the mechanisms by which 

MdBT2 negatively regulates MdMYB1 is through controlled degradation of the MdERF38 protein 

(An et al., 2020) (Figure 1.4). MdBT2 is also negative regulator of ABA by enhancing the 

degradation of MdbZIP44 and MdWRKY40 (An et al., 2018c, 2019b), thus indirectly inhibiting the 

biosynthesis of anthocyanin in apple fruit. 

 



 

18 

 

 

 

 

 

 

 

 

Figure 1.3 MdBT2’s negative regulation of anthocyanin biosynthesis in response to drought and 

wounding. Figure obtained from (An et al., 2020). 

 

The flavonoid metabolism is also highly influenced by drought conditions (Martinez et al., 

2016; Nakabayashi et al., 2014a, 2014b). While studies on transcription factors related to flavonol 

metabolism in apples have been limited, there is a whole host of research studies which provide 

evidence in other crops regarding the importance of flavonoids for plant protection during drought 

and other abiotic stresses. It has been well documented that drought results in the build-up of free 

radicals due to improper function of various metabolic processes such as transpiration, cell 

permeability issues, irregular cellular processes etc. (Anjum et al., 2011). Flavonols are also an 

excellent source of antioxidants and are one of the most efficient radical scavengers in the plant. 

Under drought conditions, Martinez et al. (2016) determined that there is a preferential 

accumulation of flavonols over hydroxycinnamic acids in order to enhance the radical scavenging 

capacity of tomato fruit. Nakabayashi et al. (2014b) investigated the MYB12 gene in Arabidopsis 

thaliana leaves and found evidence of overaccumulation of flavonoids and anthocyanins under 



 

19 

drought stress conditions. The activity of flavanol synthase (FLS) which produces the basic 

flavonols is enhanced in comparison to the activity of dihydroflavonol reductase (DFR) as they 

compete for the same substrate dihydroflavonol (Martinez et al., 2016). This system enhances the 

radical scavenging capacity of the plant and equips it to alleviate some of the effects of drought 

stress and enhance plant protection from free radicals. 

Light  

While photosynthetically active radiation is essential for plant growth and development, 

the ultraviolet (UV) spectrum causes detrimental effects on plant growth. Solar UV light is split 

into two: UV-A light is between 320-400 nm whereas UV-B light is between 290-320 nm. Even 

though most of the UV light is absorbed by the ozone layer, the remaining 5% of total light is 

comprised of UV and it can still disrupt apple growth and development (Wu et al., 2009). Many 

research studies in the early 2000’s have documented the accumulation of flavonols and 

anthocyanins in apple fruit with exposure to UV-B light (Lancaster et al., 2000; Reay and 

Lancaster, 2001; Solovchenko and Schmitz‐Eiberger, 2003) (Figure 1.4). However, the molecular 

mechanisms behind the regulation of phenylpropanoid content by UV-B light was only elucidated 

in the past few years with the advent of the high-quality draft genome of Malus хdomestica 

published by Daccord et al. (2017) and are illustrated in Figure 1.4.  

UV light activates the UVR8 gene (UV Resistance Locus 8) which inactivates the repressor 

COP1 and COP4 TF’s (Constitutive Photomorphogenesis 1) thus enhancing the activity of 

MdHY5 (Elongated Hypocotyl 5) and MYB TF’s such as MdMYB22 and MdMYB10 which are 

involved in the induction of flavonol synthase (FLS) and anthocyanin biosynthesis respectively in 

fruit (Li et al., 2012; Fang et al., 2019a). MdHY5 also induced B-box zinc finger proteins 



 

20 

MdCOL11, MdBBX20, and MdBBX22 to enhance flavonol and anthocyanidin production in fruit 

(Bai et al., 2014; Fang et al., 2019b; An et al., 2019a).  

Hu et al. (2020) illustrated a direct and indirect mechanism of UV-B mediation regulation 

of flavonol and anthocyanin content in fruit. WRKY, a zing finger TF usually combines with a 

core TGAC sequence (W-box elements) in the promoter regions of gene to enhance their synthesis 

(Eulgem et al., 2000). In apple, MdWRKY72 was found to combine with W-box element in MdHY5, 

a bZIP TF, which in turn combines with the cis-acting G-box element in MdMYB1, thus promoting 

its production. MdWRKY72 was also shown to directly bind with the W-box protein of MdMYB1, 

thus enhancing the synthesis of flavonols and anthocyanins in apple fruit (Hu et al., 2020).  



 

21 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



 

22 

Figure 1.4 Mechanisms of UV-B mediated regulation of the phenylpropanoid metabolism in 

Malus x domestica. In this figure, the arrows represent positive regulation of the subsequent 

TF/other genes, unless specified otherwise. The ‘+’ symbols refers to an interaction where 

suppression or inhibition of one gene/TF by the other gene/TF is observed. Each individual color 

represents a separate mechanism of action of activation or suppression of the phenylpropanoid 

pathway by UV light except for black, which represents genes/TF’s in multiple mechanisms of 

action. Constitutive Photomorphogenesis 1 (COP1), Elongated Hycotoyl 5 (HY5), B-Box TF 22 

(BBX22), Nitrate responsive protein 2 (BT2), basic helix loop helix 3 (bHLH3), B-Box TF 4 

(COL4), B-Box TF 11 (COL11), Zinc finger protein 72 (WRKY72), Flavonol synthase (FLS), 

Anthocyanidin reductase (ANR), UDP-glucose flavonoid 3-O-glucosyl transferase (UFGT). 

Figure based on research findings of the following articles (An et al., 2019a, 2017b; Bai et al., 

2014; Fang et al., 2019a, 2019b; Henry-Kirk et al., 2018; Hu et al., 2020; Li et al., 2012) 

 

Temperature 

Heat and cold stress can affect apples in various ways by damaging the structure of cells 

and nutrient metabolism changes. Coupled with other factors such as drought and salinity, extreme 

temperatures can be a death blow to apple trees (Thomashow, 1999). Cold stress causes damage 

to fruit buds, plant roots, leaves, flowers, and fruits. With the advent of climate change, cold stress 

is particularly a problem during frost events in spring where the blooming buds are killed off before 

flowering and fruiting, thus causing huge damages to production. Plants have evolved a myriad of 

regulatory mechanisms to tolerate cold stress responses (Zhu, 2016, 2001). Anthocyanins and 

flavonoids have been shown to be upregulated during cold stress to manage the excessive free 

radicals through their antioxidant capacities (Zhu, 2016). There are a few mechanisms of action of 

manipulation of the phenylpropanoid pathway detailed below. 

The C-repeat binding factor (CBF) TF’s respond to the cold or freezing temperatures by 

binding to the promoters of the cold-regulated genes (COR) through the C-repeat/dehydration-

responsive elements (CRT/DRE) and increase the production of phenylproapoids (Gilmour et al., 

2004; Stockinger et al., 1997). Five CBF TF’s have been identified in apple (An et al., 2018b; 



 

23 

Wisniewski et al., 2014) – MdCBF1, MdCBF2, MdCBF3, MdCBF4, MdCBF5. The CBF TF’s are 

transcriptionally regulated by the basic helix loop helix TF’s MdClbHLH1 (homolog of ICE1 in 

A. thaliana) and similar bHLH TF’s (Feng et al., 2012). Basides bHLH TF’s, CBF genes are also 

regulated by MYB TF’s. The MYB genes MdMYB308L, MdoMYB121, MdSIMYB1, MdMYB4, 

MdMYB23, MdMYB88 and MdMYB124 have been identified to be positive regulators of cold 

tolerance (An et al., 2020; Cao et al., 2013; Wang et al., 2014; Wu et al., 2017; Xie et al., 2018), 

whereas MdMYB44 and MdMYB15L were found to negatively affect plant cold tolerance (Wu et 

al., 2018; Xu et al., 2018a, 2018b). Many MYB genes act in conjunction with bHLH TF’s to 

promote or repress the CBF and DFR genes that are involved in regulating the phenylpropanoid 

pathway. 

RNA Sequencing as a technique to understand molecular controls of the phenylpropanoid 

pathway 

 In order to understand the molecular controls of the polyphenol pathway in response to 

factors such as carbohydrate availability, it becomes essential to look at gene expression at various 

stages of fruit growth to get a comprehensive overview of the molecular network of genes and 

transcription factors underpinning polyphenol synthesis. RNA sequencing (RNA-Seq for short) 

has emerged as a powerful tool in elucidating the genetic basis of different pathways (Wang et al. 

2009). This technology allows for the comprehensive profiling of the entire transcriptome, 

providing a high-resolution view of gene expression patterns. By using RNA-Seq, researchers can 

identify differentially expressed genes and transcription factors that play key roles in the synthesis 

of polyphenols. Moreover, RNA-Seq offers several advantages over traditional methods, including 

its ability to detect both known and novel transcripts, its quantitative and sensitive nature, and its 

capacity to capture alternative splicing events and non-coding RNA species (Wang et al. 2009). 



 

24 

These features make RNA-Seq a valuable approach in unraveling the complex molecular 

mechanisms governing the biosynthesis of polyphenols in response to changes in carbohydrate 

availability. 

Research objectives - developing a new genetic based acidity classification system and 

enhancing understanding of polyphenol synthesis in response to carbohydrate availability. 

The work described within this dissertation focused on providing an in-depth and novel 

understanding of how organic acids and polyphenols develop in cider apples. The research 

objectives discussed in each chapter include: 

Chapter 2. To develop a genetic system for classifying M. ×domestica cider apple acidity using the 

Ma1 and Ma3 markers. Given the major effect of Ma1 and Ma3 on fruit acidity levels below 10 

g·L-1, I hypothesized that both markers could be used to predict acidity in apples and thus allow 

for classification into acidity ranges that would aid cider apple breeding, cultivar selection, and 

cider production.  

Chapter 3. To understand the physiological and molecular underpinnings of the polyphenol 

development in cider apples in relation to crop density. To optimize management practices to 

achieve sustained enhancement of polyphenol concentrations in cider apples. Also, the experiment 

seeks to understand the development of the phenylpropanoid pathway products throughout the 

growing season and characterize the diverse accumulation patterns of different polyphenol 

compounds. I hypothesize that reduced crop density will enhance polyphenol production in cider 

apples and genes responsible for promoting polyphenol production will be upregulated under low 

crop density conditions. 



 

25 

Chapter 4. To understand the effect of carbohydrate availability on polyphenol production in cider 

apple. I hypothesize that limiting carbohydrate availability during the first five weeks of fruit 

development (cell division phase) through sunlight limiting tree shading will result in a decrease 

in total and individual polyphenols at harvest. I also seek to understand the difference between tree 

shading and fruit shading in aiding polyphenol development in cider apples. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



 

26 

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Chapter 2 

Classifying cider apple germplasm using genetic markers for fruit acidity 

 

This chapter is a reformatted version of the following publication. 

Kumar, S.K., Wojtyna, N., Dougherty, L., Xu, K. and Peck, G., 2021. Classifying cider apple 

germplasm using genetic markers for fruit acidity. Journal of the American Society for 

Horticultural Science, 146(4): 267-275. 

 

 

Abstract 

The organic acid concentration in apple (Malus ×domestica) juice is a major component 

of hard cider flavor. The goal of this study was to determine if the malic acid markers, Ma1 and 

Q8, could classify the titratable acidity concentration in cider apple accessions from the United 

States Department of Agriculture’s (USDA) Malus germplasm collection into descriptive 

classifications. Our results indicate that for diploid genotypes, the Ma1 marker alone and the Ma1 

and Q8 markers analyzed together, could be used to predict cider apple acidity (P < 0.0001). Alone, 

the Ma1 marker categorized acidity into low (<2.4 gL-1), medium (2.4-5.8 gL-1), and high (>5.8 

gL-1) groups. The combination of Ma1 and Q8 markers provided more specificity, which would 

be useful for plant breeding applications. This work also identified a significant difference (P = 

0.0132) in acidity associated with ploidy. On average, the triploids accessions had 0.33 gL-1 

greater titratable acidity than the diploid accessions. From this work, we propose a genetics-based 

classification system for cider apples, with the acidity component defined by the Ma1 and Q8 

markers. 

 



 

38 

Introduction 

Although fresh-market apples comprise the vast majority of global apple (Malus 

×domestica) production, cider apples have been of particular interest recently because their greater 

acidity and tannin content (a group of polyphenols with bitterness and astringency), can potentially 

produce a hard cider with a depth of flavor similar to wine. Cider-specific apples can have 5 to 10 

times more tannins when compared to fresh-market apples and a wide range of organic acid 

concentrations (perceived as sourness and often described as sharpness) (Thompson-Witrick et al., 

2014). In the U.S. there is currently more demand than supply for the cider-specific apple cultivars 

(Pashow, 2018). In response to this supply chain imbalance, cider-specific cultivars, mostly of 

European origin, are being planted throughout the country (Miles et al., 2020). However, there is 

limited information on the juice quality characteristics of these apple cultivars. 

The emerging U.S. cider industry has adopted a method for classifying cider apples that 

was originally developed at the Long Ashton Research Station (LARS) near Bristol, UK over a 

century ago (Barker and Ettle, 1910). The LARS system classifies apple cultivars into one of four 

categories: sweet, bittersweet, sharp, or bittersharp based on tannin and titratable acidity (TA) 

concentration. Tannins were originally measured using the Löwenthal permanganate titration 

method (Snyder, 1893). A tannic acid concentration of 2 gL-1 is used to separate non-bitter from 

bitter apples. Acidity was, and still is, measured using an acid-base titration at a pH end-point of 

8.1. Apples with a malic acid equivalent concentration below 4.5 gL-1 are classified as sweet and 

those greater than 4.5 gL-1 are classified as sharp. Although plant genetics and sensory science 

have progressed greatly since the early 20th century, the LARS system has remained unchanged. 

A French classification system divides cider apples into six categories, as follows (English 

translation in parenthesis): amère (bitter), douce amère (bittersweet), douce (sweet), acidulée 



 

39 

(subacid), aigre (sharp), and aigre amère (bittersharp) (Institut Français Des Productions 

Cidricoles, 2009). The acidity component of the French cider apple-classification method has three 

categories: douce, acidulée, and aigre. The douce category is defined as having less than 4.5 gL-1 

TA, the same threshold used by the LARS classification system. The acidulée category is defined 

as TA values between 4.5 and 6.75 gL-1 TA, while the aigre category includes apples with greater 

than 6.75 gL-1 TA. 

A Spanish cider classification system has undergone more recent changes than the LARS 

classification system and now contains six technical groups: sweet (<1.45 gL-1 tannic acid; <4.85 

gL-1 TA), bittersweet (>1.45 g L-1 tannic acid; <4.85 gL-1 TA), semi-acid (<1.45 gL-1 tannic 

acid; 4.85-6.56 gL-1 TA), semi-acid-bitter (>1.45 gL-1 tannic acid; 4.85-6.56 gL-1 TA), acid 

(<1.45 gL-1 tannic acid; 6.56 gL-1 TA), and acid-bitter (>1.45 gL-1 tannic acid; >6.56 gL-1 TA) 

(Ministerio De Agricultura, Pesca y Alimentacion, 2003). 

The major organic acid in mature apple fruit is malic (~90% to 95%, 3 to 5 gL-1), followed 

by quinic (~4%, 0.2 to 0.5 mgL-1), citric (~1.5%, 0.05 to 0.07 mgL-1), and trace amounts (< 0.05 

mgL-1) of ascorbic, shikimic, succinic formic, maleic, and tartaric acid (Wu et al., 2007; Zhang et 

al., 2010). The genetic underpinnings of apple acidity were first described in 1959 and subsequent 

studies have led to the identification and characterization of malic acid (Ma) locus on linkage 

group 16 (Maliepaard et al., 1998; Nybom, 1959; Visser and Verhaegh, 1978; Yao et al., 2008; Xu 

et al., 2012). This locus has been reported to control 17% to 42.3% of the variation in acid 

concentration in apple fruit (Xu et al., 2012). The gene underlying Ma, named Ma1, has since been 

identified to encode an aluminum-activated malate transporter-like protein (Bai et al., 2012, Khan 

et al., 2013). A single nucleotide mutation from the guanine (G) to adenine (A) at position 1455 in 

the coding sequence of Ma1 results in a premature stop codon that truncates 84 amino acids at the 



 

40 

C-terminus, causing low acidity (Bai et al., 2012; Li et al., 2020). Therefore, the Ma1 allele, with 

“G” at position 1455 is associated with high acid (Ma), and the Ma1 allele with “A” at position 

1455 is associated with low acid (ma). This distinction defines the difference between the dominant 

Ma and recessive ma alleles. However, the dominance of the Ma1 allele is incomplete which was 

indicated by the wide range of TA values for heterozygous Mama allele, suggesting that both 

additive and dominant effects of the Ma1 allele exist (Verma et al., 2019; Xu et al., 2012). 

Recently, it was found that in response to excess nitrate accumulation, the MdBT2 protein 

modulated and downregulated the expression of MdCIbHLH1 and MdMYB73, which regulate 

malate related genes, thus reducing acidity in apples (Zhang et al., 2020). 

 Linkage group 8 also contains an important quantitative trait locus (QTL) that regulates 

apple acidity, named Ma3 (Kumar et al., 2013; Liebhard et al., 2003; Ma et al., 2015; Sun et al., 

2015; Verma et al., 2019). The Ma3 locus has recently been shown to have an incomplete 

dominance effect on apple acidity (Rymenants et al., 2020). Jia et al. (2018) identified two natural 

variations in hierarchical epistatic genes MdSAUR37 and MdPP2CH that affect fruit acidity in the 

Ma3 region. To genotype the Ma3 locus, a sequence tagged site (STS) marker, named Q8 was 

developed, which is physically located at 11.1 Mb between genes MdPP2CH (8.7 Mb) and 

MdSAUR37 (11.6 Mb) on chromosome 8 in the GDDH13 apple reference genome (Daccord et al., 

2017; Jia et al., 2018). Additionally, three more QTLs Ma4, Ma5, and Ma6 located on 

chromosomes 6, 1, and 4, respectively, were found to be relevant for fruit acidity levels (Ban and 

Xu, 2020; Rymenants et al., 2020). These QTLs appeared to explain more variation in the 

background of MaMa and Mama with relatively high acidity levels (>10 g·L-1) (Ban and Xu, 

2020). 



 

41 

Given the major effect of Ma1 and Ma3 loci on fruit acidity levels below 10 g·L-1, we 

hypothesized that they could be used to predict acidity in apples and thus allow for classification 

into acidity ranges that would aid cider apple breeding, cultivar selection, and cider production. 

The goal of this study was to develop a genetic system for classifying M. ×domestica cider apple 

acidity using the Ma1 and Q8 markers.  

Materials and Methods 

Study Location and Accession Selection 

Apples were harvested in 2017, 2018, and/or 2019 from the Malus germplasm collection 

maintained by the United States Department of Agriculture’s (USDA) National Plant Germplasm 

System in Geneva, NY (lat. 4253’40.3” N, long. 7700’23.8” W). We compiled a list of 330 M. 

×domestica cultivars within the Malus germplasm collection that are mentioned in historic 

European and American texts and/or are currently being utilized for hard cider (Supplementary 

Figure 2.1). Additional M. ×domestica accessions were identified by searching the USDA 

Germplasm Resources Information Network (GRIN) Global database (Genetic Resource 

Information Network, 2020) for accessions with astringent, aromatic, and/or acidic fruit that were 

greater than 50 g at harvest. Trees that appeared unhealthy or had insufficient fruit for analyses 

were excluded. Ploidy data was obtained from a 20K single-nucleotide polymorphism (SNP) array 

as part of an ongoing collaborative apple pedigree reconstruction project (Denancé et al., 2020; 

Howard et al., 2018; Muranty et al., 2020). Accessions without associated ploidy data were 

removed from the dataset. In total, these steps led to the selection of 217 M. ×domestica accessions 

(Supplementary Table 2.1). 

 

 



 

42 

DNA Extraction and Accession Genotyping 

Young leaves of the selected accessions were collected between 2017-19. Leaf tissue (15-

20 mg) was ground for 1 min using a tissue lyser (TissueLyserII; Qiagen, Venio, The Netherlands). 

Samples were incubated for 1 h in a hexadecyltrimethlammonium bromide (CTAB) extraction 

buffer containing polyvinylpyrrolidone (Catalogue number: BP431-500, Thermo Fisher Scientific, 

Waltham, MA) and -mercaptoethanol (Catalogue number: BP176-100, Thermo Fisher Scientific) 

(Doyle and Doyle, 1987). A Nanodrop 1000 (Thermo Fischer Scientific) was used for DNA 

quantification. 

A cleaved amplified polymorphic sequence marker (CAPS1455) targeting base 1455 in the 

open reading frame of the Ma1 gene was used to distinguish the SNP between the Ma1 alleles, 

as previously described (Bai et al., 2012). Briefly, PCR amplified products were digested 

overnight with BspHI (New England Bio Labs, Ipswich, MA) in a 37 °C water bath overnight. 

Digested products were visualized on a 1.5% agarose gel and Ma1 genotypes were determined 

based on band patterning. The polymerase chain reaction (PCR) program included 2 min at 

98 °C; 35 cycles of 10 s at 98 °C, 15 s at 55 °C, and 90 s at 72 °C; and then a final 5 min at 

72 °C. The reactions were conducted in 20 μL volumes, containing 1× PrimeSTAR® MAX DNA 

Polymerase (R045A, Takara/Clontech, Mountain View, CA), 0.5 mM of each primer, and 30 ng 

of genomic DNA in an Eppendorf Mastercycler EP Gradient Thermal Cycler (Eppendorf, 

Hamburg, Germany). Restriction digestion was performed for 12 h at 37 °C in 20 μL reactions 

that contained 10 μL PCR products, 2 U of BspHI restriction enzyme (New England Biolabs, 

Ipswich, MA), and 1× NEBuffer 4 (New England Biolabs). After sample incubation, 7 L of 

sample and 3 L of loading dye was injected into each well of a 1.5% (w/v) agarose gel. The 

samples were suspended in a 1 N Tris/Acetate/EDTA (TAE) buffer solution. After 1 h of 



 

43 

electrophoresis, the gels were stained with ethidium bromide at a concentration of 2 L to 100 mg 

of gel. The banding patterns in the gel were then illuminated with a 110-V ultra-violet light 

transilluminator (Thermo Scientific, Waltham, MA). The banding patterns in the gel images were 

visually scored and the Ma1 alleles for each accession were recorded. 

The Q8 marker primer sequences were 5’-AAAAATTGAAACTTGTGGATCGTT-3’ 

(forward primer) and 5’-AAATCAAAAGCATACCACCACA-3’ (reverse primer), respectively. 

The marker was PCR amplified using 1× OneTaq DNA Polymerase (New England BioLabs) 

with the following conditions: 2 min at 98 °C; 35 cycles of 30 s at 94 °C, 30 s at 54 °C, and 45 s 

at 68 °C. The PCR products were visualized on 1.5% agarose gel and scored for Q8 genotypes. 

Fruit Sampling and Processing Procedures 

Of the 217 M. domestica accessions, 32 accessions were collected for 3 years, 114 

accessions were collected for 2 years, and 71 accessions were collected for 1 year (Supplementary 

Table 2.1). The biennial bearing habit of many of these accessions made fruit unavailable in every 

year. Fruit was sampled from mid-August to mid-November. Prior to harvest, two fruit from each 

accession that were near the reported harvest date in GRIN-Global were field tested in situ for 

maturity using the cortex starch pattern index (SPI) (Blanpied and Silsby, 1992). An iodine 

solution (0.22 g·L−1 iodine, 0.88 g·L−1 potassium iodine) (EMD Millipore Corporation, Billerica, 

MA) was applied to the stem-side of an equatorial cross-section of the apple. A visual rating of the 

cortex flesh (hypanthium and mesocarp) stained was conducted and recorded on a 1-8 scale; where 

1=100% staining (no starch degradation) and 8=0% staining (complete starch degradation). As 

much as possible, fruit was harvested when they were rated to have a SPI of six or greater. 

Fifteen fruit were randomly harvested from different regions of the tree canopy avoiding 

selecting two or more fruit from the same branch. The unique identifying Plant Introduction (P.I.) 



 

44 

number given to each accession in the USDA-PGRU collection was recorded and used to track the 

fruit throughout the phenotyping process. After harvest, the 15 fruit were randomly divided into 

three groups of five apples to allow for three subsamples of five fruit per sampled accession, as 

per Evans et al. (2012). The fruit were stored at 4 C in a commercial storage room under ambient 

atmospheric gases for 1 to 4 weeks prior to fruit processing analysis at the Cornell University 

Agricultural Experiment Station Research Orchards in Ithaca, NY. 

Starch pattern index was determined on all sample apples, as described above. The calyx 

half of each five-apple pooled subsample was milled and pressed in a Norwalk 280 juicer 

(Bentonville, AR). Juice from each subsample replication was stirred and aliquoted into 50 mL 

tubes. All juice extracting equipment was rinsed with water between samples to minimize cross-

contamination. Juice samples were stored at -80 C until titrations were performed. 

Juice Titratable Acidity and pH 

Samples were thawed to room temperature and then vortexed for 10 s. Juice TA and pH 

were measured with an automatic titrator (Unitrode pH meter, 778 sample processor, and 800 

Dosino dosing device; Metrohm, Herisau, Switzerland). Juice acidity was measured by titrating a 

5 mL juice aliquot against a standardized 0.1 N NaOH solution to an endpoint of pH 8.1 and 

expressed in grams per liter of malic acid equivalents. 

Statistical Analysis 

Linear models were developed using Ma1, Q8, or both markers as predictor variables and 

TA as the response variable with RStudio version 1.1.442 (RStudio, Boston, MA). The linear 

models were used to predict 95% confidence intervals with the Estimated Marginal Means data 

analysis package. The data were not transformed prior to analysis. A probability value of less than 

or equal to 0.05 was considered statistically significant. 



 

45 

Results 

Fruit Maturity, Titratable Acidity, and pH 

The mean SPI for the 217 evaluated accessions ranged from 5 to 8 with a mean of 6.96 ± 

0.06 SE (Supplementary Table 2.1). More than 85% of the accessions had an average SPI greater 

than 6. Titratable acidity ranged from 1.09 to 11.53 g·L-1 with a mean of 4.48±0.16 g·L-1 (Figure 

2.1A). pH ranged from 2.3 to 5.1 with a mean of 3.84±0.03 (Figure 2.1B). The sample population 

had 113 (52%) accessions with a TA less than the mean and 104 (48%) accessions greater than the 

mean. There were two peaks in the TA distribution, one in the lower range representing most of 

the very low acidity accessions with a peak at 1.5 g·L-1, and the other between TA 4.6 and 6.6 g·L-

1, suggesting a bimodal distribution for fruit TA within our sample population. 

 

 

 

 

 

 

 

 

 

 

 

 



 

46 

 

Figure 2.1 A, B The distribution of (A) titratable acidity and (B) pH of cider apple accessions (N 

= 217) harvested between 2017-19 from the U.S. Department of Agriculture Malus germplasm 

collection in Geneva, NY. 

 

Allelic Demographics 

Out of the 217 genotyped accessions, 181 (83%) were diploid accessions and 36 (17%) 

were triploid (Supplementary Table 2.1). For the Ma1 marker, 17 (9%) of the diploid accessions 

were homozygous dominant (MaMa), 107 (59%) were heterozygous (Mama), and 57 (32%) were 

homozygous recessive (mama) (Table 2.1). For the Q8 marker, which is a likely marker for the 

Ma3 gene, 129 (71%) of the diploid accessions were homozygous dominant (Q8Q8), 48 (27%) 

were heterozygous (Q8q8), and 4 (2%) were homozygous recessive (q8q8). The most common 

genotype combinations were Mama-Q8Q8 (66 accessions, 37%), mama-Q8Q8 (49 accessions, 

27%), and Mama-Q8q8 (37 accessions, 20%) (Table 2.1). The other allelic combinations each 

represented less than 10% of the total sample population, including three MaMa-Q8q8 accessions, 



 

47 

four Mama-q8q8 accessions, and eight mama-Q8q8 accessions. There were no MaMa-q8q8 or 

mama-q8q8 combinations among the accessions. 

Allele diversity was less robust for the triploid accessions. The mamama_Q8Q8Q8 

combination comprised 39% of our triploid samples, Mama-_Q8Q8Q8 comprised 28%, and 

Mama-_Q8q8- comprised 33% (Table 2.1). This was in part due to a smaller sample size (N = 36) 

and the inability to identify the third allele in triploid heterozygous accessions. 

 

Table 2.1 The number of cider apple accessions for each allelic combination of the Ma1 and Q8 

markers. Leaves from the sample population (N = 217) were collected from the U.S. Department 

of Agriculture Malus germplasm collection in Geneva, NY. 

 Diploid accessions  Triploid accessions 

Allele MaMa 

(no.) 

Mama 

(no.) 

Mama 

(no.) 

 Allele (no.) 

Q8Q8 14 66 49  mamama_Q8Q8Q8 14 

Q8q8 3 37 8  Mama-_Q8Q8Q8z 10 

q8q8 0 4 0  Mama-_Q8q8- 12 

z “-” Represents a missing allele in the heterozygous triploid accessions. 

 

Relationships among the Genotypes and Phenotypes 

Ploidy significantly influenced TA concentration (P = 0.0132), with diploid accessions 

having 0.33 g·L-1 less TA than triploids (4.43 and 4.76 g·L-1, respectively) (Table 2.2). pH was 

not significantly affected by ploidy level. The interaction between Ma1 and Q8 versus ploidy was 

not significant, indicating that ploidy is independent of the Ma1 and Q8 allele composition. Due 

to ploidy being a significant factor for TA, diploid and triploid accessions were analyzed separately 

for each Ma1 and Q8 allelic combination using an estimated marginal means analyses and 

confidence intervals (Tables 2.3 and 2.4). 



 

48 

For the diploids, the Ma1 marker had a significant effect on TA (P < 0.0001) and pH (P < 

0.0001), but the Q8 marker did not show a significant effect for either TA or pH (Table 2.2). 

However, a detailed analysis in each Ma genotype group indicated that there was a significant 

difference between Q8Q8 and q8q8 within the Mama genotype, where all the q8q8 accessions 

were found. This suggests that the insignificant effect of Q8 on acidity in the 181 diploid 

accessions may have been related to the limited number of accessions (N = 4) that possessed the 

recessive q8q8 in our sample population (Tables 2.1 and 2.3, Figure 2.2A). The analysis also 

revealed that for Mama and MaMa, the Q8Q8 genotype exhibited greater TA than Q8q8, although 

that difference was not significant (Figure 2.2A, Table 2.3). In the mama group, the difference in 

acidity between the Q8Q8 and Q8q8 genotypes were negligible (Figure 2.2A, Table 2.3). 

Consequently, when the Ma1 and Q8 markers were considered in combination, both TA (P < 

0.0001) and pH (P < 0.0001) were correlated with the genotype (Table 2.2). 

 

Table 2.2 Fixed effects from a simple linear model of the Ma1 and Q8 markers and ploidy in a 

study of cider apple accessions (N = 217) harvested between 2017-19 from the U.S. Department 

of Agriculture Malus germplasm collection in Geneva, NY. 

Fixed Effects Titratable acidity 

(P value) 

pH 

(P value) 

Ma1                                             <0.0001 <0.0001 

Q8 0.5032  0.2361 

Ma1+Q8 0.0001 <0.0003 

Ploidy 0.0132 0.1375 

Ma1+Q8 × Ploidy 0.2275 0.6368 



 

49 

Table 2.3 The estimated marginal means, and the lower (LCI) and upper (UCI) 95% confidence intervals for juice titratable acidity and 

pH for the Ma1 alleles alone and Ma1 + Q8 allelic combinations among diploid (N = 181) cider apple accessions harvested between 

2017-19 from the U.S. Department of Agriculture Malus germplasm collection in Geneva, NY. Some of the Ma1 and Q8 allele 

combinations were omitted due to non-availability of accessions with those genotypes. 

Allelic 

combinations 

Titratable Acidity 

[mean ± SE (gL-1)] 

LCI 

(gL-1) 

UCI 

(gL-1) 

pH 

(mean ± SE) 

LCI UCI Accessions 

(no.) 

Ma1 allele alone        

Mama 1.92 ± 0.226 cz 1.47 2.38 4.41 ± 0.038 a 4.34 4.49 57 

Mama 5.46 ± 0.165 b 5.13 5.78 3.59 ± 0.027 b 3.53 3.64 107 

MaMa 6.42 ± 0.415 a 5.60 7.24 3.53 ± 0.069 c 3.39 3.67 17 

Ma1 + Q8 allele        

mama_Q8q8 1.64 ± 0.571 d 0.51 2.77 4.53 ± 0.102 a 4.32 4.73 8 

mama_Q8Q8 1.96 ± 0.231 d 1.51 2.42 4.39 ± 0.041 a 4.31 4.48 49 

Mama_q8q8  3.21 ± 0.808 cd 1.62 4.81 3.90 ± 0.145 b 3.61 4.19 4 

Mama_Q8q8  4.93 ± 0.262 bc 4.42 5.45 3.64 ± 0.047 b 3.54 3.73 37 

MaMa_Q8q8   5.24 ± 0.933 abc 3.4 7.08 3.60 ± 0.168 b 3.27 3.93 3 

Mama_Q8Q8  5.89 ± 0.199 ab 5.5 6.28 3.54 ± 0.035 b 3.47 3.61 66 

MaMa_Q8Q8 6.67 ± 0.432 a 5.82 7.52 3.51 ± 0.077 b 3.36 3.67 14 

z Means within columns followed by the same lower-case letter are not significantly different based on Tukey’s means comparison at 

α= 0.05 and were analyzed separately for Ma1 alone and Ma1 + Q8 allele combinations. 

 



 

50 

 

Table 2.4 The estimated marginal means, and the lower (LCI) and upper 95% confidence intervals (UCI) for juice titratable acidity and 

pH within each of the Ma1 allelic combinations among triploid cider apple accessions (N = 36) harvested between 2017-19 from the 

U.S. Department of Agriculture Malus germplasm collection in Geneva, NY. Some of the allelic combinations were omitted due to very 

low or no availability of accessions with those genotypes. The third allele in heterozygous triploid accessions was not identified, thus “-

” represents an unknown allele. 

Allelic 

combinations 

Titratable acidity  

[mean ± SE (gL-1)] 

LCI 

 (gL-1) 

UCI 

 (gL-1) 

pH  

(mean ± SE) 

LCI UCI Accessions 

(no.) 

Ma1 allele alone        

Mamama 2.13 ± 0.508 bz 1.10 3.17 4.34 ± 0.066 a 4.20 4.47 14 

Mama- 6.51 ± 0.415 a 5.66 7.35 3.50 ± 0.054 b 3.39 3.62 22 

Ma1 + Q8 alleles        

mamama_Q8Q8Q8 2.13 ± 0.480 b 1.16 3.11 4.34 ± 0.062 a 4.21 4.46 14 

Mama-_Q8q8- 5.76 ± 0.549 a 4.64 6.88 3.61 ± 0.071 b 3.46 3.75 12 

Mama-_Q8Q8Q8 7.33 ± 0.568 a 6.17 8.48 3.39 ± 0.073 b 3.24 3.54 10 

z Means within columns followed by the same lower-case letter are not significantly different based on Tukey’s means comparison at 

α= 0.05 and were analyzed separately for Ma1 alone and Ma1 + Q8 allele combinations. 

 

 

 



 

51 

 

 

 
 

Figure 2.2 A, B The variation in titratable acidity for the Ma1 and Q8 alleles among A) diploid accessions (N = 181) and B) triploid 

accessions (N = 36) between 2017-19 from the U.S. Department of Agriculture Malus germplasm collection in Geneva, NY. The blue 

dots represent the estimated marginal means for each accession. The red plus signs represent outliers, as defined by the estimated 

marginal means test. 

 



 

52 

Classifying Cider Apples by Phenotype, Genotype, and Region of Origin 

The 217 cider apple accessions genotyped in this study were classified under the different 

existing regional classification systems based on acidity (Figure 2.3). Using the 4.5 g·L-1 threshold 

from the LARS classification system, there was approximately an equal distribution of sweet 

(52%) and sharp cider apples (48%). The French and Spanish classification systems showed a very 

similar spread of cider apple accessions with 52% and 57% falling into the sweet category, 30% 

and 24% falling into the semi-acid category, and 18% and 19% falling into the acidic category, 

respectively (Figure 2.3). 

The 95% confidence interval range (lower to upper confidence interval) from the estimated 

marginal means test for each Ma1 allele alone and Ma1 and Q8 allele combinations, were used to 

developed classification thresholds for the diploid and triploid accessions separately (Table 2.3, 

Figure 2.3). The Ma1 marker-based classification system discreetly categorized the cider apple 

accessions with minimal overlap among the genotypes (Figure 2.3). For the diploid accessions, the 

Ma1 marker could be classified into low [mama (<2.4 g·L-1)], medium [Mama (2.4 to 5.8 g·L-1)], 

and high [MaMa (>5.8 g·L-1)] acidity levels. There were only two accessions that overlapped 

between the upper threshold for diploid Mama (5.78 g·L-1) and the lower threshold for MaMa 

(5.60 g·L-1). The diploid Ma1 and Q8 allelic combinations categorized cider apples with multiple 

category overlaps; however, it provided more specificity. There was a significant difference 

between the Mama_q8q8 (1.62 to 4.81 g·L-1) and Mama_Q8Q8 (5.5 to 6.8 g·L-1) alleles. 

 

The triploid accessions were classified into two categories solely using the Ma1 allele 

(mamama and Mama-). Homozygous dominant triploid Ma1 alleles were absent in the sample 

population. Using both the Ma1 and Q8 alleles for the triploid accessions, there were three 



 

53 

identified categories of Ma1 and Q8 alleles (mamama_Q8Q8Q8, Mama-_Q8q8-, and Mama-

_Q8Q8Q8). The triploid homozygous recessive ma1 alleles and homozygous dominant Q8 alleles 

(mamama_Q8Q8Q8) had a 95% upper confidence interval value of 3.11 g·L-1, which was 0.69 

g·L-1 greater than their diploid counterpart (mama_Q8Q8). There were six accessions that 

overlapped in TA between the upper confidence interval of the Mama-_Q8q8- (6.88 g·L-1) and 

lower CI of the MaMa-_Q8Q8Q8 (6.17 g·L-1) allele combinations (Table 2.4, Supplementary 

Table 2.1). 

 

 
 

Figure 2.3 Comparison of cider apple acidity classification systems for cider apple accessions (N 

= 217) harvested between 2017-19 from the U.S. Department of Agriculture Malus germplasm 

collection in Geneva, NY. For the Long Ashton Research Station (LARS), French, and Spanish 

classification systems, the percent of accessions was calculated by titratable acidity. For the 

marker-based classification systems, the percent of accessions was calculated according to the Ma1 

and Q8 allelic combinations for diploids (N = 181) and triploids (N = 36) accessions. The third 

allele in heterozygous triploid accessions was not identified, thus “-” represents an unknown allele. 



 

54 

 

Figure 2.4 The variation in titratable acidity and pH among cider apple accessions (N = 205) 

harvested between 2017-19 from the U.S. Department of Agriculture Malus germplasm collection 

in Geneva, NY, and sorted according to their country or region of origin.

 

When the accessions were separated out into broad geographic regions of origin, they 

exhibited a similar distribution, with accessions representing a TA range between 1.09 to 11.53 

g·L-1 and pH between 2.3 to 5.1 (Figure 2.4). Five accessions were removed because of unknown 

regions of origin and seven accessions were removed due to low representation (≤ 3 accessions). 

Triploid accessions were represented in all major regions of origin. Among the 80 French 

accessions, there was a noticeable skew towards low to medium acid accessions, with 55 



 

55 

accessions falling below the 4.5 g·L-1 LARS acidity threshold and only 25 accessions above that 

threshold. Among the 55 low acid accessions, 41 of had the mama_Q8Q8 genotype. Within 

accessions that originated from England (N = 48), about a quarter (N = 13) also possessed the 

mama_Q8Q8 genotype. Twenty-two accessions originated from Central Europe, 19 of which 

possessed the heterozygous Mama alleles indicating a strong skew towards medium acid 

accessions. All 14 Spanish accessions in the dataset had the homozygous dominant Q8Q8 allele 

while possessing all three types of the Ma1 alleles (MaMa, Mama, and mama). Three of the four 

accessions that possessed the homozygous recessive q8q8 genotype originated in North America. 

Discussion 

Marker-based System for Categorizing Cider Apples 

This study lays the groundwork for using the Ma1 and Q8 (Ma3) acidity markers to 

categorize cider apple germplasm. Both the diploid and triploid Malus accessions had statistically 

significant correlations between TA concentration and the Ma1 marker alone or Ma1 + Q8 markers 

analyzed together, but not the Q8 marker alone. Based on the Ma1 marker and the estimated 

marginal mean model with a 95% confidence interval, the diploid accessions could be grouped 

into ranges of low [mama (<2.4 gL-1)], medium [Mama (2.4 to 5.8 gL-1)], and high [MaMa (>5.8 

gL-1)] acidity. These three categories had negligible overlap in TA concentration among 

accessions in our sample population. However, using only the Ma1 marker had less precision than 

using both the Ma1 and Q8 markers due to the larger range for each acidity category. Using both 

the Ma1 and Q8 markers resulted in more specificity (smaller ranges), but also extensive overlap 

among the seven allelic combinations. For example, there were 16 accessions that overlapped 

between the upper confidence interval for the heterozygous Mama allele and the lower confidence 

interval of the homozygous dominant MaMa when the homozygous dominant Q8 allele was 



 

56 

constant between both the Ma1 allele types. The utility of using one or both markers would 

therefore depend upon the end-user’s goals. Using both the Ma1 and Q8 markers would be very 

useful to plant breeders in terms of precision breeding and making marker-assisted selections, 

whereas using the Ma1 marker alone would be useful for establishing a broad marker-based cider 

apple acidity classification system. The Ma1 marker is presently used in SNP arrays by breeders; 

however, there are no SNP’s currently associated with the Q8 marker. 

Even though we analyzed a robust list of cider apple accessions, there were only four 

diploid and no triploid homozygous recessive q8q8 genotypes. The lack of some allelic groups 

limited our ability to draw conclusions about this allelic combination for identifying fruit acidity. 

It is unclear why there are very few cider apples with the homozygous recessive q8q8 alleles, but 

it may be related to human selection of greater acid apples for cider production. 

There were 128 accessions with a TA concentration for the Mama genotype group that 

spanned both sides of the LARS 4.5 gL-1 threshold. Thus, the Ma1 marker cannot be used as a 

predictor of the acidity component of the LARS cider apple classification system. With three acid 

categories, the French and Spanish systems were somewhat more closely matched to the Ma1 

acidity markers. For example, the Ma1 marker categorized accessions with less than 2.4 gL-1 as 

low acidity, and the French and Spanish systems classified apples with less than 4.5 and 4.85 gL-

1 as douce and sweet, respectively. Medium acidity ranged from 2.4 to 5.8 gL-1 for the marker-

based system, whereas the medium range was 4.5 to 6.75 gL-1 and 4.85 to 6.56 gL-1 for the French 

and Spanish systems, respectively. In general, there was a 1 to 2 gL-1 difference among the French, 

Spanish, and the Ma1 marker-based classification systems. Although apple classification systems 

are widely used by apple growers and cider producers, we have not found documentation that 

explains how the specific TA thresholds were chosen. The variations among the English, French, 



 

57 

Spanish, and our proposed marker-based systems could be due to the perception of other flavor 

components in the apples such as astringency, bitterness, and sugars, which affect the way that 

acidity is perceived (Hampson et al., 2000). More sensory studies must be conducted to analyze 

the interdependency and perception thresholds among these important flavor components. 

Similar to other studies that investigated apple fruit acidity genetics, our analyses indicate 

that the Ma1 gene is a reliable predictor (P < 0.0001) of TA (Bai et al., 2012; Khan et al., 2013). 

Our study thus adds to the body of literature indicating that the Ma1 gene largely determines apple 

acidity (Bai et al., 2015; Brown and Harvey, 1971; Kouassi et al., 2009; Nybom, 1959; Visser and 

Verhaegh, 1978; Xu et al., 2012). Although apple fruit acidity is affected by geographic, 

horticultural, and seasonal variation, the controlling genes are static and can assist breeders in 

making selections and/or crosses, and apple growers in choosing cultivars to plant. 

For the mama genotype, Xu et al. (2012) reported mean TA concentrations of 2.06 gL-1 

which is similar to the concentration of 1.92 gL-1 obtained in our study. However, for the Mama 

and MaMa genotypes, Xu et al. (2012) reported a mean TA of 8.46 and 10.38 gL-1, respectively, 

while our data indicates a mean TA concentration of 5.45 and 6.42 gL-1, respectively. The 

discrepancy between these studies may be attributed to the sample populations and differences in 

maturity at harvest. The Xu et al. (2012) study used two mapping populations containing a 

maternal ‘Royal Gala’ and two paternal M. sieversii parents, whereas the sample population in our 

dataset consisted almost entirely of M. domestica cider cultivars. Duan et al. (2017) proposed 

that cultivated apples M. domestica possess two distinct genetic regions of substantially reduced 

genetic diversity near the Ma1 gene in comparison to progenitor species M. sieversii. Thus, 

increased genetic diversity in M. sieversii could have resulted in the greater TA for the Mama 

genotype in the Xu et al. (2012) study. 



 

58 

The Ma1 and Q8 markers were the focus of our study due to their large genetic effect on 

fruit acidity that has been commonly detected in M. ×domestica accessions with acidity levels less 

than 10 g·L-1 TA. We propose that the Ma4, Ma5, and Ma6 markers be used in future studies to 

further delineate cider apples into more precise classes, particularly for those with greater than 10 

g·L-1 (Rymenants et al., 2020; Ban and Xu, 2020).  

As organic acids are metabolized during the ripening process, pH usually decreases (Ma et 

al., 2015). It should be noted that the pH is in a logarithmic scale and small differences can result 

in a sensorially detectable perception on overall flavor and taste of the fruit (Hampson et al., 2000). 

The low acid mama genotypes had a pH range between 4 to 5 which is greater than the ideal pH 

range (3.2 to 3.8) for microbial control during cider fermentation (Kosseva et al., 2016). Greater 

pH (above 3.8) could result in microbial contamination and development of off flavors during 

cider fermentation. The pH of diploid and triploid accessions is presented in Supplementary Figure 

2.2 A and B. 

Within and Among Year Variability 

Apple fruit acidity can be affected by within year (for example, harvest maturity and fruit 

location within the canopy) and year-to-year variations (for example, environmental conditions, 

crop density, and other biotic factors such as disease pressure) (Brown and Harvey, 1971). The 

variation in TA has been found to be dependent on cultivar and year more so than horticultural 

practices in the orchard (Bourvellec et al., 2015). Multiple years of acidity data collection for many 

of the accessions in our study minimized those effects. Additionally, fruit from different parts of 

the tree were mixed to get a representative sample. Further, in order to reduce variability due to 

ripening stage, all accessions in this study were harvested at a similar maturity, as measured using 

the SPI. 



 

59 

Previous studies looking at the relationship between the Ma1 gene and TA evaluated the 

fruit at a mean SPI between 4 to 6 (Bai et al., 2012, 2015; Xu et al., 2012). However, the current 

study was focused on apple accessions for the hard cider industry; thus, the goal was to test the 

fruit when the SPI was between 6 and 8, a later stage in the maturity process. This late-stage timing 

was unique to this study and helped to gain a cider-specific focus on developing a genetic-marker 

based classification system for cider apples. Since TA fluctuates due to biotic and abiotic factors, 

a marker-based system provides a general range for how much acidity to expect, but exact 

concentrations would vary based on the aforementioned factors. 

Diploid versus Triploid Accessions 

This study is one of the first to elucidate a significant difference in acidity concentrations 

in cider apples due to ploidy. On average, the diploid accessions had a TA concentration that was 

0.33 gL-1 less than triploid accessions, despite the lack of the dominant MaMaMa triploid 

genotype in our sample population. Thus, the mean triploid acidity values were skewed towards 

the heterozygous and homozygous recessive Ma1 genotypes. This study corroborates the evidence 

that from other fruit crops, such as citrus (Citrus sp.) and blueberry (Vaccinium sp.), that ploidy 

increases acidity levels (Ahmed et al., 2020; Mengist et al., 2020). The effect of ploidy on 

increasing acidity levels in cider apples could be due to the quantitative and additive nature of acid 

biosynthesis controlled by the major Ma1 gene (Brown and Harvey, 1971). Thus, more copies of 

the dominant Ma1 allele would increase acidity concentrations. 

 

Evaluated Germplasm 

The USDA Malus germplasm collection contains the world’s largest and most diverse 

catalog of Malus accessions including a large collection of European and historic American cider 



 

60 

apple cultivars, which was ideal for our analyses (Volk and Henk, 2016). The European cider 

cultivars in our study also included recently acquired traditional Spanish cider cultivars and 

traditional English and French bittersweet and bittersharp cultivars (Supplementary Table 2.1). A 

limitation for our study, is that the USDA’s Malus germplasm collection consists of a single tree 

per accession and thus no field replication. Nonetheless, the sheer diversity of the collection has 

often been used to study the genetics of other important crop traits such as dihydrochalcone 

content, volatile profiles, and anthocyanin production (Gutierrez et al., 2018; Sugimoto et al., 

2015). Additionally, the geographically and genetically diverse accessions used for our study 

captured a wide range of juice TA and pH that allowed us to identify potential genetic signatures 

to categorize cider apple genotypes. This diversity was further exploited to analyze accessions 

according to their country or region of origin. 

Every country or region of origin had representative samples of low, medium, and high 

TA. There were some interesting trends observed in the analysis of acidity and region of origin. 

For example, 41 of the 80 analyzed French accessions possessed the homozygous recessive mama 

alleles and homozygous dominant Q8Q8 alleles indicating a strong selection preference for low-

acid accessions. The dominant Q8Q8 allele was observed in all the Spanish accessions indicating 

that this genotype could be fixed in most Spanish accessions. Future pedigree and genetic linkage 

studies will hopefully uncover the history, movement, and introgression of progenitor species and 

lineages among the cider apple cultivars. 

 

Future Cider Apple Classification Recommendations 

This is the first study to use a marker-based classification system to identify cider apples. 

Although we concluded that the Ma1 and Q8 markers could usefully segregate accessions in our 



 

61 

sample pool, more germplasm needs to be evaluated to encompasses a wider range of allelic 

variability, particularly for triploids and accessions with the recessive q8 alleles. Identification of 

additional acidity genes in apples would also help to account for more variability. Furthermore, 

sensory analysis would help to understand the thresholds for the human perception of acidity at 

different TA concentrations, as well as elucidate how other factors, such as sugar and polyphenol 

content, could affect the acid perception in apples and cider (Hampson et al., 2000; Rymenants et 

al., 2020). Lastly, adding genetic markers for sugar and polyphenol content would create a robust 

suite of markers for plant breeders, horticulturalists, and commercial cider producers to rapidly 

identify potential cider apple cultivars in germplasm collections and breeding populations. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



 

62 

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Hao. 2020. BTB-BACK-TAZ domain protein MdBT2-mediated MdMYB73 ubiquitination 

negatively regulates malate accumulation and vacuolar acidification in apple. Hortic. Res. 7(1):1–

12. https://doi.org/10.1038/s41438-020-00384-z. 

 

Zhang ,Y., P. Li, and L. Cheng. 2010. Developmental changes of carbohydrates, organic acids, 

amino acids, and phenolic compounds in ‘Honeycrisp’ apple flesh. Food Chem. 123(4):1013–

1018. https://doi.org/10.1016/j.foodchem.2010.05.053.

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https://doi.org/10.1016/j.foodchem.2010.05.053


 

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Chapter 3 

Reduced crop density enhances total polyphenol content to improve cider apple quality 

Abstract 

Polyphenols contribute to hard cider quality by providing flavor, aroma, color, and 

microbial stability to hard cider. However, the biosynthesis and physiology of these compounds 

are not well understood. Carbohydrate source-sink balance may be involved in year-to-year 

variation observed in juice tannin concentrations. In order to better understand this phenomenon, 

four-year-old ‘Porter’s Perfection’/‘G.11’ trees were subject to the following crop density 

treatments post bloom in 2021: an unthinned control, low, medium, and high crop density 

treatments. At 27, 81, and 160 days after full bloom (DAFB), fruit peel and flesh tissue samples 

were measured for polyphenol compound concentration and composition, and for gene 

expression. At 160 DAFB (harvest), there was a significant increase in the concentrations of the 

monomeric and oligomeric polyphenol compounds in the low crop density treatment compared 

to the unthinned control (P<0.0100), with other crop density treatments being similar in 

concentration to the low crop density treatment. Transcriptome profiling through RNA 

sequencing of the low crop density vs. the unthinned control treatment fruit indicated that genes 

encoding enzymes that catalyze critical functions such as hydroxylation, methylation, and 

glycosylation in the phenylpropanoid pathway were upregulated at 27 DAFB and 81 DAFB, 

which corresponded with increased concentration of phenylpropanoids, especially 

proanthocyanidin monomers and oligomers. Specifically, there was a significant increase in 

anthocyanidin reductase (catalyzes the production of epicatechin) expression in the low crop 

density treatment as compared to the unthinned control at 27 and 81 DAFB (P < 0.0100) in both 



 

68 

the peel and flesh. This research indicates that a reduced crop density enhanced the expression 

of genes involved in the secondary metabolite pathway, which resulted in increased fruit 

polyphenols. Furthermore, we identified eight and three novel ethylene response factor genes, 

26 and 14 MYB-bHLH genes in the flesh and peel, respectively, which are potentially involved 

in proanthocyanidin synthesis. This study provides insights into the carbohydrate source-sink 

relationship within apple trees and the molecular controls and transcription factors involved in 

regulating secondary metabolite production. 

Introduction 

The hard cider industry 

The hard cider industry in the United States has been experiencing a renaissance in 

production and consumption over the past decade (Miles et al. 2020). Hard cider refers to an 

alcoholic beverage made from fermenting apple cider or juice. Total cider sales in the United 

States topped $537 million in 2021 (NielsonIQ, 2022). The per capita consumption of specialty 

drinks including cider increased by 487% from 2010-18. (Degenhard, 2019). The hard cider 

industry is projected to grow at a compounded annual growth rate (CAGR) of 3.5% at least until 

2027 (North America Cider Market, 2022). With the growth in the hard cider industry, demand 

for bitter cider apples that contain high concentrations of tannins has outpaced supply in the 

United States (Becot et al., 2016; Pashow, 2018). Cider apple fruit quality focuses on juice 

chemical concentration and composition, more so than cosmetic appearance and fruit texture, 

thus requiring the development of production and orchard management practices that differ from 

those developed for fresh-market apple orchards. 

 



 

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Classification of apple polyphenols 

Apple polyphenols are an important class of secondary metabolites that provide health 

benefits via their antioxidant properties and organoleptic properties to hard cider. While all apples 

contain varying levels of polyphenols, cider apples in general have much greater concentrations 

(Sanoner et al., 1999; Guyot et al., 2003; Napolitano et al., 2004; Thompson-Witrick et al., 2014). 

Polyphenols are grouped into five categories – dihydrochalcones (e.g., phloridzin), phenolic 

acids (e.g., chlorogenic acid and hydroxycinnamic acids), flavonols (e.g., quercetin glycosides), 

proanthocyanidins (e.g., catechin, epicatechin, and their oligomers and polymers), and 

anthocyanins (e.g., cyanidin glycosides) (McGhie et al., 2005; Henry-Kirk et al., 2012). Among 

them, flavonols and anthocyanins are present exclusively in peel tissue, with the exception of a 

few red fleshed genotypes (Takos et al., 2006a; Renard et al., 2007; Ban et al., 2007; Henry-Kirk 

et al., 2012). Hydroxycinnamic acids such as phlorizin (phloretin glycoside) are highly abundant 

in cider apple flesh, peel, as well as in leaves and bark (Gutierrez et al., 2018). Phenolic acids 

such as chlorogenic acid are highly abundant in apple fruit flesh and peel (Renard et al., 2007).  

Proanthocyanidin production in apples 

Proanthocyanidins, also known as condensed tannins, are an abundant class of 

polyphenols in cider apples that provide bitterness and astringency to hard cider (Lea and Arnold, 

1978; Miles et al., 2020). Proanthocyanidin oligomers and polymers under four sub-units are 

responsible for bitterness whereas larger tannin units are responsible for the astringent mouthfeel 

(Lea and Arnold, 1978; Vidal et al., 2003). Astringency refers to the perception of ‘dryness’ that 

is caused due to the binding of tannins with salivary proteins, resulting in the decrease of 

moisturizing property of saliva (McRae and Kennedy, 2011). Fresh-market and juice apples 



 

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possess low concentrations of proanthocyanidins (<100 mg·L-1), whereas many cider apples 

possess proanthocyanidin concentrations greater than 2 g·L-1 on average (Kahle et al., 2005; 

Renard et al., 2007). This represents a minimum 20-fold difference in PA concentrations between 

many fresh-market and cider apple genotypes. Highly tannic cider apple cultivars not only 

provide the required bitterness and astringency to hard cider but also possess required levels of 

sugar and acidity in a traditional cider (Lea and Timberlake, 1974).  

Through the phenylpropanoid pathway, the monomers catechin and epicatechin are 

produced due to the activity of leucoanthocyanidin reductase (LAR) and anthocyanidin 

reducatase (ANR), respectively, which can then polymerize with catechin initiators and 

epicatechin elongators to form various proanthocyanidin oligomers (Lea and Arnold, 1978; 

Delage et al., 1991; Guyot et al., 2003; Henry-Kirk et al., 2012; Liao et al., 2015). While the 

genes involved in the production of phenylpropanoids have been elucidated to a great extent, the 

transcriptional and epigenetic control of these genes is far from fully studied and many research 

studies have focused on transcription factors (TF) and their role in regulating the 

phenylpropanoid pathway. The MYB family of genes has been extensively studied and they play 

a major role in regulation of the phenylpropanoid pathway through transcriptional activation, 

degradation, and gene promoter activation (Li et al., 2015). They work in conjunction with other 

TF’s such as the basic helix loop helix protein (bHLH) and WD proteins (Allan et al., 2008). 

Ethylene response factors (ERF’s) also play a critical role in the phenylpropanoid pathway. 

MdERF1B and MdERF3 have been identified to promote proanthocyanidin and anthocyanin 

synthesis in collaboration with MYB genes such as MdMYB1 and MdMYB11 (An et al., 2018a; 

Zhang et al., 2018). MdRAV1 was found to bind to the promoter of MdANR2 (anthocyanidin 

reductase) and inhibit its activity (Li et al., 2020). Other studies have indicated that MdERF1A, 



 

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1B, and MdERF23 also have a role to play in procyanidin regulation (Zhang et al., 2018; Li et 

al., 2020).  

Recent research on the effect of orchard management practices on cider apple polyphenols 

Recently, there has been some focus on the different production and orchard 

management practices to maximize cider specific quality characteristics such as sugars, 

fermentation kinetics, and polyphenol concentrations. Karl and Peck (2022) focused on the 

effects of restricted sunlight on juice quality, and Karl et al. (2020) focused on pre-harvest 

nitrogen fertilization on fermentation kinetics. 

Several studies have reported on the effect of crop density on low polyphenol fresh-

market apple cultivars, especially focusing on peel or flesh ‘antioxidants’ (Awad et al., 2001; 

Wünsche et al., 2005; Peck et al., 2016). Flavonols and anthocyanins derived from peel tissue 

have also been extensively studied (Awad et al., 2001; Takos et al., 2006b; Ban et al., 2007; 

Khanizadeh et al., 2008). These compounds are not extractable from the peel in pressing practices 

followed by the majority of cider producers, thus limiting its relevance to hard cider (Guyot et 

al., 2003; Devic et al., 2010). There have been very limited studies on cider specific cultivars. 

Zakalik et al. (2023) conducted a three-year study on the effect of crop density on seven cider 

apple cultivars and recommended a crop density of ~9 fruits/cm2 TCSA to maximize yields and 

polyphenol concentrations while maintaining required return bloom for the next year. These crop 

density recommendations are generally greater than fresh market crop density recommendations. 

Zakalik et al. (2023) found that while polyphenol concentrations increased with crop density in 

the first year of treatment implementation, cumulative yields of polyphenols over a three-year 

period decreased with increased crop density. Also, Zakalik et al. (2023) found that reduced crop 



 

72 

density enhanced juice quality parameters such as sugars, acidity, and total polyphenol 

concentration in seven cider apple cultivars.  

Research hypothesis and objectives 

The aim of this experiment was to understand the physiological and molecular 

underpinnings of the polyphenol development in cider apples in relation to crop density; and to 

optimize management practices to achieve sustained enhancement of polyphenol concentrations 

in cider apples. Also, the experiment seeks to understand the development of the 

phenylpropanoid pathway products throughout the growing season and characterize the diverse 

accumulation patterns of different polyphenol compounds. I hypothesize that reduced crop 

density will enhance polyphenol production in cider apples and genes responsible for promoting 

polyphenol production will be upregulated under low crop density conditions. 

Materials and Methods 

Trial Location and Experimental Design 

The experiment was conducted in 2021 at the Cornell University Agricultural 

Experiment Station, Ithaca, NY (42.443880, -76.464919). For this experiment, we selected two 

high-tannin cultivars with diverse characteristics while still being relevant and suitable for 

production in NY state. ‘Binet Rouge’ is a French bittersweet with high tannin and low acidity 

content whereas ‘Porter’s Perfection’ is an English bittersharp with both high tannin and acidity 

content, thus providing the necessary diversity in terms of geographical and historical origins, 

differences in titratable acidity and polyphenol composition. Additionally, both cultivars are 

recommended for planting in NY state (Peck et al., 2021). 



 

73 

All trees for this experiment were planted in spring 2018 in uniform rows with 

approximately 100 trees per row using a 3-wire trellis system and trained as a tall spindle of 

training system at 1.2 m between trees × 3.7 m between rows (~2,200 trees/ha). ‘Geneva 11’ 

(‘G.11’) was used as a rootstock. ‘Porter’s Perfection’ in classified as a bittersharp (high tannin 

and high acid concentration) and ‘Binet Rouge’ (high tannin and low acid concentration) is 

classified as a bittersweet according to the Long Ashton Research Station (LARS) acidity 

classification system (Barker and Ettle, 1910). 

There were five replicated blocks each of which contained four crop density treatments: 

low [5 fruit/cm2 trunk cross sectional area (TCSA)], medium (10 fruit/cm2 TCSA), high (15 

fruit/cm2 TCSA), and an unthinned control treatment (UTC) which had no crop density 

manipulation. Two trunk diameter measurements were taken perpendicular to each other at 40 

cm above the graft union to measure TCSA. Each of the four treatments were randomly assigned 

to two side-by-side trees within each block. The TCSA for each tree was multiplied with the 

target crop density to arrive at the target fruit number per tree. All fruit clusters were pre-thinned 

to the central fruit in the whorl due to availability of sufficient fruitlets to ensure target crop 

density numbers. Following this, the trees were hand thinned to arrive at the target crop density. 

Efforts were made to uniformly distribute the fruit throughout the canopy. Trees were assessed 

to be in full bloom (greater than 50% of the flowers in bloom) on 14 May 2021 for ‘Porter’s 

Perfection’ and on 20 May 2021 for ‘Binet Rouge’. The hand thinning treatments were 

implemented on 27 May 2021 for ‘Porter’s Perfection’ and on 1 June 2021 for ‘Binet Rouge’. 

No chemical flower or fruit thinning chemicals were applied. The orchard was otherwise 

managed using a conventional pest management regimen for diseases and arthropods (Agnello 

et al., 2021). 



 

74 

 

 

Figure 3.1. At harvest photographs of representative trees for the low, medium, and high crop 

density treatments thinned to 5, 10, and 15 fruit/cm2 TCSA, and an Unthinned Control (UTC) 

treatment. A) ‘Porters Perfection’/‘G.11’ and B) ‘Binet Rouge’/‘G.11’.  

 

Fruitlet and harvest sampling, and return bloom assessment 

Six representative fruit from each experimental unit were sampled throughout the 

growing season at approximately 4-week intervals of 20, 48, 74, 105, and 146 days after full 

bloom (DAFB) for ‘Binet Rouge’, and at 27, 55, 80, 110, 139, and 160 DAFB for ‘Porter’s 

Perfection’. The fruit samples were collected and kept on ice until they were separated into peel 

and cortex tissue with knives. Care was taken to remove the seed region including all the seeds 



 

75 

from the tissue samples. Fruit tissue samples were flash frozen in liquid nitrogen and then stored 

at -80 °C until analysis. 

The Starch Pattern Index (SPI) was used to assess the maturity of fruit to decide on the 

harvesting date and fruit were harvested as close to an SPI of 8 (Blanpied and Silsby, 1992). 

‘Porter’s Perfection’ was harvested on 23 Oct 2021at an average SPI of 6.9. However, ‘Binet 

Rouge’ experienced a heavy pre-harvest fruit drop and had to be harvested much earlier than full 

maturity on 7 Oct 2021 with an average SPI of 3.1. Fruit yields were recorded for each 

experimental tree, as depicted in Figure 3.1. Fruit mass was measured with an Adam CPW field 

scale (Oxford, CT, USA). Return bloom was assessed on 17 May 2022 for ‘Porter’s Perfection’ 

and on 19 May 2022 for ‘Binet Rouge’ by counting the number of flower clusters on each 

experimental tree at the ‘pink’ stage of flower development (Chapman and Catlin, 1976). 

Fruit Quality Analyses 

A subset of ten fruit per experimental unit were randomly selected and used for maturity 

and quality analyses. Fruit mass and circumference were measured using a caliper integrated into 

a GÜSS fruit texture analyzer (Jennings, Strand, South Africa). Visual color measurements of 

fruit were measured as percent surface area of the fruit covered by red blush with the parameter 

ranging from 0-100%. Peel chlorophyll content was measured as a proxy for ripeness using the 

degree of absorbance meter (Model 53500, T.R. Turoni Srl, Forli, Italy). Fruits were then 

assessed for flesh firmness on both the blush and non-blush side with a GÜSS penetrometer 

(Jennings, Strand, South Africa) fitted with a 11.1 mm probe. Subsequently, fruit ripeness was 

measured using the SPI by spraying an equatorial wedge of the fruit with an iodine solution 

consisting of 0.22 g·L−1 iodine, 0.88 g·L−1 potassium iodide (Sigma-Aldrich, St. Louis, MO, 



 

76 

USA). Following the firmness and ripeness testing, fruit were milled, placed into “Good Nature” 

filter bags (Buffalo, NY, USA), and pressed using the Norwalk 290 (Bentonville, AR, USA). 

This set up is similar to a “rack and cloth” style press used by many cider producers. Juice 

samples were then frozen at -80 °C until further analyses.  

Juice Chemistry Analysis 

Samples were analyzed for soluble solids content (SSC), titratable acidity (TA), and 

total polyphenol content (TPC). Soluble solids content was measured using a hand-held PAL-1 

BLT digital refractometer (Omaeda, Saitama, Japan). Titratable acidity was measured using a 

Metrohm 809 Titrando autotitrator (Herisau, Switzerland) by titrating 5 mL of juice in 40 mL of 

ultrapure Milli-Q-water (Darmstadt, Germany) against a 0.1 M NaOH solution (Sigma-Aldrich, 

St. Louis, MO, USA) until the pH reached a value of 8.1. TPC was measured using the Folin-

Ciocalteau method (Singleton and Rossi, 1965) on a Spectramax 284 Plus spectrophotometer 

and Softmax Pro 7 Microplate Data analysis software (Molecular Devices, San Jose, CA). For 

juice samples, 1.5 µL of the sample or gallic acid standard (Sigma-Aldrich, St. Louis, MO, USA) 

was mixed with 34.9 µL of water and 90.9 µL of Folin-Ciocalteau reagent (M.P. Biochemicals, 

Aurora, Ohio, USA) in a Cellistar 96 well microplate (Greiner Bio-One, Monroe, NC, USA); 

three min after, 72.7 µL of 7 g·L−1 Na2Co3 (Sigma-Aldrich, St. Louis, MO, USA) was added; 

the microplate was incubated for 1hr under dark conditions before being measured at 765 nm. 

Gallic acid was used as a standard for the TPC measurement and results were reported in gallic 

acid equivalents.  

For peel and flesh tissue, TPC was measured according to the protocol developed by 

Huber & Rupasinghe (2009) with a few modifications. The flash frozen apple peel and cortex 



 

77 

tissue stored at -80 °C were lyophilized in a freeze drier (Labonco FreeZone 12L-84C bulk tray 

drier, Kansas City, MO, USA) which was set to 0 °C for 72 hr, and ground to a fine powder using 

a coffee grinder (KitchenAid, Benton Harbor, MI, USA). Two hundred milligrams of the tissue 

sample were sonicated (Branson 2800, Sonitek, Milford, CT, USA) with 10 mL of HPLC grade 

100% methanol (VWR Chemicals, Radnor, PA, USA) for 15 min to extract the polyphenols. The 

extract was then centrifuged at 3,500 × g for 15 min. 10 µL of the extract, or gallic acid was 

mixed with 100 µL of the Folin-Ciocalteau reagent and gently mixed in a 96-well clear 

microplate. After 6 min, 80 µL of 7.5 g·L−1 Na2Co3 was added followed by a 1 hr incubation 

period in the dark and absorption measured at 765 nm.   

High Performance Liquid Chromatography Analysis 

The monomeric polyphenols in harvest juice samples, and lyophilized peel and cortex 

samples taken throughout the growing season, were analyzed using a protocol modified from 

Hendrickson et al. (2016) and Tyagi et al. (2020). The monomeric polyphenols were 

chromatographically separated by Reverse Phase-HPLC using a Poroshell HPH-C18 (4.6 ×100 

mm 2.7 μm particle) Agilent HPLC column on an Agilent Infinity 1260 HPLC system (Agilent 

Technologies, Santa Clara, CA, USA) equipped with a diode array detector using a binary solvent 

gradient with mobile phase A (1.5% formic acid in water) and mobile phase B (1.5% formic acid 

and 1.36% water in acetonitrile, VWR, Radnor, PA, USA). Juice samples were centrifuged at 

14,000 g force for 15 min and the supernatant was filtered through a 0.2 µm membrane filter 

before injecting. A 10 µL filtrate was injected into the HPLC for monomeric polyphenol analysis.  

Lyophilized peel and cortex tissue (100 mg) was extracted with 3 mL of 50% methanol, 

1% HCl, and 10 µL of internal standard 4’,5’,7’-trihydroxy flavanone (Sigma Aldrich, St. Louis, 



 

78 

MO, USA). The homogenate was vortexed and incubated at 4 °C for 4 hr on ice with occasional 

mixing. Following that, the homogenate was centrifuged at 4,000 × g for 5 min at 4 °C and the 

supernatant was transferred to a clean 15 mL tube. The pellets were reextracted twice with 1 mL 

of methanol, and all the supernatants were combined to bring it to the final volume of 5 mL. 

From this extract, 1 mL was centrifuged at 14,000 × g for 5 min at 4 °C to remove any particulates 

and 5 μL was injected into the HPLC column for monomeric polyphenol analysis. The remaining 

4 mL of sample was stored at -20 °C for proanthocyanidin measurements. 

The column temperature was maintained at 35 °C for the monomer polyphenols 

measurement. Diode array detector wavelengths were set at 280, 320, and 360 nm. The starting 

condition of the gradient was 95% of solvent A and 5% of solvent B. Subsequently, solvent B 

was linearly increased to 15% in 25 min, then to 27% in 10 min, and keep at 27% for 3 min. 

Thereafter, the mobile phase was reverted to the initial condition in 2 min and held for 3 min for 

re-equilibration of the column before the next injection. The total run time was 43 min.  

The eluted compounds were monitored and identified by spectral and retention time 

comparisons to external standards at three different wavelengths: 280 nm [(+)-catechin (C), (−)-

epicatechin (EC), procyanidin B1, procyanidin B1, procyanidin C1, procyanidin A1, procyanidin 

A2, and phloridzin, 320 nm (5-caffeoylquinic acid, chlorogenic acid, 4-caffeoylquinic acid, p-

coumaric acid, ferulic acid, and sinapic acid), and 360 nm (quercetin-3-galactoside, quercetin-3-

glucoside, quercetin-3-O-rutinoside, avicularia, and quercitrin). The identified compounds were 

quantified by external calibration curves.  

(+)-Catechin (C), (−)-epicatechin (EC), quercetin (quercetin-3-galactoside, quercetin-3 

glucoside, quercetin-3-O-rutinoside), avicularia, quercitrin, 5-caffeoylquinic acid, chlorogenic 



 

79 

acid, 4-caffeoylquinic acid, p-coumaric acid, ferulic acid, sinapic acid, procyanidin B2, 

procyanidin C1, procyanidin A1, procyanidin A2, and phloridzin were purchased from Sigma-

Aldrich (St. Louis, MO, USA). Procyanidin B1 was purchased from INDOFINE Chemical 

Company (Hillsborough, NJ, USA). All data processing and analysis were completed using 

Agilent CDS ChemStation software on an Agilent 1260 Infinity HPLC.   

Proanthocyanidin extraction and phloroglucinol analysis 

The proanthocyanidin (PA) analysis of lyophilized peel and cortex tissue was processed 

and analyzed according to the protocol developed by Tyagi et al. (2022). The pellet from the 

monomeric polyphenol extraction was resuspended in 8 mL of 70% acidified acetone [containing 

0.1% ascorbic acid and 0.05% trifluoroacetic acid] for the extraction of polymers. Acetone was 

obtained from Honeywell (Muskegon, MI, USA), ascorbic acid from Sigma-Aldrich (St. Louis, 

MO, USA) and trifluoroacetic acid from Thermo Scientific (Ward Hill, MA, USA). The extract 

was centrifuged at 4,000 × g for 5 min and extracted overnight at 4 °C. The pellet was 

resuspended twice in 2 mL of acidified acetone to make a final volume of 12 mL. The methanol 

supernatant from the monomeric polyphenol analysis was combined with the acetone 

supernatants and were evaporated under vacuum at 30 °C to near dryness using a Buchi® 

Rotavapor R110 (Flawil, Switzerland). The extract was resuspended in 1 mL of methanol and 

stored at −20 °C until further analysis. 

A solid phase microextraction (SPME) was used to isolate proanthocyanidins following 

the protocol developed by Girardello et al. (2019). The solid phase consisted of 10 mL of 

Toyopearl HW-40 F size exclusion media (Tosoh Biosciences, Shiba, Tokyo, Japan). The 

extraction was performed in triplicate with two technical replicates. The methanol extracts were 



 

80 

diluted to 25% of their concentrations with distilled water before loading on to the solid phase 

microextraction (SPME) column. Following extraction, the vacuum reduced eluent was 

resuspended in 500 μL methanol and stored at -80 °C until analysis. 

The composition of isolated proanthocyanidins from SPME extraction was determined 

by phloroglucinolysis (Kennedy and Jones, 2001) with some modifications (Tyagi et al., 2020). 

Briefly, 80 μL of sample and 80 μL of phloroglucinol reagent (100 mg·mL-1 phloroglucinol 

(Sigma-Aldrich, St. Louis, MO, USA) and 20 mg·mL-1 ascorbic acid (Avantor, Radnor, PA, 

USA) prepared in 0.2 M HCl acidified methanol (Sigma-Aldrich, St. Louis, MO, USA) were 

combined and vortexed. The reaction was performed in duplicate at 50 °C for 20 min and it was 

quenched with 800 μL of 40 mM sodium acetate (VWR Chemicals, Solon, Ohio, USA). 

Quenched samples were centrifuged at 14,000 ×g for 5 min to sediment any debris or particles. 

The phloroglucinolysis reaction products were chromatographically separated by RP-HPLC 

using a Kinetex C18 (4.6 × 150 mm, 2.6 μm particle) HPLC column on the HPLC machine 

described above using a binary solvent gradient with mobile phase A (0.1% formic acid in water) 

and mobile phase B (0.1% formic acid in acetonitrile). Column temperature was maintained at 

35 °C. The diode array detector wavelength was set to 280 nm. Injection volume of 20 μL was 

used for extracted sample. The starting condition of gradient was 94% of solvent A and 6% of 

solvent B. Subsequently, solvent B was linearly increased to 18% in 13 min, then to 85% in 1 

min and keep at 85% for 2 min. Thereafter, the mobile phase was reverted to the initial condition 

in 1 min and held for 3 min for re-equilibration of column before the next injection. Data analysis 

was performed using Agilent CDS ChemStation software and individual reaction products 

(Epicatechin-phloroglucinol adduct, catechin, and epicatechin) were quantified by an external 



 

81 

calibration curve for (+)-catechin (C) using their response factor relative to catechin (Singleton 

& Rossi, 1965). 

RNA Extraction and Library Preparation 

The frozen peel and cortex tissue was finely ground under liquid nitrogen using a pestle 

and mortar. RNA was extracted from the peel and cortex tissue using the protocol developed by 

Reid et al. (2006). The finely ground tissue samples (350 mg) were added to 10 mL of pre-

warmed (65 °C) RNA extraction buffer (2% CTAB, 2% PVP, 300 mM Tris HCl at pH 8.0, 25 

mM EDTA, 2 M NaCl, 0.05% spermidine trihydrochloride, 2% β-mercaptoethanol), and 

incubated in 65 °C water bath for 10 min with regular vortexing. All the above chemicals were 

obtained from VWR Chemicals (Solon, Ohio, USA). Ten mL of chloroform (VWR Chemicals, 

Radnor, PA, USA): isoamylalcohol (VWR Chemicals, Solon, Ohio, USA) in the ratio of 24:1 

was added to the mixture and centrifuged at 3,500 × g for 15 min at 4 °C. The aqueous layer was 

transferred to a new tube and centrifuged at 30,000 × g for 20 min at 4 °C to remove any 

remaining insoluble material. This step was repeated twice, following which 0.1 vol 3 M NaOAc 

(pH 5.2) and 0.6 vol isopropanol (Avantor, Radnor, PA, USA) were added, mixed, and then stored 

at -80 °C for 30 min. Nucleic acid pellets (including any remaining carbohydrates) were collected 

by centrifugation at 3,500 × g for 30 min at 4 °C. The pellet was dissolved in 1 ml Tris-EDTA 

[pH 7.5, VWR Chemicals, Radnor, PA, USA)] and transferred to a microcentrifuge tube. To 

selectively precipitate the RNA, 0.3 mL of 8 M LiCl (VWR Chemicals, Solon, Ohio, USA) was 

added and the sample was stored overnight at 4 °C. RNA was pelleted by centrifugation at 20,000 

× g for 30 min at 4 °C then washed with 500 µL of ice cold 70% EtOH (VWR Chemicals, Radnor, 

PA, USA), air dried, and dissolved in 5100 μl DEPC-treated water. RNA was checked for their 



 

82 

260/280 nm ratios using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, 

Wilmington, DE, USA), and 1% agarose gels using the electrophoresis and GelDoc (Bio-Rad, 

Hercules, California, USA) to check for integrity. RNA was purified using a RNeasy kit (Qiagen, 

Valencia, CA, USA). 

Gene expression and transcriptome analysis were conducted by Polar Genomics LLC. 

(Ithaca, NY, USA). Strand-specific RNAseq libraries were constructed using the protocol 

described by Zhong et al. (2011). Briefly, the steps included polyA RNA isolation and 

fragmentation, first-strand complementary DNA (cDNA) synthesis, second strand synthesis with 

Deoxyuridine Triphosphate (dUTP), end-repair, DNA A-tailing, Y-shape adapter ligation, triple-

solid-phase reversible immobilization purification and size selection, PCR enrichment, and 

mixed barcoded libraries for multiplexed sequencing.  

RNA Sequencing, Differential Gene Expression, and Gene Enrichment Analysis 

Pooled libraries were sequenced using HiSeqX 150 bp paired end sequencing 

(Psomagen Inc, Rockville MD). Raw RNA-Seq sequencing reads have been deposited in the 

NCBI BioProject database under the accession number PRJNA1004866. Raw RNA-Seq reads 

were processed to remove adaptors and low-quality sequences using Trimmomatic (version 0.39; 

Bolger et al., 2014) with parameters ‘SLIDINGWINDOW:4:20 LEADING:3 TRAILING:3 

MINLEN:40’ and to remove polyA/T tails using PRINSEQ++ (v1.2; Cantu et. al. 2019) with 

parameters ‘-min_len 40 -trim_tail_left 10 -trim_tail_right 10’]. The remaining cleaned reads 

were aligned to the ribosomal RNA database (Quast et al., 2013) using Bowtie (version 1.1.2; 

Langmead, 2010) allowing up to three mismatches, and those aligned were discarded. The final 

cleaned reads were aligned to the ‘Golden Delicious’ double haploid (GDDH13) genome (v1.1; 

https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1004866/


 

83 

Daccord et al., 2017) using HISAT2 (version 2.1.0; Kim et al., 2019) with default parameters. 

Based on the alignments, raw read counts for each gene were calculated and then normalized to 

fragments per kilobase of exon model per million mapped fragments (FPKM). Raw read counts 

were then fed to DESeq2 to identify differentially expressed genes (DEGs) using a cutoff of 

adjusted P value < 0.05 and fold change ≥ 2. Gene ontology terms enriched in the lists of genes 

were identified using Blast2GO (Conesa et al., 2005) with a cutoff of adjusted P value < 0.05. 

Transcription factors were identified by using the blast tool in the genome database for Rosaceae 

and homolog comparisons with the Arabidopsis genome (Sook et al. 2018). 

Statistical Analysis 

This experiment was analyzed as a randomized complete block design using R (R Core 

Team, 2014). Regressions were analyzed as mixed models with a random block term, using the 

lmer function from the lme4 package. The emtrends function was used to separate the regression 

trendlines according to cultivar. Mean separation for a family of estimates (estimated marginal 

means, emmeans package), using the Tukey method, was performed using the cld function 

(multcomp package). Model assumptions were checked by assessing R2 values and examining 

the distribution and spread of residuals. The ggplot2 funtion was used to plot regressions and 

estimated marginal means graphs. A heatmap of the individual polyphenol monomers was 

generated with the Metaboanalyst 5.0 (Pang et al. 2021) software with the autoscaling function 

(mean-centred and divided by the square root of the standard deviation of each variable). The 

Venn diagrams were generated with Venny 2.1 (Oliveros, 2007-15). 

  

 



 

84 

Results 

Yield and return bloom 

‘Porter’s Perfection’ had an average TCSA of 14.7 cm2 while ‘Binet Rouge’ had an 

average TCSA of 7.8 cm2 (Supplementary Figure 3.1). There were no significant differences in 

TCSA among treatments for each cultivar. Even though they were planted in the same year, the 

‘Porter’s Perfection’ trees were larger in size and thus had a greater crop density than the ‘Binet’ 

Rouge’ trees There was a significant positive linear correlation between crop density and yield 

for both ‘Porter’s Perfection’ (P < 0.0001) and ‘Binet Rouge’ (P = 0.0001) (Figure 3.2). The UTC 

treatments had the greatest yields at 32.6 kg/tree and 8 kg/tree for ‘Porter’s Perfection’ and ‘Binet 

Rouge’, respectively. Return bloom density had a significantly negative linear correlation with 

crop density for both ‘Porter’s Perfection’ (P < 0.0001) and ‘Binet Rouge’ (P = 0.0032). For both 

cultivars, the UTC treatments had no flower blooms in 2022 (Supplementary Figure 3.1). Thus, 

despite producing high yields during the experiment year, there would have been no fruit the next 

year. Based on the regression model, ‘Porter’s Perfection’ would have zero return bloom the next 

year with a crop density equal to or greater than 28.4 fruit cm-2 TCSA, whereas ‘Binet Rouge’ 

would exhibit the same at a threshold of 27.8 fruit cm-2 TCSA (Supplementary Figure 3.1). 

Fruit mass and circumference 

The individual fruit mass of both cultivars had a significant negative linear correlation 

with crop density (Figure 3.2, P < 0.0001). ‘Binet Rouge’ had a stronger inverse relationship with 

fruit mass as compared to ‘Porter’s Perfection’. ‘Porter’s Perfection’ and ‘Binet Rouge’ exhibited 

a 0.9 and 1.8 g decrease, respectively, for every unit (fruit/cm2 TCSA) increase in crop density. 

The individual average fruit mass of ‘Porter’s Perfection’ and ‘Binet Rouge’ across all treatments 



 

85 

were 57 g and 78 g respectively (Figure 3.2). The fruit circumference also had a significant 

negative linear relationship with crop density for both cultivars (P < 0.0001).  

 

 

Figure 3.2 Regression between crop density and juice quality parameters for ‘Porters 

Perfection’/‘G.11’ and ‘Binet Rouge’/‘G.11’ subject to four crop density treatments in 2021 - 

low, medium, and high crop density treatments thinned to 5, 10, and 15 fruit/cm2 TCSA, and an 

Unthinned Control (UTC). Each point represents a single measurement from one tree. Total 

polyphenol content was measured using the Folin-Ciocalteau Assay (GAE-Gallic Acid 

Equivalents). 

 

 

 

 



 

86 

Harvest ripeness and quality parameters 

The difference of absorbance (DA), blush, starch pattern index (SPI), and flesh firmness 

were used to assess fruit ripeness and quality. The greater the DA value, the more unripe the fruit. 

The DA values for both cultivars ranged between 0.6 and 1.2. The DA values for both cultivars 

were similar in relation to their crop densities and had a positive linear relationship with crop 

density (Figure 3.2). This relationship was significant for ‘Porter’s Perfection’ (P = 0.0300).  

The SPI varied significantly between the two cultivars. ‘Porter’s Perfection’ was 

harvested at an average SPI of 6.9, whereas ’Binet Rouge’ was harvested at 3.1 SPI due to pre-

harvest fruit drop. Some cultivars such as ‘Binet Rouge’ are prone to pre-harvest fruit drop before 

fruits are fully mature, and we had to harvest the fruit left on the tree earlier than recommended, 

to obtain fruits for our analyses. Both cultivars had an insignificant relationship between SPI and 

crop density. There was as a marked difference in the relationship between firmness and crop 

density for both cultivars. While ‘Porter’s Perfection’ had a significant positive linear relationship 

with firmness (P = 0.0009), ‘Binet Rouge’ had a insignificant increase in firmness with greater 

crop density. On average, ‘Porter’s Perfection had a firmness of 90 N, whereas ‘Binet Rouge’ 

had a greater firmness at 101 N (Figure 3.2). Both ‘Porter’s Perfection’ and ‘Binet Rouge’ had 

~60%. red peel pigmentation. ‘Porter’s Perfection’ had a significant negative correlation between 

blush and crop density (P < 0.0130). 

Juice Quality Parameters 

Total soluble solids (TSS), titratable acidity (TA), and total polyphenol concentration 

(TPC) were measured on juice extracted from different crop density treatments at harvest. Both 

cultivars had an average TSS content of ~16°Brix. Both ‘Porter’s Perfection’ and ‘Binet Rouge’ 



 

87 

had a significant negative linear relationship of TSS with crop density (Figure 3.2; P = 0.0098, P 

= 0.0103). ‘Porter’s Perfection’ had an average TA of 9 g·L-1whereas ‘Binet Rouge’ had an 

average TA of 2.1 g·L-1. ‘Porter’s Perfection had a significant negative linear relationship 

between TA and crop density (Figure 3.2, P < 0.0001). The TPC, as measured by the Folin-

Ciocalteau method, indicated that ‘Porter’s Perfection’ had an average concentration of 4 g·L-1, 

whereas ‘Binet Rouge’ had an average concentration of 1.7 g·L-1 (Figure 3.2). Similar to TA, 

only ‘Porter's Perfection’ had a significant negative linear relationship between TPC and total 

polyphenol (Figure 3.2, P < 0.0001). 

 

 

 

 

 

 

 

 

 

 



 

88 

  

Figure 3.3 Regression between crop density and select polyphenol compounds from juice of Perfection’/’G.11’ and ‘Binet 

Rouge’/’G.11’ subject to four crop density treatments in 2021- low, medium, high crop density treatments thinned to 5, 10, 15 

fruit/cm2 TCSA, and an UnThinned Control (UTC). Each point represents a single measurement of one tree. 

 



 

89 

 

Juice polyphenol monomers 

Fifteen polyphenol compounds in the extracted juice were measured for both cultivars 

(Figure 3.3 and Supplementary Figure 3.2). Among them, Figure 3.3 focuses on ten polyphenol 

compounds that have significant correlations with crop density for at least one cultivar. ‘Porter’s 

Perfection’ consistently had a significant negative correlation between nine of the polyphenol 

compounds and crop density (Figure 3.3). The only exception was phlorizin which had a positive 

linear correlation with crop density for ‘Porter’s Perfection’ (P = 0.0004). There were no significant 

correlations between the polyphenol monomers and crop density for ‘Binet Rouge’. ‘Binet Rouge’ 

consistently had less concentrations of all the measured polyphenol compounds in comparison to 

‘Porter’s Perfection’, expect for 4-caffeoylquinic acid and p-coumaric acid (Figure 3.3, 

Supplementary Figure 3.2).  

For ‘Porter’s Perfection’, procyanidin B1, procyanidin B2, and chlorogenic acid had 

greater concentrations (>100 mg·L-1 inclusive), epicatechin, phlorizin, 4-caffoeoylquinic acid, p-

coumaric acid, and quercetin-3-galactoside had medium concentrations (10-100 mg·L-1), whereas 

the remaining compounds had low concentrations (<10 mg·L-1 inclusive) (Figure 3.3, 

Supplementary Figure 3.2). For ‘Binet Rouge’, only chlorogenic acid had a high concentration 

(>100 mg·L-1), procyanidin B1, B2, epicatechin, 4-caffoeoylquinic acid, and p-coumaric acid had 

medium concentrations (10100 mg·L-1), and the rest of the compounds had low concentrations 

(<10 mg·L-1). Among all the polyphenol compounds measured, chlorogenic acid had the greatest 

concentration with an average concentration of 399 and 379 mg·L-1 for ‘Porter’s Perfection’ and 

‘Binet Rouge’, respectively.   

There were four proanthocyanidin compounds that were identified in the juice of both 

cultivars: catechin, epicatechin, procyanidin B1 (catechin-epicatechin dimer), and procyanidin B2 



 

90 

 

(epicatechin-epicatechin dimer). Among the procyanidins, procyanidin B2 had the greatest 

concentrations at 236 mg·L-1 and 22 mg·L-1 for ‘Porter’s Perfection’ and ‘Binet Rouge’, 

respectively, whereas catechin had the lowest concentrations at 5 mg·L-1 and 1 mg·L-1 for ‘Porter’s 

Perfection’ and ‘Binet Rouge’ respectively (Figure 3.3). Among the different phenolic acids 

measured, chlorogenic acid had the greatest concentration (350-400 mg·L-1), followed by 4-

caffoeylquinic acid (45-70 mg·L-1), and p-coumaric acid (30-45 mg·L-1) (Figure 3.3, 

Supplementary Figure 3.2). 5-caffeoylquinic acid had very low concentrations below 1 mg·L-1 

(Supplementary Figure 3.2). Among the flavonols measured, quercetin-3-galactoside had the 

greatest concentrations (5-12 mg·L-1) (Figure 3.3). Quercitrin and quercetin-3-glucoside had low 

concentrations (<10 mg·L-1), whereas rutin and avicularin had very low concentrations (<1 mg·L-

1). Phlorizin and cyanidin-3-glucoside fall under chalcones and anthocyanins respectively. Due to 

the significant regression correlations found in the cultivar ‘Porter’s Perfection’, we focused on 

this cultivar for further analysis of flesh, peel, and juice tissue at different stages of fruit growth 

and ripening.  

Porter’s Perfection flesh and peel tannin and polyphenols 

 ‘Porter’s Perfection’ peel and flesh tannin concentration were analyzed at 27, 81, and 160 

DAFB. At 27 DAFB, the flesh had an average tannin concentration of 55 mg·g-1 of dry weight 

(DW) tissue which reduced to 24 and then to 13 mg·g-1 DW at 81 and 160 DAFB, respectively 

(Figure 3.4A). The peel had a much less average tannin concentration of 22 mg·g-1 DW at 27 

DAFB that increased to 29 mg·g-1 DW and then reduced to 12 mg·g-1 DW at 81 and 160 DAFB, 

respectively (Figure 3.4B). At 81 DAFB, there was a significant increase in average tannin 

concentrations of the low crop density treatment to 108% and 50% over the UTC treatments for 

the flesh (P = 0.0209) and peel (P = 0.0032) tissue, respectively. While the low treatments had 



 

91 

 

greater concentrations of tannin in comparison to the UTC at 27 and 160 DAFB, the differences 

were not statistically significant. Although flesh tannin concentration was twice that of the peel at 

the first sampling, by 160 DAFB, both the flesh and peel had similar concentrations. 

There were no significant differences in the mean degree of polymerization (mDP) and the 

average molecular weight of tannins among treatments at any time points (Supplementary Figure 

3.3). The peel tissue maintained a greater level of polymerization throughout the growth season 

decreasing from 11 mDP at 27 DAFB to 5 mDP at 160 DAFB. The flesh tissue average decreased 

from 9 mDP at 27 DAFB to 4 mDP at 160 DAFB (Supplementary Figure 3.3). The peel had greater 

average molecular weight values in comparison to the flesh tissue throughout the experiment. 

There was a decrease in molecular weight by ~55% from 27 DAFB to 160 DAFB in both peel and 

flesh tissue (Supplementary Figure 3.3).  

 Polyphenol compounds were measured at six time points for both peel and flesh tissue of 

‘Porter’s Perfection’ at approximately four-week intervals from the implementation of the 

treatments until harvest (Figure 3.4C, D). On a dry weight basis, both the flesh and peel tissue had 

the greatest concentrations at 27 DAFB with an average of 34 mg·g-1 DW and 27 mg·g-1 DW 

respectively. There was a drastic reduction of measured polyphenol concentration from 27 DAFB 

to 55 DAFB for both peel and flesh tissue which was maintained (± 2 mg·g-1 DW) until 160 DAFB. 

In the peel tissue there were no significant differences among treatments. The flesh tissue 

maintained a significantly greater concentration in the low crop density vs UTC treatments from a 

57% increase at 55 DAFB (P < 0.0001) to a 41% increase in concentration at 160 DAFB (P = 

0.0460). 

 Twelve polyphenol compounds were quantified in the flesh and seventeen were quantified 

in the peel tissue of ‘Porter’s Perfection’ (Figure 3.4E, F). Procyanidins A1, A2, and C1 were 



 

92 

 

identified in the flesh tissue, but were not present in juice samples. Flavonols such as quercetin 

glycosides were absent in the flesh tissue. In the peel tissue, procyanidin A1 and C1 were 

identified, but were not present in the juice samples. In both the flesh and peel tissue, there was 

greater concentration of phenolic acids and phlorizin at 27 DAFB and then a gradual decrease in 

concentration until 160 DAFB. A similar trend was found in peel tissue flavonols, such as 

quercitrin and quercetin-3-rutinoside. Peel tissue proanthocyanidins accumulated at 55 and 81 

DAFB and then decreased until 160 DAFB. The flesh tissue proanthocyanidins accumulated 

gradually and reached their peak concentration at the pre-harvest (139 DAFB) and harvest (160 

DAFB) stages.   

 



 

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* * 

A 

C 

E 

F 

B 

D 



 

94 

 

Figure 3.4 Concentration of different polyphenol compounds in flesh and peel tissue of the cultivar 

‘Porter’s Perfection’/‘G.11’ subject to four crop density treatments in 2021 - low, medium, high 

crop density treatments thinned to 5, 10, 15 fruit/cm2 TCSA, and an UnThinned Control (UTC). 

Estimated marginal means of tannin concentration for A) flesh and B) peel tissue, and total 

measured polyphenol monomer content for C) flesh and D) peel tissue is presented. Means 

comparison within each time point followed by the same lowercase letter are not significantly 

different based on Tukey’s HSD means comparison at α = 0.05. E, F – A heatmap indicating the 

relative accumulation patterns of individual polyphenol monomers or oligomers in the flesh and 

peel tissue respectively. Abbreviations: Cyanidine-3-galactoside (Cyanidinine-3-galact), 

Caffeoylquinic Acid (Caffeoylq Acid). 

 

RNA sequencing and differential gene expression 

RNA sequencing was used to identify and quantify differential expressed genes (DEGs) 

between the lowest (5 fruit/TCSA) and the greatest (UTC) crop density treatments for ‘Porter’s 

Perfection’. Both flesh and peel tissue were analyzed at 27, 81, and 160 DAFB, which represents 

the early, mid, and late stages of fruit development. Cumulatively, there were 2,207 DEGs in the 

flesh and 1,285 DEGs in the peel tissue across the three sampling time points between the UTC 

and low crop density treatments (Figure 3.5, Supplementary Table 3.1). There were 137 (74 

downregulated and 63 upregulated) and 69 (36 downregulated and 33 upregulated) DEGs at 27 

DAFB for the flesh and peel tissue, respectively. The greatest number of DEGs were found at 81 

DAFB with 1424 (509 downregulated, 915 upregulated) and 924 (420 downregulated, 504 

upregulated) DEGs in the flesh and peel, respectively. At 160 DAFB, the flesh and peel tissue had 

834 (476 downregulated and 358 upregulated) and 351 (137 downregulated and 214 upregulated) 

DEGs, respectively. There was more overlap of shared DEGs between 81 and 160 DAFB than 

between 27 and 81 DAFB for both flesh and peel tissues. There were only 20 and 8 genes shared 

between 27 and 81 DAFB in the flesh and peel respectively, whereas there were 160 and 51 genes 

shared between 81 and 160 DAFB in the flesh and peel respectively. The flesh tissue had three 



 

95 

 

genes that were differentially expressed throughout the three stages of measurement, whereas the 

peel did not have any shared genes throughout the three stages. 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3.5 Venn diagram representing the differentially expressed genes resulting from 

comparing two treatments - UnThinned Control (UTC) versus low crop density (5 fruit/ cm2 

TCSA) treatments at different time points on A) flesh and B) peel tissue of ‘Porter’s 

Perfection’/‘G.11’ in 2021. Days after full bloom (DAFB).   

 

Gene enrichment analyses 

  Enriched gene ontology (GO) terms (P < 0.05) are presented in Figures 3.6 and 3.7 for 

flesh and peel, respectively, and a detailed list is provided in Supplementary Table 2. There were 

more enriched GO terms that were downregulated for the low crop density treatment for both flesh 

and peel tissue. Also, the most active stage where there was maximum DEGs, and enriched GO 

terms was at 81 DAFB for both flesh and peel tissue (Figures 3.6, 3.7). In flesh tissue, there were 

a total of 16 GO terms that were upregulated, whereas there were 98 GO terms that were 

A B 



 

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downregulated. There were 19 downregulated GO terms shared between 81 DAFB and 160 DAFB 

in flesh (Figures 3.6A, B). Among the GO terms upregulated at 81 DAFB in flesh, biological 

processes such as “L-phenylalanine metabolic process”, “flavonoid biosynthetic process”, 

“flavonoid metabolic process”, and molecular functions such as “naringenin 3-dioxygenase 

activity” are directly involved in the phenylpropanoid pathway synthesis and regulation (Figure 

3.6C). Among the upregulated GO terms at 81 DAFB in flesh, “flavonoid biosynthetic and 

metabolic processes (7 genes – biological processes)” had the greatest gene number within their 

respective GO terms (Figure 3.6C).  

Among the downregulated GO terms at 27 DAFB in flesh, there was a downregulation of 

stress response genes including those involved in salt stress, reactive oxygen species, osmotic 

stress, heat, and hydrogen peroxide in the low crop density treatment, indicating increased stress 

response for the UTC treatment (Figure 3.6D). Molecular functions such as “pyrophosphatase 

activity”, “hydrolase activity”, and “ATP hydrolase activity” had greater number of genes (10-12) 

within their GO terms than others (Figure 3.6D). Among the downregulated GO terms at 81 and 

160 DAFB in flesh, there were multiple shared GO terms relating to photosynthesis including, but 

not limited to, photosystems 1 and 2, thylakoid membrane, generation of precursor metabolites 

and energy, photosynthetic membranes (Figure 3.6E, F, Supplementary Table 3.2). At 81 DAFB, 

there was also a downregulation of the carbohydrate metabolic process and RUBISCO (ribulose-

bisphosphate carboxylase activity). At 160 DAFB, the GO terms had a marked downregulation of 

hormone function including, “response to auxin”. 

 

 



 

97 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

A B 

C    D    

 

E    F    

27 DAFB 

81 DAFB 



 

98 

 

Figure 3.6 Enriched (P value < 0.05) gene ontology (GO) terms identified using Blast2GO 

(Conesa et al., 2005) associated with the flesh tissue of UnThinned Control (UTC) versus low crop 

density (5 fruits/cm2 TCSA) treatments at 27, 81, and 160 DAFB in ‘Porter’s Perfection.’ Venn 

diagrams outline the overlapping and distinct enriched GO terms at each developmental stage that 

were A) upregulated and B) downregulated. Select enriched GO terms upregulated at 27 and 81 

DAFB are presented in C. There were minimal enriched GO terms that were upregulated at 160 

DAFB. Select enriched GO terms downregulated at 27 DAFB, 81 DAFB, and 160 DAFB are 

presented in D, E, and F respectively. Rich factor percent is the ratio of the number of differentially 

expressed genes annotated in a pathway to the number of all genes annotated in that pathway. The 

q-value is defined as the minimum false discovery rate at which an observed score is deemed 

significant. Abbreviations: erythrose 4-phosphate/phosphoenolpyruvate family amino acid 

metabolic process (E-4-P/PEP). 

 

The peel tissue had two unregulated GO terms at 27 and 81 DAFB and 17 upregulated GO 

terms at 160 DAFB (Figure 3.7A). There were 28 and 34 downregulated genes at 27 and 82 DAFB, 

with 10 GO terms shared between the sample dates. At 160 DAFB, there was only one 

downregulated GO term (Figure 3.7B). Flavonol synthase activity and GO terms related to zinc 

ion transport were upregulated at 160 DAFB in peel tissue (Figure 3.7C). Among the 

downregulated GO terms at 27 and 81 DAFB in peel, response to different stresses such as salts, 

osmotic, hydrogen peroxide, and abiotic stimuli featured prominently in both the growth stages 

(Figure 3.7D, E). Interestingly, the “carbohydrate derivative binding” term was also downregulated 

at both 27 and 81 DAFB (Figure 3.7D, E). Additionally, there were several GO terms involved in 

molecular “binding”, such as “nucleotide binding”, “nucleoside phosphate binding”, “ATP 

binding”, “adenyl nucleotide binding”, etc. that were downregulated at 27 DAFB in peel tissue, 

and many of these GO terms have 12-16 individual genes within their GO term (Figure 3.7D). At 

81 DAFB, there was a downregulation of the ‘carbohydrate biosynthetic process’ as well as ‘UDP-

glycosyltransferase’, both GO terms being very essential in phenylpropanoid pathway (Figure 

3.7E). 



 

99 

 

  

 

  

 

 

 

 

 

 

 

 

 

 

 

Figure 3.7 Enriched (P value < 0.05) gene ontology (GO) terms identified using Blast2GO 

(Conesa et al., 2005) associated with the peel tissue of UnThinned Control (UTC) versus low crop 

density (5 fruits/cm2 TCSA) treatments at 27, 81, and 160 DAFB in ‘Porter’s Perfection.’ Venn 

diagrams outline the overlapping and distinct enriched GO terms at each developmental stage that 

were A) upregulated and B) downregulated. Select enriched GO terms upregulated at 27, 81, and 

160 DAFB are presented in C. Select enriched GO terms downregulated at 27 DAFB and 81 DAFB 

are presented in D and E, respectively. There were minimal enriched GO terms that were 

downregulated at 160 DAFB. Rich factor percent is the ratio of the number of differentially 

expressed genes annotated in a pathway to the number of all genes annotated in that pathway. The 

q-value is defined as the minimum false discovery rate at which an observed score is deemed 

significant. 

C    
27 DAFB 

81 DAFB 

160 DAFB 

 A     

 B    

D    

 

E    



 

100 

 

Phenylpropanoid pathway genes 

The phenylpropanoid pathway genes presented in Figure 3.8A were upregulated at 81 

DAFB in the low crop density treatments. A threshold of at least a 2-fold down or upregulation 

with a P value < 0.05 was used to select genes for further scrutiny. There was no significant 

upregulation at 27 DAFB and 160 DAFB for most of the genes. In general, the flesh tissue had 

greater upregulation of phenylpropanoid pathway genes at 81 DAFB than peel tissue. Even among 

genes that had a 2-fold upregulation, some were up-regulated to a significantly greater degree, 

which are highlighted below. The 4-coumarate: CoA ligase gene MD01G1236300 exhibited a 6.25-

fold increase in expression in flesh tissue at 81 DAFB (Figure 3.8A). Similarly, two of the chalcone 

synthase genes MD13G1285100 and MD04G1003000, and a flavonol synthase gene 

MD08G1168600 exhibited a 4-fold upregulation in the flesh tissue at 81 DAFB. Anthocyanidin 

synthase (MD06G1071600) and anthocyanidin reductase (MD05G1335600) are the penultimate 

and the final step in the production of epicatechin and both genes exhibited significant upregulation 

(4.75 and 3.85-fold, respectively) at 81 DAFB in flesh tissue (Figure 3.8A). Concentrations of the 

procyanidin monomers and dimers were greater in the low crop density treatment than in the UTC 

(Figure 3.8B). Except for procyanidin B1, all the compounds had a significant increase at 81 DAFB 

mirroring the increase in the expression of key genes at 81 DAFB (Figure 3.8B). The differences 

in proanthocyanidin concentrations between the low crop density and the UTC treatments were 

sustained through to 160 DAFB for all the procyanidins except for catechin, procyanidin B1, and 

procyanidin A1 in peel tissue (Figure 3.8B). 

 

 



 

101 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

B 

A 



 

102 

 

Figure 3.8 A) Differentially expressed genes (DEG’s) in the phenylpropanoid pathway comparing 

the unthinned control (UTC) to the low crop density treatment for flesh and peel tissue in ‘Porter’s 

Perfection’. Only DEG’s with at least a two-fold difference in expression and adjusted P value < 

0.05 in any one of the developmental stages is shown above. B) Estimated marginal means of 

individual polyphenol monomers comparing the unthinned control (UTC) and the low crop density 

treatment for flesh and peel tissue of the cultivar ‘Porter’s Perfection’. Means within each time 

point followed by different lowercase letter are significantly different based on the Tukey’s HSD 

means comparison at α = 0.05. Abbreviations: phenyl ammonia lyase (PAL), cinnamate-4-

hydroxymate (C4H), 4-coumarate: coenzyme A ligase (4CL), chalcone synthase (CHS), chalcone 

Isomerase (CHI), Flavanone 3-hydroxylase (F3H), dihydroflavonol 4-reductase (DFR), 

anthocyanidin reductase (ANS), UDP-glucose flavonoid 3-O-glucosyl transferase (UFGT), 

flavonol synthase (FLS), Leucoanthocyanidin reductase (LAR1/2), glycosyl transferases (GT1/2). 

 

Transcription factor DEGs 

 The ethylene responsive factors (ERF) have been found to be involved in regulation of 

various primary and secondary metabolism pathways, including the phenylpropanoid pathway. 

There were a total of 10 and 4 ERF DEGs in the flesh and peel tissue respectively. At 27 DAFB, 

there were no differences in expression between the ERF genes. At 81 and 160 DAFB, the ERFs 

were downregulated in the low crop density treatment with a notable exception in ‘AP2 like ERF’ 

(MD01G1113400), which witnessed a significant upregulation at 81 DAFB for both flesh (7-fold) 

and peel (3-fold).  

  The MYB-bHLH-WD40 complex has previously been found to control the production of 

different polyphenol compounds including proanthocyanidins. There were twelve and nine MYB 

DEGs identified in the flesh and peel tissues, respectively (Figure 3.9). There were fourteen bHLH 

DEGs in the flesh tissue and four in the peel tissue. There was only one WD-40 DEG identified in 

the peel tissue (Figure 3.9). The flesh tissue in general had more MYB-bHLH-WD40 DEG’s that 

the peel tissue. 



 

103 

 

 In the flesh tissue, there were no MYB DEGs at 27 DAFB. MYB DEGs at 160 DAFB were 

also minimal with only two downregulated MYB TFs in the low crop density treatment 

(MD16G1228600 and MD17G1050900). The majority of the MYB TFs were differentially 

expressed at 81 DAFB. Four MYB genes were upregulated (MD07G1153200, MD12G1012700, 

MD14G1234500, and MD14G1234600), while three were downregulated (MD04G1092400, 

MD14G1172900, and MD17G1073900) in the low crop density treatment at 81 DAFB. The flesh 

bHLH TFs had a similar trend as their MYB counterparts. While there was no effect of the 

treatments on MYB DEGs at 27 DAFB, there were six bHLH genes downregulated and four 

upregulated at 81 DAFB, whereas there were four genes that were upregulated and three that were 

downregulated at 160 DAFB (Figure 3.9). Notably, there was one bHLH gene (MD14G1227400) 

that had an ~3.5 downregulation at 81 DAFB and a ~17-fold upregulation at 160 DAFB. 

In the peel tissue, there were no MYB-bHLH-WD40 DEGs at 27 DAFB, whereas there 

was a general trend of upregulation at 81 and 160 DAFB. Notably, there was one MYB TF 

(MD09G1097700) that had a 5-fold upregulation at 81 DAFB. One bHLH TF (MD15G1384300) 

had a 3-fold upregulation at both 81 and 160 DAFB (Figure 3.9).  

 

 

 

 

 

 



 

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Figure 3.9 The differentially expressed genes in the ethylene-responsive transcription factors 

(ERF) and MYB-bHLH-WD40 complex signaling pathway comparing the unthinned control to 

the low crop density treatment for flesh and peel tissue in ‘Porter’s Perfection’ at 27, 81, and 160 

days after full bloom (DAFB). Only differentially expressed genes with at least a two-fold 

difference in expression and a P < 0.05 in any one of the developmental stages is shown above. 

Abbreviations: myeloblastosis viral oncogene homolog (MYB), basic Helix Loop Helix (bHLH), 

beta-transducin repeat (WD).  

 

Discussion  

Polyphenol concentrations are negatively correlated with crop density 

At harvest analysis of juice TPC illustrated a negative correlation of crop density with TPC 

for both cultivars, albeit significant only for ‘Porter’s Perfection’. This correlation is corroborated 

with evidence from Karl and Peck (2022), who observed that lower crop densities in one season 

in the French bittersweet apple ‘Medaille d’Or’ increased polyphenol concentrations. Zakalik et 



 

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al. (2023) also found over a 3-year study that increasing crop density decreased TPC for all the 

seven cider apple cultivars studied, including ‘Binet Rouge’. Our research also found that most 

polyphenols (individual polyphenol compounds and tannins) are produced in the initial cell-

division stage of fruit growth until about 30 DAFB and then reduce in concentrations until harvest. 

This reduction was also observed in other studies (Guyot et al. 2001; Guyot et al. 2003; Renard et 

al. 2007; Plotkowski and Cline 2021a; Karl and Peck 2022).  

Carbohydrate availability has a role to play in accumulation of polyphenols in cider apple 

We hypothesize that carbohydrate availability during the cell division phase of fruit growth 

(up to 30 DAFB) is the primary controlling factor of polyphenol synthesis in apples. Primary and 

secondary metabolism is dependent on carbohydrate availability during the crucial cell division 

phase from 1-5 WAFB where carbohydrates are necessary for cell division, growth, and function 

(Lakso et al., 1989). In this period of cell division in fruit, existing carbohydrate reserves 

preferentially assist with shoot growth and extension rather than invest in fruit sinks, hence the 

fruit are dependent on localized carbohydrates produced from spur leaves adjacent to the fruit 

(Bepete and Lakso, 1998). Hence, an increased crop density would reduce primary and secondary 

metabolite production and accumulation in individual fruit due to non-availability of enough 

carbohydrate to satisfy sink demands. Similar to our hypothesis, Anthony et al. (2023) and Bell 

and Henschke (2005) identified that low crop density in peach and wine grape, respectively, 

enhanced carbon availability early in the season to ensure consistent synthesis of flavonoids, such 

as catechin and epicatechin, and the fruit maintained those high levels of polyphenols until harvest 

in comparison to the UTC treatments.  

 



 

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RNA Sequencing, gene enrichment analysis and differential gene expression 

The low crop density treatment was compared with UTC at three timepoints: 27, 81, and 

160 DAFB for ‘Porter’s Perfection’ peel and flesh tissue. The 27 DAFB measurements effectively 

served as controls because samples were taken only a few days after implementation of the crop 

density treatments and as expected, there were not many significant differences. The 81 DAFB 

timepoint had the most differences in gene expression with 1,241 DEGs in the flesh and 865 DEGs 

in the peel tissue. In the gene enrichment analyses, there was an upregulation of the flavonoid 

metabolism and flavonoid biosynthesis pathway in the low crop density treatment in comparison 

to the UTC. This was further probed by examining key polyphenol pathway genes in the cider 

cultivar ‘Porter’s Perfection’, which showed a strong effect of reduced crop density on 

upregulation of polyphenol pathway genes, especially at 81 DAFB. This result corroborates with 

previous research illustrating the upregulation of the polyphenol pathway genes, which reduces 

the production of key polyphenols, including proanthocyanidins (Henry-Kirk et al., 2012; Verdu 

et al., 2014; Liu et al., 2016). Specifically, in the low crop density treatment, there was an 

upregulation in the gene encoding anthocyanidin reductase (ANR), which catalyzes the synthesis 

of epicatechin (Takos et al., 2006a; Henry-Kirk et al., 2012; McClure et al., 2019). Furthermore, 

there was also an upregulation of the L-phenylalanine metabolic process in the low crop density 

treatment, which is a key substrate needed for phenylalanine ammonia lyase to biosynthesize 

phenylpropanoids (Geng et al., 2020; Fanyuk et al., 2022).  

At 81 DAFB, low crop density also decreased carbohydrate production via the 

downregulation of photosynthesis, photosystems, and ribulose-bisphosphate carboxylase 

(Rubisco) activity, which is a key enzyme involved in regulating carbon assimilation rates. Lower 

crop densities have been shown to reduce sink demand and leaf carbon assimilation for apple trees 



 

107 

 

(Yang et al., 2021), thus downregulating photosynthesis and carbohydrate accumulation. We also 

observed lower levels of glyceraldehyde-3-phosphate dehydrogenase, which is involved in the first 

step of converting glyceraldehyde-3-phosphate (G-3-P) to ribulose bisphosphate (RuBP), another 

substrate for carbon fixation (Sharkey, 1985). In other words, once carbon sufficiency is reached 

during the initial phase of fruit growth in the low crop density treatment, there is a downregulation 

of carbohydrate production processes in low crop density trees due to lower sink demand, thus, 

there is an effective adjustment of carbohydrate status based on crop density in order to meet the 

sink demand (Yang et al., 2021).   

Pre-harvest and harvest characteristics 

 At harvest, the yield of both ‘Porter’s Perfection’ and ‘Binet Rouge’ had a strong positive 

linear correlation with increase in crop density as observed by Zakalik et al. (2023) and Plotkowski 

and Cline (2021b). While this is generally the case for the ‘on year’ yields, the following year’s 

return bloom had a strong negative correlation with increase in crop density. This tendency of 

reduced return bloom with increasing crop density in the previous year is well established in the 

literature (Pellerin et al., 2011, Robinson and Watkins, 2003). In fact, trees did not have a single 

return bloom cluster and were pushed into complete bienniality at any crop density equal to or 

greater than 28.4 fruit/cm2 TCSA for ‘Porter’s Perfection’ and 27.8 fruit/cm2 TCSA for ‘Binet 

Rouge’. Zakalik et al. (2023) observed over three years that a crop density greater than 21.2 

fruit/cm2 did not have return bloom the next year for ‘Binet Rouge’. The variation in the crop 

density versus return bloom observed between studies could be attributed to variability in weather 

conditions, tree spacing and training system, pre-experiment crop density, and/or length of the 

experiment. Even if the ‘on-year’ yields are greater with increased crop density, cumulative yields 

over multiple years have shown that increase in crop density resulted in decreased yields in the 



 

108 

 

long-term (Plotkowski and Cline 2021b; Zakalik et al. 2023). Since our experiment was only 

conducted for one year to understand the molecular underpinnings of polyphenol development in 

cider apples, we are not able to make a judgement on cumulative yields as more field seasons are 

necessary to reach a meaningful conclusion.  

Crop density significantly influences polyphenol production in only high polyphenol cultivars 

Literature on the effects of crop density on polyphenol development in apples has been 

mixed. While Awad et al. (2001), Unuk et al. (2006), and Peck at al. (2016) found minimal effects 

of crop density on polyphenol development in apples, Zakalik et al. (2023), and Karl and Peck 

(2022) found that increased crop densities significantly reduced polyphenol concentrations in 

many of the cultivars they studied. The main difference between the two sets of studies is the TPC 

of their cultivars. The studies that used low polyphenol fresh-market or juice cultivars found 

minimal to no effects of crop density on polyphenol content whereas high tannin cider cultivars 

usually responded to increased crop densities with lower TPC. A case in point was the cultivar 

‘Binet Rouge’, where we did not find treatment differences in TPC due to the low average TPC of 

1.7 g·L-1. However, Zakalik et al. (2023) observed that ‘Binet Rouge’ had a ~40% greater TPC 

from similar crop densities (TPC of 2.5 g·L-1 for crop densities of 6 and 9 fruits/cm2 TCSA in 

comparison to our TPC of 1.78 g·L-1 for crop densities of 5 and 10 fruits/cm2 TCSA). That 

difference in TPC could explain the difference in trends observed in our respective studies and 

crop density being found to have a significant reduction in total polyphenols in Zakalik’s study but 

not in our study. This variation in TPC could be due to their three years of field data, where more 

data points were able to provide enough strength to determine significant differences between crop 

density and TPC.  



 

109 

 

There may be different regulatory mechanisms of polyphenol production in low and high 

polyphenol cultivars. There seems to be a cultivar-dependent standard level of polyphenol 

production in apples irrespective of crop density until a certain threshold, above which a source-

sink relationship comes into play to regulate polyphenol development according to crop density. 

Functional research into the genes and TF’s controlling polyphenol production is necessary to 

elucidate the mechanisms of action of polyphenol regulation in cider apples.  

Unique accumulation trends of phloridzin and proanthocyanidins in flesh and peel tissue 

Due to significant treatment differences in the cultivar ‘Porter’s Perfection’, we analyzed 

the trends of polyphenol accumulation throughout the growing season in both flesh and peel tissue. 

Our research supports previous reports that most polyphenols (individual polyphenol compounds 

and tannins) are produced in the initial cell-division stage of fruit growth until about 30 DAFB, 

however, there are some polyphenol compounds that do not follow this accumulation pattern, such 

as anthocyanins which are synthesized in large quantities in the peel as the fruit ripens (Takos et 

al., 2006b). Additionally, there was an increase in accumulation of procyanidin monomers and 

oligomers throughout the growing season and at pre-harvest (139 DAFB) and harvest (160 DAFB) 

stages for ‘Porter’s Perfection’ flesh tissue. This trend was also observed by Renard et al. (2007) 

in the cultivar ‘Kermerrien’. The accumulation in proanthocyanidin content at pre-harvest and 

harvest stages could be due to the breakdown of larger tannin compounds that decreased in 

concentration from the first date of measurement until harvest (Renard et al., 2007).  

At harvest, most individual polyphenol compounds measured from juice through HPLC 

indicated a significant negative correlation with crop density for ‘Porter’s Perfection’, except for 

phlorizin which showed a significant but weak increase with increase in crop density for ‘Porter’s 

Perfection’  



 

110 

 

Takos et al. (2006), Renard et al. (2007), and Henry-Kirk et al. (2012) reported catechin, 

epicatechin, and the procyanidins B1, B2, C1 in flesh and peel tissue. In this study. procyanidin 

A1 (epicatechin-catechin) was found in both flesh and peel, as well as procyanidin A2 (dimeric 

epicatechin) in flesh tissue. In flesh tissue, both procyanidin A1 and A2 were present only at 

harvest. Strong radical scavenging activity of procyanidin B1 and B2 may be converting to 

procyanidin A1 and A2 after utilizing the C-2 hydrogen unit in addition to the o-dihydroxyl 

structure to neutralize the free radical (Kondo et al. 2000). Our research also confirmed previous 

reports on polyphenol present only in the peel such as quercetin glycosides and anthocyanins such 

as cyanidin glycosides (Takos et al., 2006b; Espley et al., 2007; Ban et al., 2007). Other compounds 

such as phenolic acids, proanthocyanidins, and dihydrochalcones are present in both peel and flesh, 

albeit in different concentrations (Renard et al., 2007; Henry-Kirk et al., 2012).  

Tannin accumulation trends in cider apples 

The flesh had greater tannin concentrations than the peel with maximum accumulation at 

27 DAFB and slowly decreased in concentration until harvest (160 DAFB). Similarly, mDP 

decreased from ~9 to 4 units at harvest for both peel and flesh tissue, with peel tissue exhibiting 

slightly greater mDP values throughout. Similar trends were observed for the cultivar ‘Kermerrien’ 

by Renard et al. (2007) and in multiple grape cultivars where proanthocyanidin content decreased 

in concentration as well as an mDP to about 50-150 subunits post veraison (Downey et al., 2003; 

Obreque-Slier et al., 2010). This mDP trend is not universal and is at odds with the cultivar 

‘Avrolles’ analyzed by Renard et al. (2007), where proanthocyanidin monomers polymerized and 

increased in mDP for the remainder of the growing season. Cultivar differences seem to play a 

major role in mDP of proanthocyanidins and could explain the increase in mDP in the cultivar 

‘Avrolles’ (50-80 subunits) (Guyot et al., 2003). The cultivar differences are influenced by a strong 



 

111 

 

genetic component linked to most polyphenol compounds, which suggests a considerable part of 

genetic variability in the expression of these traits (Verdu et al., 2014). More research on 

identifying genes and markers responsible for high polyphenol content and degree of 

polymerization is necessary to understand the regulatory mechanisms of tannin production and 

polymerization in cider apples. 

Tannins breakdown into proanthocyanidin monomers and oligomers during the growing season 

The tannin concentration was at its maximum levels during our first sampling at 27 DAFB 

which was also observed by other researchers (Renard et al., 2007; Henry-Kirk et al., 2012). At 

the mid-point of 81 DAFB, we were able to see a significantly greater tannin concentration in the 

low crop density versus the unthinned control, whereas there were no treatment differences at 

harvest. However, individual procyanidin monomers and oligomers followed a different trend of 

gradual accumulation throughout the growing season, especially in the flesh tissue, with a peak 

accumulation at the pre-harvest and harvest stages. At 81 DAFB, there was a marked upregulation 

of polyphenol pathway genes for the low crop density treatment which corresponded with an 

increase in procyanidin concentration; however, the differential gene expression tapered off at 160 

DAFB, while the differences in procyanidin content remained at harvest. A breakdown of tannins 

into procyanidin monomers and oligomers at pre-harvest and harvest stages might becausing 

greater procyanidin content in low crop density versus the UTC treatment at harvest. Future studies 

should use carbon isotope or green fluorescent protein labelling of procyanidin monomers catechin 

and epicatechin to understand their polymerization and breakdown patterns throughout the 

growing season.   

 



 

112 

 

Transcription factors possibly involved in regulation of the phenylpropanoid pathway 

Over the past decade, there have been multiple studies examining the effect of 

transcriptional activators and repressors on different genes in the phenylpropanoid pathway. The 

crop density stress applied in this experiment would be helpful to understand the molecular 

regulation of different aspects of the phenylpropanoid pathway. Specifically, the MYB-bHLH-

WD40 and ERF transcription factors have been found to regulate proanthocyanidin production 

(Xu et al., 2015; Zhou et al., 2015; Wang et al., 2017; An et al., 2018b; Zhang et al., 2018; Sun et 

al., 2019; Li et al., 2020; Geng et al., 2020; An et al., 2021). Since the variation in phenylpropanoid 

levels among treatments were only found in proanthocyanidins, there is a high likelihood that the 

differentially expressed transcription factors are involved in regulation of proanthocyanidin 

metabolism. We identified ten differentially expressed ERF genes in the flesh and four in the peel. 

The vast majority of these genes were downregulated in the low crop density treatment except for 

the MdWIN/AP2 and MdRAP2-7 genes. Specifically, MdRAV1 was highly downregulated in the 

low crop density treatment indicating a less suppressive effect on proanthocyanidin synthesis as 

compared to the UTC treatment. MdRAV1 was found to bind to the promoter of MdANR2 

(anthocyanidin reductase), inhibiting its activity (Li et al. 2020). Other studies have indicated that 

MdERF1A, 1B, and MdERF23 also play a role in in procyanidin regulation (Zhang et al., 2018; 

Li et al., 2020). We have identified eight and three novel ERF genes in the flesh and peel 

respectively that could potentially be involved in proanthocyanidin synthesis. We also identified 

26 MYB and bHLH DEG’s in the flesh and 14 MYB, bHLH, and WD40 DEG’s in the peel that 

could potentially be involved in proanthocyanidin synthesis. Many of these MYB and bHLH genes 

can bind to the promoters of key genes in the phenylpropanoid pathway such as those encoding 

phenyl ammonia lyase, chalcone synthase, flavonol synthase, leucoanthocyanidin reductase, and 



 

113 

 

anthocyanidin reductase. They can act as enhancers or suppressors for these key phenylpropanoid 

pathway genes. More research into these TFs will uncover new regulatory mechanisms for 

proanthocyanidin production in cider apples. 

Conclusion 

Crop density had a measurable significance on all fruit and juice quality variables. Reduced 

crop density enhanced fruit juice characteristics such as SSC, TA, and TPC. TPC was enhanced 

not due to dilution as the fruit cells expanded, but due to a reduction in yield of polyphenols on a 

dry weight basis. Early season carbohydrate availability during the cell division phase (1-30 

DAFB) of fruit growth is hypothesized to be the primary driver of polyphenol production in cider 

apples. Crop densities at or greater than 15 fruits/cm2 TCSA resulted in markedly reduced or no 

return bloom and hence, we support a crop density of 9-10 fruits/cm2 TCSA as suggested by 

Zakalik et al. (2023) to enhance yields and TPC, while maintaining enough return bloom for 

regular bearing. Further, this study provides an overview of the molecular regulation of 

carbohydrate and polyphenol metabolism. Reduced crop density enhanced the expression of 

secondary metabolite pathway genes, which resulted in an increase in polyphenols and specifically, 

proanthocyanidin building blocks such as epicatechin through upregulation of anthocyanidin 

reductase activity. This study found tannin polymerization and potential breakdown patterns in 

cider apples that should be validated with carbon isotope studies. Also, key TF’s that could be 

involved in regulating proanthocyanidin production in cider apples have been identified and 

further functional analysis on these genes would help to uncover regulatory mechanisms of 

proanthocyanidin production in cider apples.  

 

 



 

114 

 

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Chapter 4 

Early season tree shading decreases phenolic acids and quercetin glycosides, but not 

proanthocyanidin monomer and oligomer concentrations in cider apples at harvest  

Abstract 

Polyphenols are a much sought after component that contributes to fermented (hard) apple 

cider flavor, aroma, color, and microbial stability. However, polyphenol biosynthesis is not well 

understood in cider apples. In order to understand the factors involved in polyphenol biosynthesis, 

apple trees were wholly or partially covered with shade cloth to limit photosynthesis. In 2020, 3-

year-old ‘Porters Perfection’ trees were subject to the following treatments from 1 to 5 weeks after 

full boom (WAFB): a no-treatment control, 40 tree shade (TS), 80TS, 40 fruit shade (FS), and 

80FS. The number in each treatment refers to the percentage of photosynthetically active radiation 

that was blocked by the shade cloth. In 2021, the experiment was implemented on ‘Porter’s 

Perfection’ and ‘Binet Rouge’, but the treatments were modified to include a non-treatment 

control, 30TS, 60TS, 30FS, and 60FS. At harvest in 2020, ‘Porter’s Perfection’ apples from the 

control had 25-30% greater mass, circumference, and SSC than the four shade treatments. 

However, these effects were not consistent across years. No differences in harvest and juice quality 

parameters were found for either cultivar in 2021. In 2021, cumulative HPLC juice polyphenols 

from the 60TS treatment had a 15 and 27% less concentration than the control for ‘Porter’s 

Perfection’ and ‘Binet Rouge’, respectively. Phenolic acids and quercetin glycosides accounted for 

most of the difference. Although most polyphenols, including procyanidins, from the TS 

treatments had reduced concentrations in comparison to the unshaded controls during the shading 

period of 1-5 WAFB, they did not differ from the control at harvest. The FS treatments were not 

significantly different from the control in any of the parameters measured in 2021. This research 



 

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indicates that early season shading of apple trees resulted in reduced phenolic acids and quercetin 

glycosides at harvest, but did not influence proanthocyanidin monomer and oligomer 

concentrations. Moreover, direct sunlight on fruit seemed to have minimal effects on polyphenol 

development except for influencing peel anthocyanin synthesis. 

Introduction 

 Hard cider production and consumption has witnessed a worldwide increase over the past 

decade. In 2021, hard cider generated over $553 million in revenue from sales in the US, with the 

market expected to grow by 3.5% between 2022 and 2027 (NielsonIQ, 2022). Most cider is 

produced from imported apple concentrate and lower-quality apples, leading to a high demand for 

tannic apples in the United States (Zakalik and Peck, 2023). This demand has resulted in a premium 

price for high tannin apples, encouraging the interest in growing these cultivars domestically 

(Pashow, 2018; Miles et al., 2020). Growing high tannin cider cultivars poses several horticultural 

challenges, including biennial bearing (Zakalik et al., 2023), pre-harvest fruit drop (Miles et al., 

2020), fluctuating proanthocyanidin/tannin concentrations of up to 50% from year-to-year within 

an orchard (Lea unpublished data) and among different orchard locations (Alexander et al., 2016).  

Apple polyphenols are an important class of secondary metabolites that provide health 

benefits via antioxidant, as well as organoleptic, color, and microbial stability properties to hard 

cider. While all apples contain polyphenols, many cider apples have greater concentrations of these 

compounds (Sanoner et al., 1999; Guyot et al., 2003; Napolitano et al., 2004; Thompson-Witrick 

et al., 2014). Polyphenols are grouped into five categories–dihydrochalcones (e.g. phloridzin), 

phenolic acids (e.g., chlorogenic acid and hydroxycinnamic acids), flavonols (e.g., quercetin 

glycosides), proanthocyanidins (e.g., catechin, epicatechin, and their oligomers and polymers), and 

anthocyanins (e.g., cyanidin glycosides) (McGhie et al., 2005; Henry-Kirk et al., 2012). Among 



 

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them, flavonols and anthocyanins are present exclusively in peel tissue in most apple cultivars 

except for pink and red fleshed apples where they are present in large quantities in the flesh tissue 

(Takos et al., 2006b; Renard et al., 2007; Henry-Kirk et al., 2012). Hydroxycinnamic acids such 

as phlorizin (phloretin glycoside) are highly abundant in apple flesh, peel, as well as in leaves and 

bark (Gutierrez et al., 2018) while phenolic acids such as chlorogenic acid are highly abundant in 

apple fruit flesh and peel (Renard et al., 2007).  

Proanthocyanidins, also known as condensed tannins, are abundant in cider apples and 

contribute bitterness and astringency to hard cider (Lea and Arnold, 1978; Miles et al., 2020). They 

are well-known antioxidant compounds (Tsao et al., 2005; Aron and Kennedy, 2008) and are 

enzymatically formed from catechin and epicatechin units through leucoanthocyanidin reductase 

(LAR) and anthocyanidin reductase (ANR) in the phenylpropanoid pathway (Henry-Kirk et al., 

2012; Liao et al., 2015). These monomers polymerize with initiators and elongators to form 

proanthocyanidins or condensed tannins (Lea and Arnold, 1978; Delage et al., 1991; Guyot et al., 

2003), accumulating in the plant's vacuole (Dixon, 2005). Proanthocyanidin oligomers and 

polymers with four sub-units or less contribute to bitterness, while larger tannin units create the 

astringent mouthfeel (Lea and Arnold, 1978; Vidal et al., 2003). Many cider apples contain 

significantly greater—up to 20-fold—proanthocyanidin concentrations compared to fresh-market 

and juice apples (Kahle et al., 2005; Renard et al., 2007). These highly tannic cider apple cultivars 

provide the required bitterness and astringency to hard cider while maintaining the necessary levels 

of sugar and acidity (Lea and Timberlake, 1974). Although the phenylpropanoid pathway genes 

and enzymes are well-studied in apple fruit (Henry-Kirk et al., 2012; Liao et al., 2015), the 

molecular controls and regulation of proanthocyanidin polymerization have not been fully 

elucidated. 



 

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 Previous studies have indicated that most polyphenols in apples are produced in the first 

5-6 weeks of fruit development, during the cell division phase of fruit growth and reduce in 

concentrations due to the dilution effect of cell expansion and fruit growth (Renard et al., 2007; 

Zhang et al., 2010; Henry-Kirk et al. 2012). Anthocyanins and flavonols biosynthesis is light 

sensitive, whereas proanthocyanidin biosynthesis has not been found to be directly affected by 

light exposure (Awad et al., 2000; Awad et al., 2001; Chen et al., 2012). 

There are several studies on factors influencing variation in tannin content from year-to-

year in cider apples. Bourvellec et al. (2015) found that cultivar and year-to-year variation rather 

than agricultural practices affected the secondary metabolism in three fresh-market apple cultivars. 

Proanthocyanidin concentrations and mean degree of polymerization at maturity was found to be 

predominantly cultivar dependent (Sanoner et al., 1999; Guyot et al., 2001; Guyot et al., 2002; 

Alonso-Salces et al., 2004) and did not vary with maturity, management practices, harvest year, or 

storage (Guyot et al., 2002; Guyot et al., 2003; Ewing et al., 2019). Crop density was found to 

negatively correlate with polyphenol concentrations of apple flesh and juice in both fresh-market 

and cider apple cultivars (Awad et al., 2001; Stopar et al., 2002; Guillermin et al., 2015; Zakalik 

et al., 2023).  

Sunlight has also been found to influence polyphenol production in cider apples. Karl and 

Peck (2022) found that reduced carbohydrate availability through early tree shading resulted in 

30% reduced polyphenol concentration at harvest in the cider apple bittersweet cultivar ‘Dabinett’. 

Karl and Peck (2022) also found that juice from fruit in the top of tree canopies had 33% greater 

total polyphenol concentrations than juice from fruit in the interior. Apples from more exposed 

areas of the tree canopy have been found to have greater proanthocyanidin concentrations (Awad 

et al., 2001; Feng et al., 2014). These differences in polyphenol concentrations could have been 



 

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influenced by localized differences in carbohydrate availability, which was modified by reducing 

sunlight exposure through tree shading. 

This research seeks to explain some of the variation in total polyphenol concentrations in 

cider apple cultivars observed in different years and locations. We hypothesize that limiting 

carbohydrate availability during the first five weeks of fruit development (cell division phase) 

through sunlight limiting tree shading will result in a decrease in total and individual polyphenols 

at harvest. We also compared full tree shading with just fruit shading to test the direct versus 

indirect effects of sunlight on polyphenols development. For this experiment, we used an English 

bittersharp cultivar ‘Porter’s Perfection’, and a French bittersweet cultivar ‘Binet Rouge’, both of 

which are recommended for production in NY state (Peck et al., 2021). We tracked the 

concentrations of individual polyphenol compounds from 1 WAFB until harvest to understand 

their accumulation trends in response to reduced sunlight availability through tree and fruit 

shading.  

Materials and Methods 

Trial Location and Experimental Design 

The experiment was conducted at the Cornell University Agricultural Experiment Station, 

Ithaca, NY (42.443880, -76.464919) on two high-tannin cider apple cultivars ‘Porter’s Perfection’ 

(2020 and 2021), and ‘Binet Rouge’ (2021). All trees for this experiment were planted in spring 

2018 in uniform rows with approximately 100 trees per row using a 3-wire trellis system and 

trained as a tall spindle of training system at 1.2 m between trees × 3.7 m between rows (~2,200 

trees/ha). ‘Geneva 11’ (‘G.11’) was used as a rootstock. 



 

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In 2020, there were five replicated blocks each of which contained five shade treatments 

that were implemented from 1-5 WAFB: 40% and 80% tree shade (TS), 40% and 80% fruit shade 

(FS), and a control which was left as is without any shade manipulation. The percentage of shade 

refers to the percentage of photosynthetically active radiation (PAR) blocked by the shade cloth. 

The shaded treatments will be hereafter referred to as 40TS, 80TS, 40FS, and 80FS. The 80TS 

treatment had a drastic effect on fruit growth due to 80% of PAR being blocked by the shade cloth, 

and the trees under these treatments experienced heavy fruit drop and we were able to obtain only 

a few fruit at harvest, and thus we could not conduct all the analyses. In 2021, the same experiment 

was replicated with one key difference: instead of the 40% and 80% shade treatments, we used 

30% and 60% shade cloth for both the tree and fruit shade treatments. The shade cloth (grommeted 

panel type) for all experiments was obtained from Greenhouse Megastore (Danville, IL, USA). 

Each of the four treatments were randomly assigned to two side-by-side trees within each block. 

All fruit clusters were thinned before the shade cloth was deployed to a single fruit due to 

availability of sufficient fruitlets for the experiment. The shade treatments were removed at 5 

WAFB. 

Trees were assessed to be in full bloom (greater than 50% of the flowers in bloom) on 12 

May 2020 and 14 May 2021 for ‘Porter’s Perfection’ and on 20 May 2021 for ‘Binet Rouge’. All 

the treatment trees were thinned to king blooms, and then further thinned to obtain similar crop 

densities (Supplementary Table 1). In general, the fruit shade treatments were found to have much 

less crop densities in comparison to the unshaded control and the tree shade treatments, hence, 

crop density was used as a covariate in our statistical modelling to account for the differences in 

crop density among treatments. The shade treatments were implemented immediately post hand 

thinning at 1 WAFB. No flower or fruit thinning chemicals were applied. The orchard was 



 

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otherwise managed using a conventional pest management regimen for diseases and arthropods 

(Agnello et al., 2020). 

Fruitlet and harvest sampling 

Six representative fruits from each experimental unit were sampled at 1, 3, and 5 WAFB, 

and at harvest. The fruit samples were kept on ice until they were separated into peel and cortex 

with knives. Care was taken to exclude the seed region including all the seeds from the tissue 

samples. Fruit tissue samples were flash frozen in liquid nitrogen and then stored at -80 °C until 

analysis. 

The Starch Pattern Index (SPI) was used to assess the maturity of fruit to decide on the 

harvesting date and fruit were harvested as close to an SPI of 8 (Blanpied and Silsby 1992). 

‘Porter’s Perfection’ was harvested at an average SPI of 7.1 on 23 Oct 2020, and 7.4 on 21 Oct 

2021. However, ‘Binet Rouge’ experienced a heavy pre-harvest fruit drop and had to be harvested 

much earlier than full maturity on 7 Oct 2021 with an average SPI of 3.4. ‘The fruits were harvested 

and counted on a per tree basis. Fruit mass was measured with an Adam CPW field scale (Oxford, 

CT, USA).  

Fruit Quality Analyses 

At harvest, a subset of ten fruit per experimental unit were randomly selected and used for 

maturity and quality analyses. Fruit mass and circumference were measured using a GÜSS fruit 

texture analyzer (Jennings, Strand, South Africa). Visual color measurements of fruit were 

measured as percent surface area of the fruit covered by red blush with the parameter ranging from 

0-100%. The chlorophyll content of the fruit was measured as a proxy for ripeness using the degree 

of absorbance meter (Model 5350 0, T.R. Turoni Srl, Forli, Italy). The apples were then punctured 



 

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with a GÜSS penetrometer (Jennings, Strand, South Africa) fitted with a 11.1 mm probe to assess 

flesh firmness on the blush and non-blush side of the fruit. Subsequently, fruit ripeness was 

measured using SPI by spraying an equatorial wedge of the fruit with an iodine solution consisting 

of 0.22 g·L−1 iodine, 0.88 g·L−1 potassium iodide (Sigma-Aldrich, St. Louis, MO, USA). 

Following the firmness and ripeness testing, fruit were milled, placed into ‘Good Nature’ filter 

bags (Buffalo, NY, USA), and pressed using the Norwalk 290 (Bentonville, AR, USA). This set 

up mimics a “rack and cloth” style press. Juice samples were frozen at -80 °C until further 

analyses.  

Juice Chemistry Analysis 

Samples were analyzed for soluble solids content (SSC), titratable acidity (TA), and total 

polyphenol content (TPC). A hand-held PAL-1 BLT digital refractometer (Omaeda, Saitama, 

Japan) was used to measure SSC. A Metrohm 809 Titrando autotitrator (Herisau, Switzerland) was 

used to measure TA by titrating 5 mL of juice in 40 mL of ultrapure Milli-Q-water (Darmstadt, 

Germany) against a 0.1M NaOH solution (Sigma-Aldrich, St. Louis, MO, USA) until the pH 

reached a value of 8.1. The Folin-Ciocalteau method (Singleton and Rossi, 1965) was used to 

measure TPC on a Spectramax 284 Plus spectrophotometer and Softmax Pro 7 Microplate Data 

analysis software (Molecular Devices, San Jose, CA). For juice samples, 1.5 µL of the sample or 

gallic acid standard (Sigma-Aldrich, St. Louis, MO, USA) was mixed with 34.9 µL of water and 

90.9 µL of Folin-Ciocalteau reagent (M.P. Biochemicals, Aurora, Ohio, USA) in a Cellistar 96 

well microplate (Greiner Bio-One, Monroe, NC, USA); three min after, 72.7 µL of 7 g·L−1 Na2Co3 

(Sigma-Aldrich, St. Louis, MO, USA) was added; the microplate was incubated for 1 hr under 

dark conditions before being measured at 765 nm. Gallic acid was used a standard for the TPC 

measurement and results were reported as g·L-1 of gallic acid equivalents.  



 

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For peel and flesh tissue, TPC was measured according to the protocol developed by Huber 

& Rupasinghe (2009) with a few modifications. The flash frozen apple peel and cortex tissue stored 

at -80 °C were lyophilized in a freeze drier (Labonco FreeZone 12L-84C bulk tray drier, Kansas 

City, MO, USA) which was set to 0 °C for 72 hr, and ground to a fine powder using a coffee 

grinder (KitchenAid, Benton Harbor, MI, USA). Two hundred mg of the tissue sample was 

sonicated (Branson 2800, Sonitek, Milford, CT, USA) with 10 mL of HPLC grade 100% methanol 

(VWR Chemicals, Radnor, PA, USA) for 15 min to extract the polyphenols. The extract was then 

centrifuged at 3,500 × g for 15 min, and 10 µL of the extract, or gallic acid was mixed with 100 

µL of the Folin-Ciocalteau reagent and gently mixed in a 96-well clear microplate. After 6 min, 80 

µL of 7.5 g·L−1 Na2CO3 (VWR Chemicals, Radnor, PA, USA) was added followed by a 1 hr 

incubation period in the dark and absorption measured at 765 nm. Results were expressed as mg 

of gallic acid equivalent per g of dry weight.  

High Performance Liquid Chromatography Analysis 

The harvest juice samples, and lyophilized peel and cortex samples taken at 1,3,5 WAFB, 

and at harvest, and were analyzed using a protocol modified from Hendrickson et al. (2016) and 

Tyagi et al. (2020). The monomeric polyphenols were chromatographically separated by Reverse 

Phase-HPLC using a Poroshell HPH-C18 (4.6 ×100 mm 2.7 μm particle) Agilent HPLC column 

on an Agilent Infinity 1260 HPLC system (Agilent Technologies, Santa Clara, CA, USA) equipped 

with a DAD detector using a binary solvent gradient with mobile phase A (1.5% formic acid in 

water) and mobile phase B (1.5 % formic acid (VWR Chemicals, Radnor, PA, USA), 1.36% water 

in acetonitrile (Avantor, Radnor, PA, USA). Juice samples were centrifuged at 14,000  g for 15 

min and the supernatant was filtered through a 0.2 µm membrane filter before injecting into HPLC. 

A 10 µL filtrate was injected into the HPLC for monomeric polyphenol analysis.  



 

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Lyophilized peel and cortex tissue (100 mg) was extracted with 3 mL of 50% methanol, 

1% HCl, and 10 µL of internal standard 4’,5’,7’-trihydroxy flavanone (50 mg·L-1) (Sigma Aldrich, 

St. Louis, MO, USA). The homogenate was vortexed and incubated at 4 °C for 4 h on ice with 

occasional mixing. Following that, the homogenate was centrifuged at 4,000 × g for 5 min at 4 °C 

and the supernatant was transferred to a fresh 15 mL tube. The pellets were reextracted twice with 

1 mL of methanol, and all the supernatants were combined to bring it to the final volume of 5 mL. 

1 mL from this extract was centrifuged at 14,000 × g for 5 min at 4 °C to remove any particulates 

and 5 μL was injected onto the HPLC column for monomeric polyphenol analysis. The remaining 

4 mL of sample was stored at -20 °C for proanthocyanidin measurements. 

The column temperature was maintained at 35 °C for the monomer polyphenol 

measurement. DAD detector wavelengths were set at 280, 320, and 360 nm. The starting condition 

of the gradient was 95 % of solvent A and 5 % of solvent B. Subsequently, solvent B was linearly 

increased to 15 % in 25 min, then to 27 % in 10 min, and keep at 27 % for 3 min. Thereafter, the 

mobile phase was reverted to the initial condition in 2 min and held for 3 min for re-equilibration 

of the column before the next injection. The total run time was 43 min. Briefly, the eluted 

compounds were monitored and identified by spectral and retention time comparisons to external 

standards at three different wavelengths: 280 nm [(+)-catechin (C), (−)-epicatechin (EC), 

procyanidin B1, procyanidin B1, procyanidin C1, procyanidin A1, procyanidin A2, and phloridzin, 

320 nm (5-caffeoylquinic acid, chlorogenic acid, 4-caffeoylquinic acid, p-coumaric acid, ferulic 

acid, and sinapic acid), and 360 nm (quercetin-3-galactoside, quercetin-3-glucoside, quercetin-3-

O-rutinoside, avicularia, and quercitrin). The identified compounds were quantified by external 

calibration curves. (+)-Catechin (C), (−)-epicatechin (EC), quercetin (quercetin-3-galactoside, 

quercetin-3 glucoside, and quercetin-3-O-rutinoside), avicularia, quercitrin, 5-caffeoylquinic acid, 



 

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chlorogenic acid, 4-caffeoylquinic acid, p-coumaric acid, ferulic acid, sinapic acid, procyanidin 

B2, procyanidin C1, procyanidin A1, procyanidin A2, and phloridzin were purchased from Sigma-

Aldrich (St. Louis, MO, USA). Procyanidin B1 was purchased from INDOFINE Chemical 

Company (Hillsborough, NJ, USA). All data processing and analysis were completed using 

Agilent CDS ChemStation software on an Agilent 1260 Infinity HPLC.   

Statistical Analysis 

This experiment was analyzed as a randomized complete block design using R (R Core 

Team, 2014). The data was analyzed in a mixed model with a random block term and a crop density 

covariate, using the lmer function from the lme4 package. However, it was found that the crop 

density variable and the random block variable were not significant by themselves or in any 

intereaction and hence, they were removed them from the analysis. Mean separation for a family 

of estimates (estimated marginal means, emmeans package), using the Tukey method, was 

performed using the cld function (multcomp package). Model assumptions were checked by 

examining the distribution and spread of residuals. The ggplot2 funtion was used to plot estimated 

marginal means line graphs and box plots for harvest and juice characteristics. 

 

Results 

Fruit harvest characteristics  

Overall, in 2020, there were differences between the control and the other treatment groups 

for fruit mass, circumference, DA, and firmness (Figure 4.1). However, the trends for these 

characteristics were not significantly different between treatments in 2021 for either ‘Porter’s 

Perfection’ or ‘Binet Rouge’. The average fruit mass of ‘Porter’s Perfection’ exhibited a 40% 

increase from 51 g in 2020 to 72 g in 2021, while ‘Binet Rouge’ had an average fruit mass of 88 g 



 

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in 2021. Interestingly in 2020, fruit from the various shade treatments had a significantly less mass 

(~30% decrease) in comparison to the control (P = 0.0013). Fruit circumference followed the same 

trends and treatment differences as fruit mass. The average DA in 2020 was 0.92, whereas it was 

0.68 and 0.48 for ‘Porter’s Perfection’ and ‘Binet Rouge’ in 2021, respectively (Figure 4.1). While 

the FS treatments had a statistically greater DA value than the control in 2020 (P = 0.0039), there 

were no differences for either of these treatments with 40 and 80 TS treatments in 2020. There 

were no DA differences among treatments in 2021 for either ‘Porter’s Perfection’ or ‘Binet Rouge’. 

The SPI was similar between years for ‘Porter’s Perfection’ with 7.1 in 2020 and 7.4 in 2021. 

‘Binet Rouge’ was harvested at an SPI of 3.4 due to heavy fruit drops. For SPI, there were no 

differences between treatments for any of the cultivars in any year. ‘Porter’s Perfection’ in 2020 

and ‘Binet Rouge’ in 2021 had an average firmness of 103 N compared to 87 N for ‘Porter’s 

Perfection’ in 2021. In 2020, apple from the tree and fruit shade treatments had ~18% greater 

firmness than the control (P = 0.0006). There were no differences among treatments in 2021 for 

both ‘Porter’s Perfection’ and ‘Binet Rouge’. 



 

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Figure 4.1 Boxplots of juice quality characteristics of cultivar ‘Porter’s Perfection’ subject to 40 

and 80% Tree Shade (40TS and 80TS), 40 and 80% Fruit Shade (40FS and 80FS), and no shade 

(Control) in 2020 and cultivars ‘Porter’s Perfection’ and ‘Binet Rouge’ subject to 30 and 60% Tree 

Shade (30TS and 60TS), 30 and 60% Fruit Shade (30FS and 60FS), and no shade (Control) in 

2021. Means comparison followed by the same lowercase letter are not significantly different 

based on Tukey’s HSD means comparison at α = 0.05. In 2020, there were insufficient 80TS fruit 

for analyses. 



 

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Fruit juice characteristics 

 The average TA was comparable between years for ‘Porter’s Perfection’ with a value of 8.9 

and 9.3 g·L-1 measured in 2020 and 2021, respectively, whereas ‘Binet Rouge’ had an average TA 

of 2.2 g·L-1 (Figure 4.2). The total average SSC content for ‘Porter’s Perfection’ was 15.2 and 

16.1°Brix for 2020 and 2021, respectively. ‘Binet Rouge’ had a greater average SSC at 17.8 °Brix. 

In 2020, the FS treatments had a significantly less SSC (~8% decrease) than the control (P = 

0.0035), but there were no differences between the TS treatments and the control. There were no 

differences among treatments for SSC in 2021 for both cultivars. At harvest, there were no 

differences among treatments in TPC for any of the cultivars in either year. ‘Porter’s Perfection’ 

had an average TPC of 4.2 g·L-1 for both years of the study and ‘Binet Rouge’ had an average TPC 

of 1.9 g·L-1.   

 



 

136 

 

 

Figure 4.2 Boxplots of juice quality characteristics of cultivar ‘Porter’s Perfection’ subject to 40 

and 80% Tree Shade (40TS and 80TS), 40 and 80% Fruit Shade (40FS and 80FS), and no shade 

(Control) in 2020 and cultivars ‘Porter’s Perfection’ and ‘Binet Rouge’ subject to 30 and 60% Tree 

Shade (30TS and 60TS), 30 and 60% Fruit Shade (30FS and 60FS), and no shade (Control) in 

2021. Means comparison followed by the same lowercase letter are not significantly different 

based on Tukey’s HSD means comparison at α = 0.05. In 2020, there were insufficient 80TS fruit 

for analyses. 

 

 

 

 

 



 

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Juice polyphenol compounds 

In 2021, we identified about fifteen different polyphenol compounds in the juice of both 

‘Porter’s Perfection’ and ‘Binet Rouge’ at harvest through HPLC. The average total measured 

polyphenol concentrations for ‘Porter’s Perfection’ and ‘Binet Rouge’ were 566 and 990 mg·L-1, 

respectively (Figure 4.3). In general, ‘Porter’s Perfection’ had greater concentrations of all 

measured juice polyphenols over ‘Binet Rouge’ with the exception of caffeoylquinic acids. The 

greater concentrations of proanthocyanidin monomers and oligomers in ‘Porter’s Perfection’ (464 

mg·L-1) compared with ‘Binet Rouge’ (70 g·L-1) accounted for most of the differences in TPC 

between the two cultivars. Among the five different categories of polyphenol compounds, the 

major contributors were proanthocyanidin monomers and oligomers, and phenolics acids which 

together constituted 97% of the total measured polyphenols in the juice of both cultivars (Figure 

4.3). The proanthocyanidin monomers and oligomers contributed to 47% (464 mg·L-1) and 12% 

(70 mg·L-1) of juice polyphenols for ‘Porter’s Perfection’ and ‘Binet Rouge’, respectively, whereas 

the phenolics acids contributed to 50% (492 mg·L-1) and 85% (480 mg·L-1) of juice polyphenols 

for ‘Porter’s Perfection’ and ‘Binet Rouge’. Procyanidin B2 and chlorogenic acid were the 

predominant proanthocyanidin and phenolic acid compound found in both cultivars. In the total 

measured HPLC juice polyphenols, overall trends indicate that the 30TS and 60TS had 

sequentially less concentrations of polyphenols than the control and the fruit shade treatments were 

not significantly different from the control, with a few exceptions. In the total measured HPLC 

juice polyphenols, the 60TS treatment had a significant 23% decrease in concentration from the 

60FS (P = 0.0345), as well as a 15% decrease in concentration as compared to the control (P = 

0.0917). In ‘Binet Rouge’, the 60TS and 60FS treatment had a 27 (P = 0.0134) and 30% (P = 

0.0083) decrease in concentration respectively as compared to the control. Among procyanidins, 



 

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there were no significant differences among treatments for ‘Porter’s Perfection’ although the 

average values for the 60TS was trending downward from the control and FS treatments. For ‘Binet 

Rouge’ there was a significant decrease in the 60FS treatment in comparison to the 30TS treatment 

for procyanidin B2 (P = 0.0308); this trend was similar for epicatechin (P = 0.0389). Among 

phenolic acids, chlorogenic acid and p-coumaric acid exhibited treatments differences for both 

cultivars, and 4-caffeoylquinic acid for ‘Binet Rouge’ alone (Figure 4.3). For ‘Porter’s Perfection’, 

the 60TS treatment had 24% less chlorogenic acid concentration than the control. For ‘Binet 

Rouge’, the 30TS and 60TS treatments had 11 and 25% less chlorogenic acid concentration than 

the control, respectively, with the latter comparison being statistically significant (P = 0.0176). For 

p-coumaric acid, in ‘Porter’s Perfection’, the 60TS treatment had 26 and 20% less concentration 

than the 60FS (P = 0.0103) and the control (P = 0.0790) treatments, respectively. For 4-

caffeoylquinic acid, in ‘Binet Rouge’, the 30TS and 60TS had 13 and 27% less concentrations than 

the control respectively, with the latter being a statistically significant difference (P = 0.0014). The 

quercetin glycosides were grouped together as they followed the same trends and many of the 

compounds had very low concentrations in the juice (Figure 4.3). In ‘Porter’s Perfection’, the 60TS 

treatment had 25 and 14% less quercetin glycoside concentration than the 60FS treatment (P = 

0.0103) and the control (P = 0.0747) respectively. In ‘Binet Rouge’, the 60TS treatment had 47 

and 33% less quercetin glycoside concentration than the 30TS treatment (P = 0.0103) and the 

control (P = 0.0747) respectively. 



 

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Figure 4.3 Boxplots of individual juice polyphenols of cultivars ‘Porter’s Perfection’ and ‘Binet 

Rouge’ subject to 30 and 60% Tree Shade (30TS and 60TS), 30 and 60% Fruit Shade (30FS and 

60FS) and no shade (Control) in 2021 . Means comparison followed by the same lowercase letter 

are not significantly different based on Tukey’s HSD means comparison at α = 0.05. 



 

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Total polyphenols in the peel and flesh - Folin-Ciocalteau assay 

To understand the accumulation patterns of polyphenols in different tissue types such and 

flesh and peel, we analyzed TPC for both flesh and peel at 3 and 5 WAFB, and at harvest (23 

WAFB for ‘Porter’s Perfection’ and 21 WAFB for ‘Binet Rouge’) for both cultivars and between 

years (Figure 4.4). At 1 WAFB, a combined sample was used for analysis as the fruits were too 

small to separate the peel and flesh tissue. For ‘Porter’s Perfection’, there was some variation in 

TPC between years in both peel and flesh tissue. While the TPC of ‘Porter’s Perfection’ was 

comparable between years at 1 WAFB and at harvest, it had greater concentrations at 3 and 5 

WAFB in 2020 as compared to 2021.  

In the flesh tissue, the treatment differences were mostly limited to 3 and 5 WAFB for both 

cultivars. In ‘Porter’s Perfection’ flesh tissue at 3 WAFB, the 40 and 80TS had 35 and 44% less 

concentrations of TPC in comparison to the control (P < 0.0001) in 2020, whereas in 2021, the 

60TS treatment had a 40% less TPC in comparison to the control (P = 0.0061) (Figure 4.4). At 5 

WAFB in 2020 for the flesh tissue, the 40TS and 80TS treatments had 23 (P = 0.0022) and 54% 

(P < 0.001) less TPC than the control, respectively, whereas in 2021, the 40TS and 80TS treatments 

had 31 (P = 0.0172) and 62% (P < 0.001) less TPC than the control respectively. ‘Binet Rouge’ 

flesh tissue also had significant differences between the control and the TS treatments, albeit to a 

smaller degree and only at 3 WAFB. At 3 WAFB, the 40TS and 80TS treatments of ‘Binet Rouge’ 

had 25 (P = 0.0427) and 32% (P = 0.0051) less TPC than the control respectively. There were no 

significant differences between the control and the FS treatments.  

In the peel tissue, there were largely no differences between treatments at 1 WAFB and at 

harvest and between the control and FS treatments (Figure 4.4). For ‘Porter’s Perfection’, the 40TS 

had 24% less TPC than the control at both 3 and 5 WAFB in 2020 (P < 0.0001). The peel tissue of 



 

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80TS treatment in 2020 is not shown due to lack of samples at some time points. In 2021, the 

differences between the 60TS and control treatments were observed only at 5 WAFB in ‘Porter’s 

Perfection’, with the 60TS treatment having 54% less TPC than the control (P < 0.0026). There 

were no differences between treatments for ‘Binet Rouge’ peel, except for some initial variation at 

1 WAFB.  

 

 

 

 

 

 

 

 

 

Figure 4.4 Estimated marginal means of total polyphenol content as measured by the Folin-

Ciocalteau assay for flesh and peel tissue of the cultivars ‘Porter’s Perfection’ in 2020 and 2021, 

and ‘Binet Rouge’ in 2021. At 1 WAFB, a combined sample was used for measurements as the 

flesh and peel could not be separated. Significant differences between treatments at any time point 

is indicated based on the P value (* P < 0.05, ** P < 0.01, *** P < 0.001). Tree Shade (TS), Fruit 

Shade (FS). 

 

 



 

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Flesh and peel polyphenol compounds measured by HPLC 

For ‘Porter’s Perfection’, we analyzed the individual polyphenol compounds in the flesh 

and peel separately. We used only samples from 2021 as enough replicates were not available for 

some treatments in 2020. Also, we decided to focus on the control and TS treatments, as we did 

not observe variation between the control and FS treatments. At 1 WAFB, a combined sample was 

used for analysis as the fruits were too small to separate the peel from flesh to obtain sufficient 

tissue for analysis. We identified eleven polyphenol compounds in the peel and flesh tissue of 

‘Porter’s Perfection’ (Figure 4.5), and an additional five quercetin glycosides that were only 

present in the peel tissue (Figure 4.6). Two additional procyanidins (procyanidin A1 and C1) were 

identified in the peel and flesh tissue that was not found in the juice sample (Figure 4.5).  

The concentrations of most polyphenol compounds decreased from either 1, 3, or 5 WAFB 

through to harvest with some notable exceptions including many proanthocyanidins and quercetin 

glycosides (Figure 4.5). The total concentrations of all the polyphenol compounds measured in the 

peel and flesh was greatest at 1 WAFB (38 mg·g-1 DW) and decreased from 1 to 3 WAFB (~10-14 

mg·g-1 DW), followed by a gradual reduction in concentration until 23 WAFB (~5-7 mg·g-1 DW). 

At 23 WAFB, the peel had ~45% greater total measured polyphenol concentration than the flesh 

tissue. At 1 WAFB, the major contributors to the high concentration of polyphenols were phlorizin 

and chlorogenic acid which contributed 57 and 27% (22 and 10 mg·g-1 DW), respectively, to the 

total measured polyphenol content. The concentrations of all the other compounds were equal to 

or less than 1 mg·g-1 DW at 1 WAFB. Both phlorizin and chlorogenic acid quickly reduced in 

concentrations to ~1 mg·g-1 DW and ~3-5 mg·g-1 DW at 3 WAFB for both peel and flesh 

contributing to the drastic decline in the total measured polyphenol content from 1 to 3 WAFB. At 

23 WAFB, the flesh and peel tissue differed in composition of polyphenols. The primary difference 



 

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was that the peel had ~30% less concentration of proanthocyanidins and ~50% less concentration 

of chlorogenic acid than the flesh tissue but more than made up for the difference with quercetin 

glycosides which were absent in flesh tissue (Figure 4.5, 4.6).  

In the total measured polyphenols in flesh by HPLC, at 3 and 5 WAFB, the 60TS treatment 

had a significantly less concentration of 26 and 46% respectively in comparison to the control (P 

= 0.0390, P = 0056). In the peel, the difference was significant only at 5 WAFB with 60TS 

treatment having 44% less polyphenols than the control (P < 0.0056).  

 All proanthocyanidins, except for catechin and procyanidin A1, gradually increased in 

concentration to peak at 23 WAFB. Catechin and procyanidin A1 decreased in concentration from 

1 WAFB until harvest at 23 WAFB. At 1 and 23 WAFB, there were no differences between 

treatments for any of the proanthocyanidins. In general, the 60TS at 5 WAFB had a significant 

decrease in proanthocyanidin concentration for both peel and flesh, and for certain 

proanthocyanidins at 3 WAFB (flesh of procyanidin B1, B2, A1, and C1).  

For phenolic acids, there were no differences between treatments at 23 WAFB. However, 

in both the peel and flesh at 5 WAFB, the 60TS had less concentrations of all phenolic acids than 

the control whereas at 3 WAFB, the 60TS treatment had less concentrations of chlorogenic acid 

and p-coumaric acid than the control. On average, the 60TS treatment had anywhere from 20-85% 

less concentrations of the different phenolic acids in comparison to the control at 3 and 5 WAFB. 

For quercetin glycosides, the 60TS treatment had significantly less concentrations than the control 

from 3 WAFB to harvest for all five of the polyphenol compounds measured in the peel.  



 

144 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4.5 Estimated marginal means of total and individual polyphenol compounds as 

measured by HPLC for flesh and peel tissue of the cultivar ‘Porter’s Perfection’ subject to 30 and 

60% Tree Shade (30TS and 60TS) and no shade (Control) in 2021. Means comparison within 

each time point followed by the same lowercase letter are not significantly different based on 

Tukey’s HSD means comparison at α = 0.05. At 1 WAFB, a combined sample was used for 

measurements as the flesh and peel could not be separated.



 

145 

 

 

 

 

 

 

 

Figure 4.6 Estimated marginal means of total and individual polyphenol compounds as 

measured by HPLC identified only in the peel tissue of the cultivar ‘Porter’s Perfection’, subject 

to 30 and 60% Tree Shade (30TS and 60TS) and no shade (Control) in 2021. Means comparison 

within each time point followed by the same lowercase letter are not significantly different based 

on Tukey’s HSD means comparison at α = 0.05. At 1 WAFB, a combined sample was used for 

measurements as the flesh and peel could not be separated. 

 

Discussion 

Effect of sunlight exposure on cider fruit quality 

In 2020, the shade treatment fruit in the cultivar ‘Porter’s Perfection’ had ~30% less fruit 

mass and circumference as compared to the rest of the shade treatments. The shade treatments also 

had 37% greater DA and 18% greater firmness values, but 8-10% less soluble solids concentration 

than the control. The 80TS treatment was very extreme and resulted in harvesting <15 fruits at 

harvest as most of the other fruit dropped possibly in response to lack of carbohydrate 

accumulation to sustain their growth. Further, the loss in fruit quality parameters such as mass and 

circumference and reduced SSC seemed to be controlled in part by less access to carbohydrates 

during a critical period when the leaves are greater sinks than sources of carbohydrates (Wünsche 

et al., 2005; Feng et al., 2014). Increased canopy light interception allows for greater 

photosynthesis and thus carbohydrate production which is needed to sustain fruit growth and 

development (Lakso et al., 1989; Wünsche et al., 2005). The cell division stage of apple fruit 

Control 
30TS 
60TS 



 

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growth (7-35 DAFB or 1-5 WAFB) is heavily dependent on carbohydrate availability (Lakso et al. 

1989). Reduced availability of carbohydrates during the cell division stage of fruit growth due to 

reduced net photosynthesis achieved through early tree shading, reduces fruit size by limiting cell 

growth (Lakso et al., 1989; Grappadelli et al., 1994; Bepete and Lakso, 1998). These results agree 

with an experiment using the cider cultivar Dabinett where 60% shaded fruit had 10% less mass 

than the control trees from 1-5 WAFB (Karl and Peck 2022). 

However, the shading effects found in 2020 were not repeated in 2021 for either ‘Porter’s 

Perfection’ or ‘Binet Rouge’. In fact, there were no differences among treatments for any of the 

fruit or juice quality measurements at harvest. The seasonal variation could be due to differences 

in carbohydrate reserves between the two years of study, as there is more stress on carbohydrate 

availability in the ‘on year’ due to excessive cropping (Lakso et al., 1989). Differences due to 

limited carbohydrate availability could be more visible during the ‘on year’ than in the ‘off year’ 

and could explain why we saw differences in fruit quality parameters in ‘Porter’s Perfection’ due 

to shade in 2020 but not in 2021. However, further studies on carbohydrate (starch and sugars) 

status during the ‘on year’ and ‘off year’ is required to confirm this hypothesis.  

Early tree shading results in reduced polyphenols in cider apple juice 

 Cumulative juice polyphenols measured through HPLC, had a consistent trend of TS 

treatments having less polyphenols as compared to the control, whereas the FS treatments were 

not significantly different from the control. The 60TS treatment had 15 and 27% less TPC in 

comparison with the control in ‘Porter’s Perfection’ and ‘Binet Rouge’, respectively. This is 

consistent with results from Karl and Peck (2022) who found a 22% less polyphenol content in the 

60TS treatment versus the control in the cider cultivar ‘Ellis Bitter’ and ‘Major’. Direct effect of 

sunlight on the fruit did not impact polyphenol levels as there were no differences between the FS 



 

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treatments and the control indicating that there is minimal effect of direct sunlight on polyphenol 

development in cider apples, except for anthocyanin accumulation (Karl and Peck, 2022).   

Carbohydrate availability during the cell division phase of fruit growth (1-5 WAFB) was likely the 

primary controlling factor of polyphenol synthesis in apples. Primary and secondary metabolism 

is dependent on carbohydrate availability during the crucial cell division phase from 1-5 WAFB 

where carbohydrates are necessary for cell division, growth, and function. In this period of cell 

division in fruit, existing carbohydrate reserves preferentially assist with shoot growth and 

extension rather than invest in fruit sinks, hence the fruit are dependent on localized carbohydrates 

produced from spur leaves adjacent to the fruit. Early tree shading which also shades the spur 

leaves results in reduced photosynthesis and carbohydrate production, thus delaying the movement 

of carbohydrates from the leaves to adjacent fruit sinks (Grappadelli et al., 1994). This delay in 

carbohydrate movement to the fruit sinks could delay and reduce the production of secondary 

metabolites such as polyphenols (Anthony et al., 2023)  

Most of the differences in total juice polyphenols between the 60TS treatments and control 

were due to the differences in phenolic acids and quercetin glycosides. There were no significant 

differences between treatments for total procyanidin content and dihydrochalcones. We did not 

find any sources in the literature to substantiate the differential response of different polyphenol 

classes to carbohydrate stress. However, many of the procyanidin monomers and oligomers are 

accumulated throughout the growing season (Renard et al., 2007), which would allow the tree to 

adjust the carbohydrate metabolism based on the sink needs (Yang et al., 2021). For example, 

procyanidin concentrations for the 30TS and 60TS treatments had similar proanthocyanidins as 

the control treatment at harvest, even though they were lower in these treatments at 3 and 5 WAFB.  



 

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 Analyzing the accumulation patterns for different polyphenol compounds in peel and flesh 

tissue, we observed a consistent trend of less polyphenol content in all compounds in the 30TS and 

60TS treatments as compared to the control during that treatment application period of 3 to 5 

WAFB. However, there were no differences among treatments at harvest. The harvest TPC of the 

flesh and peel polyphenols was not in concurrence with the results obtained from juice samples 

which showed differences between the 60TS treatments and the control for certain phenolic acids 

and quercetin compounds. These differences could be related to chemical extraction processes 

during pressing or other chemical reactions, such as oxidation (Ma et al., 2019). For cider 

production, it is perhaps more important to look at the differences in juice content.  

Most polyphenol compounds are produced during the 1-5 WAFB period 

An objective in this study was to obtain a detailed understanding of the development of 

polyphenols and their subclasses in cider apples. Our results demonstrated that most of the 

polyphenols, except for proanthocyanidin monomers and oligomers, develop in the first five weeks 

of fruit development during the cell division phase, followed by a gradual decline in polyphenol 

concentrations until harvest. Karl and Peck (2022) also observed that the total polyphenols, as 

measured through the Folin-Ciocalteau assay, reached their peak concentration at 3-5 WAFB for 

the cider cultivars ‘Dabinett’, ‘Major’, and ‘Ellis Bitter’. Additionally, Renard et al. (2007) found 

that most polyphenols develop in the first 6 WAFB in the cultivars ‘Avrolles’ and ‘Kermerrian’, 

and drastically declined in concentration after the cell division phase of fruit growth.  

  We observed an increase in the proanthocyanindin monomer and oligomer concentrations 

towards harvest, which was a unique trend in terms of polyphenol development in apples and could 

be due to breakdown of longer chain tannins as suggested by Renard et al. (2007) or due to de 



 

149 

 

novo synthesis. Carbon isotope studies would provide additional understanding of the development 

of proanthocyanidin monomers and oligomers in cider apples. 

Accumulation patterns of different polyphenol classes in cider apple juice, flesh, and peel 

At harvest, the peel had a 45% greater polyphenol concentration than flesh tissue, which is 

consistent with previous studies on different cider apple cultivars that indicated that polyphenols 

are in greater concentration in the peel than the flesh on a dry weight basis (Vieira et al., 2011; 

Jakobek et al., 2013; Thompson-Witrick et al., 2014). However, fruit flesh has a larger mass than 

the peel and therefore, have more polyphenol concentration on a fresh weight basis (McGhie et 

al., 2005). 

Procyanidins were prominent and constituted almost ~50% of the juice and flesh, and 

~30% of the peel polyphenol concentrations. While these results are consistent with some studies 

(Tsao et al., 2005; Khanizadeh et al., 2008), other studies reported much greater concentrations of 

procyanidins, as high as 80% of total polyphenols measured (Alonso-Salces et al., 2004; Vrhovsek 

et al., 2004; Wojdyło et al., 2008) and yet another set of studies reported less concentrations of 

procyanidins than we observed (Thompson-Witrick et al., 2014; Kschonsek et al., 2018). There 

are no standardized extraction conditions for juice, flesh, or peel HPLC analyses, which likely 

contributes to some of this variation (McGhie et al., 2005; Goulas et al., 2014). Our results also 

indicated greater concentrations of all procyanidins in the flesh as compared to the peel, with the 

flesh containing as much as 30% greater proanthocyanidin content than the peel (Renard et al., 

2007). Procyanidin B2, followed by procyanidin B1 and epicatechin had the greatest 

concentrations in juice, flesh, and peel tissue, accounting for the majority of the total procyanidins 

present, and this was consistent with studies done on other cider and fresh-market apple cultivars 

(Mayr et al., 1995; Tsao et al., 2005; Marks et al., 2007; Renard et al., 2007).   



 

150 

 

Proanthocyanidin monomers and oligomers in our study had a unique accumulation pattern 

in the peel and flesh tissue. Except for catechin and procyanidin A1 which were mostly produced 

in the 1-5 WAFB period and decreased in concentrations until harvest, the other compounds 

decreased in concentrations from 1-5 WAFB, but then increased in concentrations from 5 WAFB 

until harvest. Most of the compounds had greater concentrations of epicatechin and procyanidin 

oligomers at harvest as compared to the 1-5 WAFB concentrations. This trend was also observed 

by Renard et al. (2007) in the cider cultivar ‘Kermerrian’. This increase in procyanidin 

concentrations during harvests could be due to aggregation of catechin and epicatechin molecules, 

or it could be due to the breakdown of larger procyanidins or tannins into procyanidin oligomers 

(Mayr et al., 1995; Renard et al., 2007).  

The next class of compounds that were prominently present was phenolic acids. 

Chlorogenic acid was the most predominant phenolic acid in cider apple juice, flesh, and peel 

tissue in our study constituting ~50% of juice and flesh tissue, whereas it constituted ~80% of peel 

tissue. Chlorogenic acid was the main phenolic acids in multiple cider cultivars in previous studies 

(Sanoner et al., 1999; Guyot et al., 2003; Jakobek et al., 2013). The increase in concentration of 

chlorogenic acid as a ratio of total polyphenols in our study could be due to the non-accounting 

for tannin concentrations as part of the total procyanidin content in our study. Phenolic acid 

compounds were mainly produced during the 1-5 WAFB period and gradually reduced in 

concentration until harvest (Renard et al., 2007).  

The only dihydrochalcone identified was phlorizin. At harvest, the concentration of 

phlorizin in apple juice polyphenols was ~1.5%, roughly in line with studies on other cultivars 

(Vrhovsek et al., 2004; Tsao et al., 2005). Phlorizin had the greatest concentration among all 



 

151 

 

polyphenol compounds at 1 WAFB; however, it decreased drastically in concentration from 1 to 3 

WAFB and gradually decreased after that to only 2% of total polyphenol concentration at harvest. 

Quercetin glycosides were only identified in the peel tissue as previously confirmed in 

multiple studies on cider and fresh-market apples (Tsao et al., 2005; Takos et al., 2006a; Renard et 

al., 2007) and they had very low concentrations in juice as most of the juice polyphenols are 

obtained from the flesh and not from the peel (Lea and Arnold, 1978). At harvest, ~50% of the 

total polyphenols in peel were quercetin glycosides, which is in line with other studies on peel 

polyphenols (Escarpa and González, 1998; Vrhovsek et al., 2004).  

Polyphenol measurement methods  

 Within the 1-5 WAFB period, we obtained slightly different results between total 

polyphenols as measured by the Folin-Ciocalteau assay, and the sum of the individual polyphenol 

compound concentrations that were measured using HPLC. While the Folin-Ciocalteau assay 

indicated a steady accumulation of polyphenols until 5 WAFB followed by a steady decline until 

harvests for ‘Porter’s Perfection’, the sum of the individual polyphenols measured indicated the 

maximum concentrations of measured polyphenols at 1 WAFB and a steady decline in polyphenol 

concentrations until harvests. Both methods have their respective advantages and disadvantages. 

The Folin-Ciocalteau method is a reliable, precise, and consistent method for polyphenol 

measurement while also being time and cost effective; however, it does not measure concentrations 

of individual polyphenol compounds and also measures other ferric reducing compounds such as 

ascorbic acid, sulfur dioxide, and reducing sugars (Everette et al., 2010). The other measurement 

of total polyphenols was through HPLC. While the HPLC measurements of individual polyphenols 

with standards is the most precise for polyphenol measurements, the sum of all the individual 

measured polyphenols considers only the polyphenol compounds that were able to be measured 



 

152 

 

using the particular HPLC method, but also does not measure other polyphenol compounds such 

as long chain proanthocyanidins or tannins that are important for hard cider quality. 

Implications for future research 

 Modifying the light exposure in the early stages from 1-5 WAFB resulted in differences in 

juice polyphenol concentrations that persisted until harvest, especially with phenolic acids and 

quercetin glycosides. Research on sensory thresholds for different polyphenol compounds and 

classes for hard cider would be useful to understand the practical significance of differences in 

juice polyphenol content. There were no differences in proanthocyanidin monomer and oligomer 

concentrations in fruit or juice at harvest. In conclusion, early tree shading’s effect on polyphenol 

development is extremely specialized to different classes of polyphenol compounds and further 

research on longer chain proanthocyanidins or tannins is imperative to gain a comprehensive 

understanding of the impact of early tree shading on polyphenol compounds. 

 

 

 

 

 

 

 

 

 

 



 

153 

 

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https://doi.org/10.1016/j.foodchem.2010.05.053


 

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Chapter 5 

Concluding remarks and reflections 

 

I came into Dr. Gregory Peck’s lab working with cider apples and hard cider in 2018, at a 

time when there had been continued year-over-year growth in cider production in the United States. 

Cider consumption and production was booming, especially in NY state which currently houses 

more than 125 cideries, among more than 1,000 nationwide. While large-scale cider producers 

faced a slowdown of sorts with regards to cider sales during the COVID-19 pandemic, the regional 

cider scene was and is still growing. There is much interest from consumers and cider producers 

regarding traditional, heirloom, and specialty cider cultivars, and consequently a huge demand for 

this fruit. However, there is not enough supply to meet this demand (Zakalik and Peck, 2023). In 

conversations with cidermakers and orchardists at the national cider conference (CiderCon) over 

the past few years, a considerable acreage of new cider cultivars such as ‘Porter’s Perfection’, 

‘Dabinett’, ‘Binet Rouge’, ‘Ellis Bitter’, ‘Tremlett’s Bitter’, and many others have been planted. 

There was also interest in rediscovering some of the cultivars that were popularly grown in the 

past and growing cultivars that were popular for cider in England and France. To respond to this 

interest to understand the diversity of available cultivars, along with others in the Peck Lab, we 

phenotyped around 350 accessions from the United States Department of Agriculture’s (USDA) 

Malus germplasm collection. Although only part of this work (acidity chapter) forms a chapter of 

my dissertation, it is a comprehensive list of cultivars with detailed phenotypic data on 

horticultural, fruit, and juice quality traits and will serve as a database for growers, fellow 

researchers, and cider enthusiasts to learn from it and use that information to make informed 

decisions on planting and cultivar selections.  



 

160 

 

While phenotyping a comprehensive list of cultivars from the USDA Malus germplasm 

collection, we also genotyped them for acidity markers Ma1 and Ma3. The idea was to develop a 

marker-based classification system instead of the somewhat arbitrary threshold of 4.5 g·L-1 of 

malic acid content popularized by the Long Ashton system of classification to classify apples into 

the sweet or sharp category. Alone, the Ma1 marker can be used to categorize cider apple acidity 

into low (<2.4 gL-1), medium (2.4-5.8 gL-1), and high (>5.8 gL-1) groups and allow for ease of 

comparisons across various geographical, seasonal, and horticultural considerations. The 

combination of Ma1 and Ma3 markers provided more specificity and can be useful for plant 

breeding applications. This work also identified a significant difference (P = 0.0132) in acidity 

associated with ploidy with triploids having on average 0.33 gL-1 greater TA than diploids. This 

research is a useful starting point to think about how we want to approach acidity classification 

systems for cider apples and further refine the system by using newly developed acidity markers. 

As far as the next steps for this project are concerned, more germplasm needs to be evaluated to 

encompass a wider range of allelic variability, particularly for triploids and accessions with the 

recessive q8 alleles. Identification of additional acidity genes in apples would also help to account 

for more variability. Furthermore, sensory analysis would help to understand the thresholds for the 

human perception of acidity at different TA concentrations, as well as elucidate how other factors, 

such as sugar and polyphenol content, could affect the perception of acidity (Hampson et al. 2000; 

Rymenants et al. 2020). Lastly, adding genetic markers for sugar and polyphenol content would 

create a robust suite of markers for plant breeders, horticulturalists, and commercial cider 

producers to rapidly identify potential cider apple cultivars in germplasm collections and breeding 

populations. 



 

161 

 

Specialized cider apple orcharding in a high-density planting system is a relatively new 

phenomenon in the US and there is a wide range of practices being followed to manage these 

cultivars. While some orchardists follow a standard orchard maintenance regimen akin to fresh-

market apples, others managed cider orchard blocks with a laissez-faire approach, which leads to 

biennial bearing (overcropping in the ‘on’ year and less to no cropping in the ‘off’ year). There is 

a need for research into production practices for these cultivars including a training and thinning 

regimen to tackle issues of biennial bearing. Another important observation from orchardists was 

the differences in tannin content that they observed from year-to-year. The way they observed this 

was that some years, the fruits were more bitter and astringent than other years. While a lot of that 

variation is due to differing weather conditions, we wanted to understand the source sink 

relationship between carbohydrates and total polyphenol content in cider apples. We focused our 

efforts on the cell division phase of fruit growth (about 1-5 weeks after full bloom) as previous 

research has shown that most polyphenols are produced primarily during the early phase of fruit 

growth and development (Renard et al. 2007; Henry-Kirk et al. 2012; Karl and Peck 2022). Hence, 

we established different crop densities (Chapter 3) to not only help us understand the molecular 

underpinnings and accumulation patterns of individual polyphenol compounds over the growing 

season but also inform thinning recommendations for cider apple orchards. Crop densities had a 

measurable significance on all fruit and juice quality variables. In general, reduced crop density 

enhanced fruit juice characteristics such as soluble solid content, titratable acidity, and total 

polyphenol content. Total polyphenol content was enhanced not due to dilution but due to a 

reduction in yield of polyphenols on a dry weight basis. Crop densities at or greater than 15 

fruits/cm2 trunk cross sectional area resulted in markedly reduced or no return bloom and hence, I 

support establishing a crop density of 9-10 fruits/cm2 trunk cross sectional area as suggested by 



 

162 

 

Zakalik et al. (2023) to enhance yields and total polyphenol content, while maintaining enough 

return bloom for regular year-year bearing. It was interesting to note that proanthocyanidin 

monomers and oligomers accumulated in greater concentrations during the pre-harvest in the flesh 

tissue, indicating a different trend than most other polyphenols which are produced during the first 

30 days after full bloom. Further, this study provides an overview of the molecular underpinnings 

of carbohydrate and polyphenol metabolism. A reduced crop density enhanced the expression of 

secondary metabolite pathway production genes, which resulted in an increase in polyphenols and 

specifically, proanthocyanidin building blocks such as catechin and epicatechin. This study has 

also identified key transcription factors that could potentially be involved in regulating 

proanthocyanidin production in cider apples and further functional analysis on these genes would 

help to uncover regulatory mechanisms of proanthocyanidin production in cider apples. Based on 

the results, I believe that thinning as early as possible will result in the best results for achieving 

both a sustainable crop density and high tannin content. Since most orchardists do not get 

renumerated for greater tannin content, the immediate practical significance of my research is most 

relevant for a vertically integrated orchard where achieving high tannin concentrations could be 

one of their objectives to enhance flavor, aroma, and color of their hard ciders.   

 In chapter 4, we wanted to understand the effect of reduced carbohydrate availability on 

the development of polyphenols by blocking photosynthetically active radiation using early tree 

shading. We observed reduced phenolic acids and quercetin glycoside concentrations at harvest in 

the 60-tree shade treatment (60% of photosynthetically active radiation blocked) in comparison to 

the unshaded control. Early season shading of apple trees had a minimal impact on production of 

procyanidin monomers and oligomers in cider apples. Fruit shaded trees were not significantly 

different from the control. While concentrations of different polyphenol compounds were less 



 

163 

 

during the shading period of 1-5 weeks after full bloom, their concentrations recovered during the 

rest of the growing season and were not significantly different from the unshaded control at 

harvest. More research on longer chain proanthocyanidins or tannins is necessary to obtain the full 

picture of the effect of early tree shading. It would be interesting to analyze the carbohydrate 

(starch and sugars) content at the different stages of fruit growth to understand how it relates to the 

polyphenol concentrations of the different classes of polyphenol compounds. Based on the results 

from the early tree shading experiment, I suggest that there is some sort of equilibrium level for 

polyphenol content or more specifically proanthocyanidin content in apples, for which there needs 

to be more research on carbohydrate status throughout the growing season and labelled carbon 

studies to understand how the metabolic and molecular processes are regulated to assist the shaded 

treatments recover their polyphenol concentrations and reach a similar concentration as the control 

post the shade treatment application (post - 5 WAFB). 

I would also like to reflect on my time as a PhD student over the last five years. Some of 

the lessons I have learnt is that nature is a force to be reckoned with and can waylay your best laid 

plans. Our early tree shading experiment with the French bittersweet cultivar ‘Medaille D’Or’ was 

completely devastated by the fire blight pathogen Erwinia amylovora and we had to remove two 

whole rows of this cultivar, and with it our experiment. COVID-19 also delayed some of our work. 

I faced quite a few setbacks with some of my experiments which were all good learning 

experiences. Some of them included not finding any significant genome wide association study 

hits for polyphenols when phenotyping an F1 seedling population of a cross between a high 

polyphenol M. sieversii accession and a low polyphenol fresh-market apple Royal Gala. Despite 

the setbacks, I got to work with a wonderful team of people at the Peck Lab which alleviated some 

of the pressure points during my PhD and I hope my work has broadened our understanding of the 



 

164 

 

accumulation of individual polyphenol compounds throughout the growing season, molecular 

controls of polyphenols in cider apples, source sink relationship of carbohydrates and polyphenols, 

and laid the foundation for more work on a genetic marker based acidity classification system for 

cider apples, apart from helping to identify potential transcription factors involved in 

proanthocyanidin regulation in fruit. The American cider industry is burgeoning and yearning to 

learn more to adapt and grow cultivars suitable for making hard cider. I hope my research adds to 

this growing field in support of the cider apple and hard cider industry. 

References 

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https://doi.org/10.1023/A:1003769304778.  

 

Henry-Kirk, R.A., McGhie, T.K., Andre, C.M., Hellens, R.P., Allan, A.C. 2012. Transcriptional 

analysis of apple fruit proanthocyanidin biosynthesis. J. Exp. Bot. 63(15):5437–5450. 

https://doi.org/10.1093/jxb/ers193.  

 

Karl, A.D., Peck, G.M. 2022. Greater sunlight exposure during early fruit development increases 

polyphenol concentration, soluble solid concentration, and fruit mass of cider apples. 

Horticulturae. 8(11):993. https://doi.org/10.3390/horticulturae8110993.  

 

Renard, C.M.G.C., Dupont, N., Guillermin, P. 2007. Concentrations and characteristics of 

procyanidins and other phenolics in apples during fruit growth. Phytochemistry 68(8):1128–1138. 

https://doi.org/10.1016/j.phytochem.2007.02.012.  

 

Rymenants, M., van de Weg, E., Auwerkerken, A., De Wit, I., Czech, A., Nijland, B., Heuven, H., 

De Storme, N., Keulemans, W. 2020. Detection of QTL for apple fruit acidity and sweetness using 

sensorial evaluation in multiple pedigreed full-sib families. Tree Genet. Genomes 16(5):1-17. 

https://doi.org/10.1007/s11295-020-01466-8.  

 

Zakalik D., Peck, G.M. 2023. High-tannin apple supply and demand in North America: results 

from a 2021 cider industry survey. Fruit Q. 31(2):30–35. 

 

Zakalik, D. L., Brown, M. G., Peck, G. M. 2023. Fruitlet thinning improves juice quality in seven 

high-tannin cider cultivars. HortScience 58(10):1119-1128. 

https://doi.org/10.21273/HORTSCI17096-23.  

 

https://doi.org/10.1023/A:1003769304778
https://doi.org/10.1093/jxb/ers193
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Appendix i 

Chapter 2 

 

Supplementary Table 2.1 The cider apple accessions (n=217) from the USDA Malus collection in Geneva, NY that were used in this 

study. The accessions were phenotyped for titratable acidity (TA) and pH, and genotyped for the Ma1 and Q8 alleles. The PI number is 

the unique identification number assigned by the USDA. The MUNQ number is the Malus UNiQue genotype code based on single 

sequence repeat (SSR) data (Denancé et al., 2020). The starch pattern index (SPI), pH, and TA values are a mean of three biological 

replicates over each year the accession was harvested. The Ma1 and Q8 markers are unable to distinguish the third allele in heterozygous 

triploid accessions, thus ‘-‘ signifies a missing allele. The table is arranged by ascending values of TA, followed by ploidy level. 
 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

Boutteville 162723 n/a 2 mama Q8Q8 1.09 5 27 Sept. 2018 8 France 

Sweet Coppin 589688 2574 2 mama Q8Q8 1.13 4.4 06 Oct. 2017, 05 Oct. 2016 7.1 England 

Stembridge Jersey 589693 1455 2 Mama Q8q8 1.15 4.7 
15 Sept. 2017, 07 Sept. 

2018, 20 Sept. 2019 
7.5 England 

Taylor's 589663 2572 2 mama Q8q8 1.19 5.1 
21 Sept. 2017, 17 Sept. 

2018 
6.7 England 

Harry Masters 

Jersey 
589653 2589 2 mama Q8q8 1.21 4.7 07 Sept. 2018 6.4 England 

Perthyre 589674 127 2 mama Q8Q8 1.21 4.6 
15 Sept. 2017, 07 Sept. 

2018 
8 England 

Dabinett 589073 802 2 mama Q8Q8 1.24 4.6 27 Sept. 2018 8 England 

Reinette De Cuzy 590136 1060 2 mama Q8Q8 1.33 4.4 
15 Sept. 2017, 16 Sept. 

2018 
5.1 France 

Le Bret 589690 n/a 2 mama Q8Q8 1.34 4.6 19 Oct. 2017, 19 Oct. 2018 7.4 England 

Holaart Doux 589585 2108 2 mama Q8Q8 1.35 4.8 
06 Oct. 2017, 25 Oct. 2018, 

30 Oct. 2019 
7.7 

Northern 

Europe 

Gala 392303 739 2 Mama Q8q8 1.36 4.2 27 Sept. 2018 7.8 
New 

Zealand 

Ellis Bitter 589650 2593 2 mama Q8Q8 1.4 4.6 
01 Sept. 2017, 24 Aug. 

2018, 04 Sept. 2019 
7.8 England 



 

166 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

PRI 1744-1 589789 n/a 2 mama Q8Q8 1.4 4.6 
15 Sept. 2017, 15 Aug. 

2018 
7.2 

North 

America 

Binet Blanc 122598 2358 2 mama Q8Q8 1.41 4.6 29 Sept. 2017, 24 Oct. 2019 7.5 France 

Sweet Alford 589081 n/a 2 mama Q8Q8 1.41 4.4 06 Oct. 2017, 05 Oct. 2018 6 England 

Bedan des Parts 123733 2377 2 mama Q8Q8 1.43 4.7 19 Oct. 2018 5.3 France 

Coat Jersey 589175 2598 2 mama Q8Q8 1.45 4.6 
15 Sept. 2017, 18 Sept. 

2019 
8 England 

Colozette 162712 2357 2 mama Q8q8 1.48 4.1 12 Oct. 2018 4.7 France 

Frequin 162503 6564 2 mama Q8Q8 1.48 4.9 
21 Sept. 2017, 24 Sept. 

2019 
7.7 France 

Binet Rouge 158730 2360 2 mama Q8Q8 1.54 4.6 26 Oct. 2017, 12 Oct. 2018 5.6 France 

Frequin Audievre 161838 n/a 2 MaMa Q8Q8 1.61 4.8 05 Oct. 2018 7.7 France 

Amer Gauthier 136243 6548.1 2 mama Q8Q8 1.64 4.6 19 Oct. 2017, 30 Oct. 2019 8 France 

American Forestier 173978 4779 2 mama Q8Q8 1.64 4.5 06 Oct. 2017, 12 Oct. 2018 7.6 France 

Dekkers Glorie 188515 2497 2 Mama q8q8 1.65 4.3 
21 Sept. 2017, 17 Sept. 

2018 
7.3 

Northern 

Europe 

Repinaldo do 

Liebana 
105528 n/a 2 mama Q8Q8 1.66 4.5 29 Sept. 2017, 09 Oct. 2019 7.5 Spain 

Dunkerton Late 

Sweet 
589666 2595 2 mama Q8q8 1.67 4.7 19 Oct. 2017, 19 Oct. 2018 6.5 England 

Pepa 680620 n/a 2 mama Q8Q8 1.68 4.4 05 Oct. 2018, 30 Oct. 2019 7.5 Spain 

Pommier Llorca 240817 n/a 2 mama Q8Q8 1.72 4.8 
01 Sept. 2017, 07 Sept. 

2018 
7.4 

Northern 

Africa 

Damelot 162722 4696 2 mama Q8q8 1.73 4.6 
19 Oct. 2017, 19 Oct. 2018, 

24 Oct. 2019 
7.4 France 

Fuero Rous 187352 n/a 2 mama Q8Q8 1.81 4.6 
06 Oct. 2017, 19 Oct. 2018, 

30 Oct. 2019 
6 France 

Marin Onfroy 173982 6559 2 mama Q8Q8 1.81 4.5 
15 Sept. 2017, 20 Sept. 

2019 
7.8 France 

Doux-AMR 122616 535 2 mama Q8q8 1.85 4.5 29 Sept. 2017, 09 Oct. 2019 7.8 France 

Jouveaux 162731 326 2 Mama Q8Q8 1.89 4.3 
21 Sept. 2017, 12 Oct. 

2018, 24 Oct. 2019 
7.7 France 



 

167 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

Coloradona 681633 n/a 2 Mama Q8Q8 1.9 4.2 
21 Sept. 2017, 27 Sept. 

2018, 20 Sept. 2019 
6.6 Spain 

Manch Rouge 162716 n/a 2 Mama Q8Q8 1.9 4.2 05 Oct. 2018, 15 Oct. 2019 7 France 

Muscadet de Lense 173985 4625 2 Mama Q8Q8 1.9 4.2 
15 Aug. 2018, 20 Sept. 

2019 
7.6 France 

Doux Normandie 589667 2597 2 Mama Q8q8 1.94 4.2 29 Sept. 2017, 12 Oct. 2018 7.6 France 

Muscadet de 

Dieppe 
589493 2213 2 Mama Q8Q8 1.95 4.6 

16 Sept. 2018, 24 Sept. 

2019 
6.7 France 

Amere de 

Berthecourt 
127311 1624 2 Mama Q8Q8 1.98 4.4 

29 Sept. 2017, 27 Sept. 

2018, 24 Sept. 2019 
7.9 France 

Royal Jersey 175545 2549 2 Mama Q8Q8 1.99 4.4 06 Oct. 2017 7.9 England 

Major 150649 6559 2 Mama Q8Q8 2.03 4.5 06 Oct. 2017, 27 Sept. 2018 7.9 England 

Michelin 589670 535 2 mama Q8q8 2.05 4.3 
15 Sept. 2017, 27 Sept. 

2018 
7.8 France 

C'Huero Biz Bras 187297 n/a 2 mama Q8Q8 2.06 4.6 
21 Sept. 2017, 20 Sept. 

2019 
6.9 France 

Frequin Tardive de 

la Sarthe 
589689 367 2 mama Q8Q8 2.07 4.4 26 Oct. 2017, 24 Oct. 2019 7.6 France 

Pomme Thoury 162548 4184 2 mama Q8Q8 2.08 4.2 
21 Sept. 2017, 16 Sept. 

2018, 24 Oct. 2019 
7.6 France 

Tale Sweet 589691 2573 2 mama Q8Q8 2.08 4.3 12 Oct. 2018, 30 Oct. 2019 6.5 England 

Twistbody Jersey 175551 2561 2 mama Q8Q8 2.08 4.4 
15 Sept. 2017, 18 Sept. 

2019 
7.8 England 

Belle de Crollon 162544 4625 2 mama Q8Q8 2.09 4.2 
06 Oct. 2017, 05 Oct. 2018, 

24 Oct. 2019 
7.9 France 

Fillbarrel 589679 2592 2 mama Q8Q8 2.16 4.2 29 Sept. 2017, 30 Oct. 2019 7.9 England 

Frequin Lacaille 247314 6644.1 2 mama Q8Q8 2.16 4.4 
21 Sept. 2017, 27 Sept. 

2018, 09 Oct. 2019 
8 France 

Crow Egg 589196 4674 2 Mama Q8Q8 2.18 4 19 Oct. 2017, 12 Oct. 2018 7.3 
North 

America 

White Jersey 175553 2568 2 mama Q8Q8 2.21 4.5 
21 Sept. 2017, 07 Sept. 

2018, 09 Oct. 2019 
7.1 England 

Bramtot 158731 6557.1 2 mama Q8Q8 2.22 4.3 06 Oct. 2017, 05 Oct. 2018 6.8 France 

Muscadet Bernay 200780 4625 2 mama Q8Q8 2.23 4.2 19 Oct. 2018, 30 Oct. 2019 7.7 France 



 

168 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

Launette 162732 n/a 2 mama Q8Q8 2.28 4.3 
29 Sept. 2017, 27 Sept. 

2018, 15 Oct. 2019 
7.3 France 

Tardive Forestier 175548 723 2 mama Q8Q8 2.3 4.13 
29 Sept. 2017, 17 Sept. 

2018, 09 Oct. 2019 
7.9 France 

Pomme Framboise 131975 345 2 Mama Q8q8 2.33 3.8 
01 Sept. 2017, 15 Aug. 

2018 
8 

Central 

Europe 

La Paix 589598 488 2 Mama Q8q8 2.42 3.9 
15 Sept. 2017, 07 Sept. 

2018 
7.2 

Central 

Europe 

Medaille d'Or 594108 256 2 mama Q8Q8 2.42 4.3 
29 Sept. 2017, 27 Sept. 

2018 
7.5 France 

Daux Belan 162062 4708 2 mama Q8Q8 2.51 4.5 
06 Oct. 2017, 05 Oct. 2018, 

24 Oct. 2019 
6.4 France 

Geeveston Fanny 589123 4674 2 Mama Q8Q8 2.51 4 26 Oct. 2017, 05 Oct. 2018 7.8 Australia 

Djulabia 264689 n/a 2 Mama Q8Q8 2.56 3.8 
21 Sept. 2017, 17 Sept. 

2018 
n/a 

Central 

Europe 

Hubbardston 

Nonsuch 
589202 941 2 Mama Q8q8 2.66 3.8 27 Sept. 2018 5.5 

North 

America 

Rousse Latour 136604 n/a 2 mama Q8Q8 2.69 4.1 01 Sept. 2017, 24 Oct. 2019 5.8 France 

Noel Deschamps 173986 4728 2 mama Q8Q8 2.72 4.2 19 Oct. 2017, 24 Oct. 2019 7.8 France 

Reinette Jaune De 

Butzel 
131978 n/a 2 Mama Q8q8 3.01 3.9 27 Sept. 2018 7.3 

Central 

Europe 

Improved 

Lambrook Pippin 
589682 1454 2 Mama Q8Q8 3.1 3.8 

15 Sept. 2017, 12 Sept. 

2019 
7 England 

Empire 588842 656 2 MaMa Q8q8 3.11 3.6 19 Oct. 2018 7.3 
North 

America 

Bella de Jardins 105498 96 2 Mama Q8Q8 3.12 3.7 
21 Sept. 2017, 27 Sept. 

2018 
7.8 France 

Hudson's Golden 

Gem 
590157 6528 2 Mama q8q8 3.14 4 25 Oct. 2018 7.3 

North 

America 

Blahova Oranzova 

Renetor 
341067 931 2 Mama Q8q8 3.15 4 21 Sept. 2017, 05 Oct. 2018 7.9 

Central 

Europe 

Binet Blanc Dore 158729 n/a 2 mama Q8Q8 3.16 4.1 
26 Oct. 2017, 05 Oct. 2018, 

30 Oct. 2019 
7.3 France 

Royal Gala 651008 739.2 2 Mama Q8q8 3.24 3.8 25 Aug. 2018 1.3 
New 

Zealand 

Margil 264558 732 2 Mama Q8q8 3.46 4 26 Oct. 2017, 05 Oct. 2018 7.2 n/a 



 

169 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

Langworthy 161851 2505 2 Mama Q8Q8 3.61 3.6 06 Oct. 2017 8 England 

Ben Davis 588953 106 2 Mama q8q8 3.64 3.7 19 Oct. 2017, 14 Nov. 2018 7 
North 

America 

Macoun 589895 1055.1 2 Mama Q8Q8 3.72 3.6 17 Sept. 2018 5.7 
North 

America 

Redstreak 175543 n/a 2 MaMa Q8q8 3.9 3.9 25 Oct. 2018 7 England 

White Winter 

Pearmain 
613887 50.1 2 Mama Q8Q8 3.9 3.7 26 Oct. 2017, 02 Nov. 2018 7 

North 

America 

Pohorka 588745 2699 2 Mama Q8q8 3.93 3.6 06 Oct. 2017 8 
Former 

USSR 

Gewurzluiken 132225 315 2 Mama Q8q8 3.95 3.7 29 Sept. 2017 8 
Central 

Europe 

Kingston Black 589703 632 2 Mama Q8Q8 3.97 3.8 
21 Sept. 2017, 07 Sept. 

2018, 09 Oct. 2019 
6.7 England 

Belle Sans Pepin 588951 2705 2 Mama Q8Q8 3.98 3.8 26 Oct. 2017, 02 Nov. 2018 7.6 France 

Jefferis 589185 2865 2 Mama Q8Q8 3.98 3.7 
21 Sept. 2017, 07 Sept. 

2018 
5.8 

North 

America 

Lande 162724 67.2 2 Mama Q8Q8 4 3.7 21 Sept. 2017, 12 Oct. 2018 6.3 France 

Herring's Pippin 136001 82 2 MaMa Q8Q8 4.02 3.6 15 Aug. 2018 8 England 

Cortland 588848 503 2 Mama Q8Q8 4.1 3.6 12 Oct. 2018 5 
North 

America 

Northern Spy 588872 23 2 Mama Q8q8 4.26 3.5 06 Oct. 2017 8 
North 

America 

Drap d'Or 

Guemene 
131823 92 2 Mama Q8q8 4.31 3.7 06 Oct. 2017, 05 Oct. 2018 7.6 France 

Belle Fleur Rouge 245048 n/a 2 Mama Q8Q8 4.36 3.5 05 Oct. 2018 6.7 France 

American Summer 

Pearmain 
589214 2986 2 Mama q8q8 4.41 3.6 07 Sept. 2018 7.4 

North 

America 

Grimes Golden 588791 3190 2 Mama Q8q8 4.41 3.6 02 Nov. 2018 5 
North 

America 

Reine des Reinettes 

x 82 
279325 732 2 Mama Q8q8 4.44 3.7 

21 Sept. 2017, 27 Sept. 

2018 
7.4 France 

Fenouillet de 

Ribours 
590126 255 2 Mama Q8Q8 4.49 3.6 

29 Sept. 2017, 12 Oct. 

2018, 09 Oct. 2019 
5.3 France 



 

170 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

Golden Delicious 590184 65 2 Mama Q8q8 4.49 3.7 21 Sept. 2017, 12 Oct. 2018 6.8 
North 

America 

Pomme Raisin 134669 670 2 Mama Q8Q8 4.52 3.5 
15 Sept. 2017, 27 Sept. 

2018 
7.2 

Central 

Europe 

Cristalina 681639 n/a 2 Mama Q8Q8 4.62 3.7 25 Oct. 2018, 30 Oct. 2019 6.2 Spain 

Foxwhelp 

(misidentified) 
589318 n/a 2 Mama Q8Q8 4.62 3.5 

21 Sept. 2017, 07 Sept. 

2018, 09 Oct. 2019 
7.6 England 

Stoke Red 589697 2575 2 Mama Q8q8 4.62 3.7 17 Sept. 2018, 09 Oct. 2019 7.7 England 

Reine des Reinettes 

x 1700 
279326 37 2 Mama Q8Q8 4.64 3.7 06 Oct. 2017, 27 Sept. 2018 7.5 France 

Vagnon Ascher 175552 273 2 Mama Q8Q8 4.65 3.8 
29 Sept. 2017, 12 Oct. 

2018, 09 Oct. 2019 
7.5 England 

Golden Russet 589892 2862 2 Mama Q8q8 4.72 3.6 25 Oct. 2018 5.7 n/a 

Sunset 589694 164 2 Mama Q8q8 4.82 3.4 17 Sept. 2018 6.5 England 

Winesap 588799 447 2 Mama Q8q8 4.9 3.6 26 Oct. 2017, 02 Nov. 2018 4.8 
North 

America 

Pigeonnet Rouge 132273 1509 2 Mama Q8Q8 4.96 3.5 
21 Sept. 2017, 27 Sept. 

2018, 18 Sept. 2019 
6.9 France 

Reine des Pommes 132571 285 2 Mama Q8Q8 5.05 3.7 19 Oct. 2017 8 France 

Perico 680621 n/a 2 MaMa Q8Q8 5.14 3.6 25 Oct. 2018, 24 Oct. 2019 4.9 Spain 

Marshall McIntosh 588998 508 2 Mama Q8Q8 5.16 3.5 
21 Sept. 2017, 17 Sept. 

2018 
6.6 

North 

America 

Red Ralls 437047 546 2 Mama Q8Q8 5.18 3.6 19 Oct. 2017, 19 Oct. 2018 7.5 
Central 

Europe 

Hubbards Pearmain 590130 2476 2 Mama Q8Q8 5.24 3.6 
29 Sept. 2017, 27 Sept. 

2018 
7.8 England 

Stembridge Cluster 589692 1454 2 Mama Q8q8 5.32 3.5 
29 Sept. 2017, 27 Sept. 

2018, 09 Oct. 2019 
7 England 

De La Riega 680619 n/a 2 MaMa Q8Q8 5.33 3.5 12 Oct. 2018, 24 Oct. 2019 6.5 Spain 

Esopus Spitzenburg 588785 522 2 Mama Q8Q8 5.33 3.5 19 Oct. 2017, 19 Oct. 2018 7 
North 

America 

Thorgauer 

Weinapfel 
506361 1333 2 Mama Q8Q8 5.37 3.6 21 Sept. 2017, 12 Oct. 2018 6.8 

Central 

Europe 

Court Pendu Rose 589587 105 2 Mama Q8Q8 5.39 3.7 19 Oct. 2017 7 France 



 

171 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

McIntosh 

Summerland Red 
588817 508 2 Mama Q8Q8 5.4 3.4 

01 Sept. 2017, 16 Sept. 

2018 
6.8 

North 

America 

Friandise 590127 378 2 Mama Q8Q8 5.43 3.6 21 Sept. 2017, 25 Oct. 2018 5.1 
Northern 

Europe 

Bella di Pontoise 102537 87 2 Mama Q8q8 5.5 3.5 19 Oct. 2018 6.3 France 

Reinette Da Mana 322032 131 2 Mama Q8Q8 5.52 3.6 25 Oct. 2018 6.5 France 

Court Pendu Gris 589602 105 2 Mama Q8Q8 5.56 3.5 06 Oct. 2017, 19 Oct. 2018 5.3 France 

Landsberger 

Reinette 
589565 61 2 MaMa Q8Q8 5.56 3.5 

15 Sept. 2017, 16 Sept. 

2018 
7.4 

Central 

Europe 

Renetta Dorata 104034 38.1 2 Mama Q8Q8 5.63 3.5 05 Oct. 2018 5.4 
Southern 

Europe 

Liberty 588943 585 2 Mama Q8Q8 5.72 3.4 12 Oct. 2018 6.6 
North 

America 

Blanquina 681637 n/a 2 MaMa Q8Q8 5.79 3.5 05 Oct. 2018, 30 Oct. 2019 7 Spain 

Edelroter 590125 173 2 Mama Q8q8 5.79 3.7 06 Oct. 2017, 27 Sept. 2018 7.3 
Central 

Europe 

Pomme Grise 589242 n/a 2 Mama Q8q8 5.79 3.5 25 Oct. 2018 6.6 
North 

America 

Arkansas Black 589117 44 2 Mama Q8Q8 5.8 3.5 06 Oct. 2017, 02 Nov. 2018 6 
North 

America 

D'Arcy Spice 590122 1923 2 Mama Q8q8 5.85 3.5 05 Oct. 2018 3.2 England 

Clear Heart 206022 2733 2 Mama Q8Q8 5.92 3.6 07 Sept. 2018 7.1 
Northern 

Europe 

Collaos 666188 n/a 2 MaMa Q8Q8 5.95 3.6 05 Oct. 2018 5.3 Spain 

Teign Harvey 175549 2545 2 Mama Q8Q8 6 3.5 
21 Sept. 2017, 17 Sept. 

2018, 20 Sept. 2019 
6.6 England 

Golden Pippin 590129 1441 2 Mama Q8Q8 6.01 3.4 
21 Sept. 2017, 27 Sept. 

2018 
6.4 n/a 

Yellow Bellflower 589195 77 2 Mama Q8q8 6.07 3.6 19 Oct. 2017, 12 Oct. 2018 8 
North 

America 

Cap of Liberty 161830 n/a 2 Mama Q8q8 6.13 3.5 06 Oct. 2017, 10 Oct. 2019 7.8 England 

Weidners 

Goldreinette 
590143 3040 2 Mama Q8Q8 6.14 3.4 

21 Sept. 2017, 27 Sept. 

2018 
6.7 

Central 

Europe 

Jonathan 590185 57 2 Mama Q8q8 6.16 3.4 29 Sept. 2017, 05 Oct. 2018 7.7 
North 

America 



 

172 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

Skyrme's Kernel 161846 n/a 2 Mama Q8q8 6.19 3.4 
21 Sept. 2017, 17 Sept. 

2018, 09 Oct. 2019 
6.3 England 

Reinette Franche 590137 278 2 Mama Q8Q8 6.22 3.5 
26 Oct. 2017, 12 Oct. 2018, 

24 Oct. 2019 
7.8 France 

Grenadier 589684 93 2 Mama Q8Q8 6.23 3.4 
15 Sept. 2017, 07 Sept. 

2018 
7.5 England 

Pigeonnet Blanc 132272 n/a 2 Mama Q8q8 6.23 3.3 19 Oct. 2018 7 France 

Blue Pearmain 590180 n/a 2 Mama Q8q8 6.26 3.6 06 Oct. 2017, 27 Sept. 2018 6.2 
North 

America 

Belle de 

Nordhaussen 
589584 99 2 Mama Q8Q8 6.3 3.3 27 Sept. 2018 7.7 

Central 

Europe 

Yellow Newtown 588773 787 2 Mama Q8Q8 6.4 3.5 29 Sept. 2017, 25 Oct. 2018 6.7 
North 

America 

Cheddar Cross 589656 2696 2 Mama Q8Q8 6.41 3.4 15 Aug. 2018 7.8 England 

Court Pendu 589601 105 2 Mama Q8Q8 6.42 3.4 05 Oct. 2018 6 France 

Weisser Winter 

Taffetapfel 
590144 234 2 Mama Q8Q8 6.44 3.6 06 Oct. 2017, 05 Oct. 2018 7.3 

Central 

Europe 

Reinette do Chenee 131828 n/a 2 Mama Q8Q8 6.48 3.6 06 Oct. 2017 7.8 
Central 

Europe 

Lambrook Pippin 161839 n/a 2 Mama Q8Q8 6.5 3.4 12 Oct. 2018 7.7 England 

Old Pearmain 590133 598 2 Mama Q8q8 6.57 3.4 
21 Sept. 2017, 17 Sept. 

2018 
6.5 England 

Raxao 681640 n/a 2 Mama Q8Q8 6.65 3.4 
29 Sept. 2017, 07 Sept. 

2018, 24 Oct. 2019 
7.8 Spain 

Redfield 589211 n/a 2 Mama Q8q8 6.68 3.4 29 Sept. 2017, 25 Oct. 2018 7.8 
North 

America 

Nanot 161763 n/a 2 Mama Q8q8 6.81 3.2 21 Sept. 2017, 09 Oct. 2019 8 France 

Reinette Grise 589588 2114 2 Mama Q8Q8 6.84 3.5 23 Aug. 2018 6.9 France 

Dufflin 175542 2554 2 Mama Q8Q8 6.86 3.4 07 Sept. 2018 7.2 England 

Reinette Clochard 589444 118 2 Mama Q8Q8 6.9 3.5 19 Oct. 2017, 19 Oct. 2018 6.1 France 

Bonne-Hotture 590120 124 2 Mama Q8q8 7.07 3.5 06 Oct. 2017, 05 Oct. 2018 7 France 

Grosse Mouche 162545 n/a 2 Mama Q8Q8 7.23 3.4 06 Oct. 2017 8 France 

Reineta do Caravia 105524 n/a 2 Mama Q8Q8 7.24 3.4 06 Oct. 2017, 19 Oct. 2018 6.1 Spain 



 

173 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

Finkenwerder 

Herbstprinz 
651010 2889 2 MaMa Q8Q8 7.27 3.4 12 Oct. 2018 6.1 

Central 

Europe 

Calville Blanc 589596 95 2 Mama Q8q8 7.39 3.4 25 Oct. 2018 7 France 

Reinette Thouin 590140 134 2 Mama Q8Q8 7.41 3.5 06 Oct. 2017, 12 Oct. 2018 7.9 France 

Inducoa No. II 613830 16 2 Mama Q8Q8 7.49 3.5 27 Sept. 2018 7.8 Spain 

Ross Nonpareil 590141 2100 2 Mama Q8q8 7.58 3.6 
06 Oct. 2017, 19 Oct. 2018, 

09 Oct. 2019 
6.3 n/a 

Golden Harvey 590128 2841 2 Mama Q8Q8 7.79 3.5 21 Sept. 2017, 19 Oct. 2018 7.2 England 

Pomme Cloche 134668 85 2 MaMa Q8Q8 7.89 3.2 29 Sept. 2017, 12 Oct. 2018 7.3 
Central 

Europe 

Champagne 

Reinette 
264688 91 2 Mama Q8Q8 8.07 3.3 06 Oct. 2017, 19 Oct. 2018 7.5 France 

Sturmer Pippin 307382 5249 2 MaMa Q8Q8 8.34 3.3 26 Oct. 2017, 14 Nov. 2018 5.4 England 

William Crump 589309 333 2 Mama Q8Q8 8.34 3.3 15 Sept. 2017, 19 Oct. 2018 6.4 England 

Edward VII 392312 74 2 MaMa Q8Q8 8.36 3.2 19 Oct. 2017, 19 Oct. 2018 7.7 England 

Lorna Doone 161840 2694 2 Mama Q8q8 8.45 3.3 24 Aug. 2018 5.4 England 

Lombart's Calville 589920 9 2 Mama Q8Q8 8.61 3.4 05 Oct. 2018 6.3 
Northern 

Europe 

Wickson 613818 n/a 2 MaMa Q8Q8 8.69 3.5 19 Oct. 2017, 25 Oct. 2015 7.2 
North 

America 

King David 589156 71 2 MaMa Q8q8 8.71 3.3 12 Oct. 2018 6.6 
North 

America 

Xuanina 680623 n/a 2 MaMa Q8Q8 8.71 3.3 12 Oct. 2018, 30 Oct. 2019 7.3 Spain 

Vista Bella 588819 366 2 Mama Q8Q8 8.75 3.4 21 Sept. 2017 8 
North 

America 

Court Pendu Plat 123960 105.2 2 Mama Q8Q8 8.88 3.6 25 Oct. 2018 4.8 France 

Toreno 245145 n/a 2 MaMa Q8Q8 9 3.3 19 Oct. 2018 3.7 Spain 

Pigeonnet 589597 2112 2 MaMa Q8Q8 9.01 3.3 05 Oct. 2018 6.2 France 

Tremlett's Bitter 

(misidentified) 
175550 n/a 2 Mama Q8Q8 9.07 3.3 21 Sept. 2017, 12 Oct. 2018 7.8 n/a 

Boche 162549 n/a 2 Mama Q8Q8 9.39 3.2 21 Sept. 2017 7.9 France 



 

174 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

Zuccamaglio 589569 56 2 Mama Q8Q8 10.89 3.4 05 Oct. 2018 5.8 
Central 

Europe 

Isle of Wight 

Pippin 
590131 n/a 2 Mama Q8Q8 11.36 3.4 19 Oct. 2019 6.8 England 

Court Royal 589671 n/a 3 mamama Q8Q8Q8 1.43 4.5 29 Sept. 2017 7.8 England 

Miron Sacharanij 154164 n/a 3 mamama Q8Q8Q8 1.61 4.3 15 Aug. 2018 8 
Central 

Europe 

Domaine 173979 n/a 3 mamama Q8Q8Q8 1.66 4.6 12 Oct. 2018 7.4 France 

Arkansas 588952 n/a 3 mamama Q8Q8Q8 1.84 4.6 
21 Sept. 2017, 27 Sept. 

2018, 20 Sept. 2019 
7.6 

North 

America 

Belle Fille 162709 n/a 3 Mamama Q8Q8Q8 1.88 4.6 
29 Sept. 2017, 16 Sept. 

2018, 26 Sept. 2019 
7.6 France 

C’Huero Ru Bienn 187298 n/a 3 Mama- Q8Q8Q8 1.9 4.5 26 Oct. 2017, 12 Oct. 2018 7.5 France 

Bulmer Norman 588808 618 3 Mamama Q8Q8Q8 1.96 4.4 
21 Sept. 2017, 16 Sept. 

2018, 20 Sept. 2019 
7.2 France 

Doucet Rouge 161760 n/a 3 mamama Q8Q8Q8 2.03 4.2 
15 Aug. 2018, 20 Sept. 

2019 
7.5 France 

Nehou 175544 n/a 3 mamama Q8Q8Q8 2.21 4.5 17 Sept. 2018 7.3 France 

Gros Frequin 131105 n/a 3 mamama Q8Q8Q8 2.26 4.2 27 Sept. 2018, 09 Oct. 2019 7 France 

Surpasse Frequin 125566 n/a 3 mamama Q8Q8Q8 2.58 4 18 Sept. 2019 7.9 France 

Teint Fraise 127370 n/a 3 mamama Q8Q8Q8 2.65 4.2 
06 Oct. 2017, 19 Oct. 2018, 

30 Oct. 2019 
7.6 France 

Doux Tardif 162715 n/a 3 mamama Q8Q8Q8 2.69 4 12 Oct. 2018, 30 Oct. 2019 6.2 France 

Peau De Vache 136489 n/a 3 mamama Q8Q8Q8 3.17 4.1 05 Oct. 2018, 24 Oct. 2019 8 France 

Double Bon 

Pommier 
131104 n/a 3 Mama- Q8q8- 3.31 4.4 21 Sept. 2017 8 France 

Notaire 137094 n/a 3 Mama- Q8q8- 3.88 4 
21 Sept. 2017, 27 Sept. 

2018 
7.1 France 

Stayman 588975 117.2 3 Mama- Q8q8- 4.28 3.6 19 Oct. 2017, 02 Nov. 2018 5.9 
North 

America 

Rott Jarnpple 102148 n/a 3 Mama- Q8q8- 4.37 3.5 21 Sept. 2017 7 
Central 

Europe 

Mutsu 223602 874.2 3 Mama- Q8q8- 4.73 3.5 15 Sept. 2017, 19 Oct. 2018 6.4 Japan 



 

175 

 

Accession name 
PI 

(no.) 

MUNQ 

(no.)
z
 

Ploidy Ma1 Q8 
TA 

(g·L-1) 
pH Date(s) harvested SPI 

Region 

of origin 

Smokehouse 589903 n/a 3 Mama- Q8q8- 4.79 3.5 05 Oct. 2018 6 
North 

America 

Ribston 588840 907.2 3 Mama- Q8q8- 4.86 3.6 21 Sept. 2017, 7 Sept. 2018 7.3 England 

Reinette Jamin 135645 n/a 3 Mama- Q8Q8Q8 4.97 3.6 
29 Sept. 2017, 27 Sept. 

2018 
7.9 France 

Reinette van 

Ekenstein 
188521 n/a 3 Mama- Q8Q8Q8 5.06 3.5 

21 Sept. 2017, 17 Sept. 

2018 
7.5 

Northern 

Europe 

Rhode Island 

Greening 
589520 n/a 3 Mama- Q8Q8Q8 5.59 3.5 26 Oct. 2017, 19 Oct. 2018 7.5 

North 

America 

Reinette Jaeghers 131561 n/a 3 Mama- Q8q8- 6.2 3.5 15 Sept. 2017, 12 Oct. 2018 7.9 
Central 

Europe 

Tom Putt 125271 n/a 3 Mama- Q8Q8Q8 6.37 3.4 06 Oct. 2017 6.6 England 

Vandevere 589060 n/a 3 Mama- Q8Q8Q8 6.56 3.4 19 Oct. 2017, 12 Oct. 2018 7.3 
North 

America 

Ashmead's Kernel 589654 1546 3 Mama- Q8q8- 6.7 3.4 19 Oct. 2017, 19 Oct. 2018 6 England 

Roxbury Russet 588971 n/a 3 Mama- Q8Q8Q8 6.76 3.4 19 Oct. 2017, 25 Oct. 2018 6.6 
North 

America 

Reinette Ontz 590139 n/a 3 Mama- Q8Q8Q8 7.79 3.3 19 Oct. 2018 6.2 England 

Canavial-14 183961 n/a 3 Mama- Q8Q8Q8 7.93 3.4 26 Oct. 2017, 02 Nov. 2018 5.7 
Central 

Europe 

Rouge Belle De 

Boskoop 
589143 886.2 3 Mama- Q8q8- 9.07 3.4 07 Sept. 2018 7.7 France 

Reinette Tres 

Tardive 
162741 890 3 Mama- Q8Q8Q8 10.73 3.2 

19 Oct. 2017, 12 Oct. 2018, 

09 Oct. 2019 
5 France 

Reinette D'Anjou 590135 n/a 3 Mama- Q8q8- 11.17 3.3 12 Oct. 2018 6.7 France 

Piel De Sapo 681634 913 3 Mama- Q8Q8Q8 11.53 3.2 14 Nov. 2018 5 Spain 
 

zValue ending in ".1" doubt regarding MUNQ value due to small differences in SSR genotypes. Value ending in ".2" MUNQ attributed only because of name. 

Value ending in ".3" MUNQ attributed only because of name, but the pedigree is consistent between SSR data and SNP data.



 

176 

 

 

 

 

Supplementary Figure 2.1 Flowchart illustrating the selection process for the cider apple 

accessions (n=217) from the USDA Malus collection in Geneva, NY that were used in this study. 

 



 

177 

 

 

 

 

Supplementary Figure 2.2 The variation pH for each of the combinations of Ma1 and Q8 alleles 

among (A) diploid accessions (n=181) and (B) triploid accessions (n=37) between 2017-19 from 

the USDA Malus collection in Geneva, NY. There were no mama-q8q8 or MaMa-q8q8 gene 

combinations. The blue dots represent the estimated marginal means for each accession. The red 

plus signs represent outliers as defined by the estimated marginal means test. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



 

178 

 

Appendix ii 

Chapter 3  

Supplementary Tables 3.1 and 3.2 can be found at https://doi.org/10.7298/kksr-7928 
 

 

 

Supplementary Figure 3.1 Regression of fall crop density and A) yield, and B) return bloom 

density for ‘Porters Pefection’ and ‘Binet Rouge’ at Cornell Orchards in a crop density experiment 

in 2021. Each point represents a single measurement of one tree. Trunk cross sectional area 

(TCSA)is measured 40 cm above the graft union.

27.8 fruit/cm2 28.4 fruit/cm2 

A B 

https://doi.org/10.7298/kksr-7928


 

179 

 

 

 

 

 

 

 

Supplementary Figure 3.2 Regression of select phenolic monomers from juice of Perfection’/’G.11’ and ‘Binet Rouge’/’G.11’ which 

were subject to four crop density treatments - low, medium, high, and UnThinned Control (UTC) in 2021. The low, medium, and high 

treatments were thinned to 5, 10, and 15 fruit/cm2 TCSA. Each point represents a single measurement of one tree.  

 

 

Binet Rouge 
Porter’s Perfection 



 

180 

 

 

 

 

 

Supplementary Figure 3.3 Estimated marginal means of mean degree of 

polymerization and average molecular weight of tannins for flesh and peel tissue of the 

cultivar ‘Porter’s Perfection’ subject to four crop density treatments -low, medium, 

high, and unthinned control (UTC) in 2021. Means comparison within each time point 

followed by the same lowercase letter are not significantly different based on Tukey’s 

HSD means comparison at α = 0.05. 

 

 

 

 

                      Flesh                                                                                     Peel                          



 

181 

 

 

 

 

 

 

 

 

 

 

 

 

Supplementary Figure 3.4 Estimated marginal means of individual polyphenol 

monomers comparing the unthinned control (UTC) and the low crop density treatment 

for flesh and peel tissue of the cultivar ‘Porter’s Perfection’. Means comparison within 

each time point followed by the same lowercase letter are not significantly different 

based on the Tukey’s HSD means comparison at α = 0.05. 

         Flesh                              Peel                          

         Peel Only                        

         Flesh Only                        



 

182 

 

Appendix iii 

Chapter 4 

 

 

Supplementary Table 4.1 Means of crop density and yield of ‘Porter’s Perfection’ and 

‘Binet Rouge’ subject to 30 and 60% Tree Shade (30TS and 60TS), 30 and 60% Fruit 

Shade (30FS and 60FS), and no shade (Control) in 2021. TCSA - Trunk cross-sectional 

area, TS – Tree Shade, FS – Fruit Shade. In 2020, yield and crop density measurements 

were not taken.  

zData is presented as means ± standard error. Each value represents the mean and 

standard error of ten experimental trees. 

 

 

Treatment Crop Density (fruit/cm2 TCSA) Yield (kg) 

 Porter’s 

Perfection 

Binet Rouge Porter’s 

Perfection 

Binet Rouge 

Control   8.3 ± 0.95z 13.2 ± 1.85 9.1 ± 0.96 8.1 ± 0.20 

30TS  8.0 ± 0.72 14.0 ± 1.34 7.9 ± 0.64 7.0 ± 0.66 

60TS  6.0 ± 0.90  9.0 ± 1.59 6.5 ± 0.60 4.8 ± 0.54 

30FS  3.1 ± 0.36  6.5 ± 0.58 4.8 ± 0.25 4.3 ± 0.11 

60FS  3.3 ± 0.31  6.4 ± 0.58 4.7 ± 0.34 4.1 ± 0.18