Spore Wars: Tools to Identify, Predict, and Prevent Dairy Spoilage

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Microbial spoilage is an important aspect of food loss and can occur in products that have been heat-treated and are stored refrigerated, such as dairy products. Routes of contamination for dairy spoilage organisms include presence in raw materials and survival during processing (generally Gram-positive sporeformers) and post-processing contamination (caused by Gram-negative bacteria, yeast and molds). Given the multiple contamination pathways across the dairy processing continuum, a holistic approach is required to address dairy spoilage. To identify, predict, and prevent dairy spoilage, the studies reported here focused on (i) the application of modern molecular approaches to understand the types of fungi in dairy products and facilitate source tracking along the processing continuum in a standardized method, (ii) the development of a stochastic model and a challenge study protocol to allow industry to better evaluate spoilage control strategies for post-pasteurization fungal contamination and assess the value of these strategies quantitatively, and (iii) the development of a stochastic model to understand the effect of sporeformer contamination over the entire processing continuum and quantitatively assess the effect of spoilage control strategies. Our data revealed that dairy-relevant fungi represent a broad diversity over multiple phyla. Molecular subtyping approaches, namely ITS sequencing, are a useful tool for fungal identification. Moreover, we demonstrated that ITS sequencing can be used for fungal contamination source tracking along the processing continuum, especially for over-represented subtypes. In our stochastic model based on mold post-pasteurization contamination of yogurt, we estimated consumer exposure to visible mold based on a proof-of-concept approach using air plate samples to estimate initial mold contamination rates. This model estimated that 550 +/- 25.2 consumers would be exposed to visible mold growth for every 1 million cups of yogurt produced when no fungal inhibitor was used in the yogurt formulation. Our challenge study protocol developed a method for industry to better evaluate novel spoilage control strategies, such as protective cultures, and revealed that the two protective cultures we evaluated retarded mold, but not yeast growth in Greek yogurt. Finally, our second stochastic model provided a way to model spoilage due to psychrotolerant sporeformers in fluid milk throughout the processing continuum, from the dairy farm to the end of shelf-life and predicted that the mean concentration of psychrotolerant sporeformers in fluid milk at 21 days of storage at 6C is 4.54 +/- 1.71 Log10CFU/mL. Our model also revealed ways to quantitatively assess intervention strategies (e.g., microfiltration) to reduce dairy spoilage through the use of what-if scenarios. Overall, these studies broaden our understanding of dairy spoilage organisms. The combination of molecular subtyping and stochastic modeling represents powerful tools the dairy industry can adopt to (i) achieve more accurate estimates of product spoilage and (ii) tailor spoilage control strategies based on data-driven evidence, thus providing a roadmap to reduce microbial spoilage of dairy products.

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Food science


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Wiedmann, Martin

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Grohn, Yrjo Tapio
Miller, Dennis Dean

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Food Science and Technology

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Ph. D., Food Science and Technology

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Doctor of Philosophy

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dissertation or thesis

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