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

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Spore Wars: Tools to Identify, Predict, and Prevent Dairy Spoilage SPORE WARS: TOOLS TO IDENTIFY, PREDICT, AND PREVENT DAIRY SPOILAGE 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 Ariel Jean Buehler August 2018 © 2018 Ariel Jean Buehler SPORE WARS: TOOLS TO IDENTIFY, PREDICT AND PREVENT DAIRY SPOILAGE Ariel Jean Buehler, Ph. D. Cornell University 2018 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 i 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 6°C 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. ii BIOGRAPHICAL SKETCH Ariel was born and raised in Knoxville, Tennessee. Her parents exposed Ariel to the joys and trials of food production through home gardening in Tater Valley, Tennessee at a young age. Ariel earned a Bachelor of Science degree in Food Science from the University of Tennessee as a Haslam Scholar, where she completed a Scholars thesis investigating the survival of Escherichia coli O157:H7 on microgreens. Ariel’s love for the food industry grew during her time as an undergraduate, where she interned for Kraft Foods Research and Development in Glenview, Illinois for three summers. At Kraft Foods, Ariel experienced the creativity of food science by translating research to products consumers demand and enjoy. Ariel began her graduate studies at Cornell University under the guidance of Dr. Martin Wiedmann in 2014. Ariel tailored her graduate experience to focus on real-world applications for the food industry. Ariel had the opportunity to travel to Hyderabad, India and Beijing, China to explore value addition in the dairy and produce industries. Ariel also applied her dairy spoilage research to the food industry directly as an intern for General Mills in Minneapolis, Minnesota, working on dairy quality innovation for Yoplait. After graduating, Ariel will return to General Mills as a research scientist, where she will work to ensure the safety and quality of foods for consumers in the United States and worldwide. iii For my parents, who have endlessly supported me on this journey iv ACKNOWLEDGMENTS “A Ph.D. is a marathon, not a sprint” was the truest advice I received before starting graduate school. I had many people by my side during this journey, and I would like to thank them personally here. First, I would like to thank my committee chair and advisor, Dr. Martin Wiedmann, for his guidance throughout graduate school. Martin allowed me to tailor my graduate experience to focus on industry-specific research projects and equipped me with a unique skill set to be an effective industry scientist. I would also like to thank my minor advisors, Dr. Yrjö Gröhn and Dr. Dennis Miller. Dr. Gröhn was always available to discuss project intricacies and give life advice along the way. I have Dr. Miller to thank for allowing me to go to China to help develop an edamame value-added snack and teaching me nutrition lessons to live by. My racemates along the marathon route of graduate school were my labmates of the Food Safety Lab and Milk Quality Improvement Program and my classmates at Cornell. Labmates Sam Reichler, Genevieve Sullivan, Rachel Miller, and Yichang Liu made Saturdays spent inoculating Greek yogurt fun, and my classmates Pedro Menchik, Beth Burzynski, and Emily Griep exposed me to a broader realm of food science and enology. I have our lab managers, Sherry Roof and Nicole Martin, and our media support team, Maureen Gunderson and Alan Bitar, to thank for their endless support on my research projects. I had many cheerleaders by my side for the journey. Friends at Cornell, lovingly known as Tri-Delt, L’oriel, and Cookies and Crème, helped me through the trials and triumphs. Laura Carrol (LC) supplied many nights of rosè and laughter, Lory v Henderson (LOH) was the best lunch date, Sheena Hilton was lip-sync extraordinaire, and Liz Buerman, Rachel Allison, and Madeline Bee showed me how to have fun in upstate New York. The Cornell Symphony viola section and Bethel Grove Bible Church worship team kept my creative side going and provided an outlet for playing the viola during graduate school. Finally, my family served as my pacemaker for the race. My parents, David and Beth, were always a phone call away, and encouraged me constantly. My brother, Eddie, provided brotherly advice as he finished his Ph.D. on the West coast. My boyfriend, Kevin, endlessly supported me to the finish line of graduate school and served as my audience for countless practice presentations. vi TABLE OF CONTENTS BIOGRAPHICAL SKETCH iii DEDICATION iv ACKNOWLEDGEMENTS v-vi TABLE OF CONTENTS vii LIST OF FIGURES viii-x LIST OF TABLES xi-xii CHAPTER 1 Introduction 1-8 CHAPTER 2 ITS Sequencing Reveals Considerable Fungal Diversity 9-54 in Dairy Products CHAPTER 3 ITS Sequence-Based Characterization of Fungal Isolates 55-73 from Multiple Yogurt Facilities—a Case Study CHAPTER 4 Evaluation of Lactic Acid Biopreservatives in Greek 74-121 Yogurt to Inhibit Yeast and Mold Spoilage and Development of a Yogurt Spoilage Predictive Model CHAPTER 5 Psychrotolerant Sporeformer Growth Characterization 122-178 for the Development of a Dairy Spoilage Predictive Model CHAPTER 6 Conclusion 179-183 vii LIST OF FIGURES Figure 2.1 Midpoint-rooted maximum-likelihood (ML) phylogenetic 23 tree of ITS region sequences from fungal species isolated from raw milk, raw milk cheese, pasteurized milk cheese, and yogurt Figure 3.1 Internal transcribed spacer (ITS) allelic type (AT) matrix 63 displaying ITS ATs collected from two yogurt facilities Figure 3.2 Association between sample type and ITS AT within each 66 yogurt processing facility Figure 4.1 Concentration of yeast and mold in Greek yogurt by day of 95 storage at 7°C Figure 4.2 Time to visible growth of yeast and mold on the surface of 101 Greek yogurt Figure 4.3 Histogram of the simulated percent of consumers exposed to 104 visible mycelium mold growth per 1 million yogurt cups produced without the use of fungal inhibitors Figure 4.4 Best-case and worst-case scenarios of the number of 106 consumers exposed to visible mycelium mold (≥ 3mm) in yogurt at the time of domestic consumption Figure 4.5 Histograms of the simulated percent of consumers exposed 110 to visible mycelium mold growth in yogurt per 1 million cups of yogurt produced for a nationally distributed yogurt viii compared to a regionally distributed yogurt Figure 4.6 Histograms of the simulated percent of consumers exposed 112 to visible mycelium mold growth in yogurt
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