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dc.contributor.authorBelias, Alexandra Marie
dc.date.accessioned2021-12-20T20:48:01Z
dc.date.available2021-12-20T20:48:01Z
dc.date.issued2021-08
dc.identifier.otherBelias_cornellgrad_0058F_12631
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:12631
dc.identifier.urihttps://hdl.handle.net/1813/110507
dc.description264 pages
dc.description.abstractMicrobial hazards, including Listeria monocytogenes, Salmonella, and Enterohemorrhagic Escherichia coli (EHEC), present complex challenges for the produce industry. The work here aimed to characterize these hazards and test relevant control strategies. A review was conducted to identify hazards, risks and challenges associated with Listeria along the food supply chain. This review showed Listeria is prevalent across various environments (e.g., processing environments) and presents both public health and business risks. As such, identification of the root causes of contamination and implementation of control measures are warranted. To characterize Listeria presence and diversity in the preharvest environment, feces and water also were collected from a produce farm and tested for Listeria. A highly diverse Listeria population and evidence of Listeria transfer between sample types were observed, indicating tracing Listeria contamination of produce or the processing environment to a specific pre-harvest source is likely difficult without large sampling and subtyping efforts. To control Listeria contamination of the post-harvest environment, a root cause analysis (RCA) procedure was developed to guide intervention identification, implementation, and testing. Interventions using quaternary ammonium compound powder around forklift stops and a floor crack appeared to be effective. Two additional interventions were tested (i.e., use of a chlorinated cleaner in drains and dead-end pipe removal) that were not immediately effective but highlighted that RCA should often be an iterative process. Machine learning models were developed to predict Salmonella and EHEC marker (i.e., eaeA and stx) presence in canal water using spatial and temporal factors. These models indicated machine learning shows promise for predicting pathogen contamination of agricultural water. Additionally, leafy greens were inoculated with E. coli and Salmonella via simulated irrigation events in 3 distinct climates, and produce samples were collected for 4 days following inoculation. The E. coli and Salmonella die-off rates were calculated and the effect of weather on die-off was assessed. These results showed weather, and particularly relative humidity, is important in determining the effectiveness of using die-off as a control strategy. Overall, this work further illustrates the complexities of managing microbial hazards along the produce supply chain and provides potential solutions for controlling these hazards.
dc.language.isoen
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleIDENTIFICATION AND CONTROL OF MICROBIAL HAZARDS ALONG THE PRODUCE SUPPLY CHAIN
dc.typedissertation or thesis
thesis.degree.disciplineFood Science and Technology
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Food Science and Technology
dc.contributor.chairWiedmann, Martin
dc.contributor.committeeMemberWorobo, Randy W.
dc.contributor.committeeMemberMiojevic, Renata Ivanek
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/p2rq-7q30


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