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Protecting the Safety and Quality of Live Oysters Through the Integration of Predictive Microbiology in Cold Supply Chains

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Protecting the Safety and Quality of Live Oysters Through the Integration of Predictive Microbiology in Cold Supply Chains Protecting the Safety and Quality of Live Oysters through the Integration of Predictive Microbiology in Cold Supply Chains by Judith Fernandez-Piquer MSc Food Safety, Wageningen University, 2007 BSc Food Science and Technology, University of Barcelona, 2006 BSc Technical Industrial Engineering, Polytechnic University of Catalonia, 2003 A thesis submitted to the School of Agricultural Science, University of Tasmania in fulfilment of the requirements for the degree of Doctor of Philosophy November, 2011 Declaration of originality and authority of access Declaration of originality and authority of access Declaration of Originality I, Judith Fernandez-Piquer, certify that this thesis does not contain any material which has been accepted for a degree or diploma by the University of Tasmania or any other institution, except by way of background information and duly acknowledged in the thesis. To the best of my knowledge and belief, this thesis does not contain material previously published or written by another person except where due reference is made in the text of the thesis and nor does this thesis contain any material that infringes copyright. _____________________________ Judith Fernandez-Piquer, 30 November 2011 Authority of access This thesis may be made available for loan and limited copying in accordance with the Copyright Act 1968. _____________________________ Judith Fernandez-Piquer, 30 November 2011 - 3 - Acknowledgements Acknowledgements This research has been possible with the collaboration of amazing people I have met along the way. After three and a half years of oyster adventures, I am glad to have the opportunity to express my gratitude to all of you. I would like to offer my sincere gratitude to my supervisor, Mark Tamplin, for all the experience and knowledge you have shared with me. I thank your guidance, advice and your intensive review of different documents during my PhD including this thesis. I really appreciate having had the opportunity of discovering Tasmania while working with you. I am also very grateful for the review of part of this thesis by my co-supervisors Tom Ross and John Bowman. I acknowledge Tom Ross for the insightful discussions, and also for your patience developing and optimizing the predictive program. I also thank John Bowman for your kind guidance during the molecular studies. Thanks to Tom McMeekin and June Olley for your friendly comments and wise suggestions during all this period. Many thanks to all my colleagues at University of Tasmania, Ann Wanasen and Lyndal Mellefont for the professional and personal great moments shared on the island. A special thanks to Corina Ly and Steve Quarrell for your invaluable emotional support. I am grateful to Tom Madigan, Damian May and Cath McLeod who collaborated on Pacific oyster studies. I thank Adam Smolenski for conducting fragment analyses. I also wish to thank Shane Powell for your advice during the genetic studies, David Ratkowsky for the fantastic statistics discussions and Silvia Estrada-Flores for your assistance in - 4 - Acknowledgements preparing the stochastic model. Thanks to Bianca Porteus, Alison Dann and Chris Chapman “Chapito” for helping with oyster washing and drilling. My gratitude to the Australian Shellfish Quality Assurance Program managers and the Department of Primary Industries in Tasmania, New South Wales and South Australia for support and participation in the project. I thank Wayne O'Connor, Michael Dove and Tony Troup for their help with the work undertaken with the New South Wales oysters; Ken Lee for organizing the oyster shipments from South Australia; Alison Turnbull and Ray Brown for providing information about the Tasmanian oyster industry; and Anthony Zammit and Rachel King for their enthusiastic interest in my research. I acknowledge Christian Garland and Scott Parkinson who introduced me to oyster science and oyster industry. I would also like to thank Peter Kosmeyer and the rest of the Marine Culture Ltd oyster farm team in Tasmania for answering my many curiosities about oysters, for teaching me how to shuck oysters and kindly providing all samples at no cost. I am deeply grateful to the Australian Seafood Cooperative Research Centre and the Australian Oyster Consortium for supporting financially this research, some of my training and my PhD scholarship. I also want to thank to University of Tasmania for my Endeavour International Postgraduate Research scholarship. I am forever grateful for the love and encouragement of my family: Montse and Ramón :“Gracias por entender mis ganas de experimentar nuevos destinos y estar a mi lado a pesar de la distancia”, and Héctor: “Gracias por entender mi ausencia”. Judith Fernandez-Piquer, Hobart, 2011 - 5 - Abstract Abstract Vibrio parahaemolyticus is a bacterial species indigenous to marine environments and can accumulate in oysters. Some V. parahaemolyticus strains are pathogenic and seafood- borne outbreaks are observed worldwide. This pathogen can reach infectious levels in oysters if post-harvest temperatures are not properly controlled. The aim of this thesis was to support oyster supply chain management by developing predictive microbiological tools to improve the safety and quality of oysters in the market. A predictive model was produced by injecting Pacific oysters (Crassostrea gigas) harvested in Tasmania with a cocktail of pathogenic and non-pathogenic V. parahaemolyticus strains, and measuring population changes over time at static storage temperatures from 4 to 30ºC. In parallel, the total viable bacteria count (TVC) was measured. The V. parahaemolyticus and TVC growth models were then evaluated with Pacific and Sydney Rock oysters (Saccostrea glomerata) harvested in New South Wales containing natural populations of V. parahaemolyticus. Oysters were stored at static temperatures from 15 to 28ºC, and Vibrio parahaemolyticus and TVC viability were measured. In Pacific oysters, TVC growth was observed at all tested temperatures while V. parahaemolyticus growth was observed only at 23 and 28ºC. In Sydney Rock oysters, TVC growth was observed only at 24ºC and V. parahaemolyticus did not grow at any storage temperature tested. These interesting findings potentially indicate that Sydney - 6 - Abstract Rock oysters have enhanced anti-bacterial defences compared to Pacific oysters, and that commercial temperature controls to manage V. parahaemolyticus growth can be different. Consistently higher growth rates of V. parahaemolyticus and TVC were observed in Tasmanian versus New South Wales oysters and may have been caused by different factors. They include variations in levels of background competitive flora, different growth rates among V. parahaemolyticus strains, and/or changes in the natural bacterial community structure influenced by conditions at the harvest site or during shipment to the laboratory. Nevertheless, the overall performance of the model was “fail-safe” for predicting growth of V. parahaemolyticus in Pacific oysters and would be a preferred public health tool. The V. parahaemolyticus and TVC predictive models for Pacific oysters were integrated in an Excel® software tool. The model allows users to input time-temperature profiles and analyse the effects of dynamic storage temperatures normally found in oyster supply chains on bacterial growth. The tool was evaluated in five different simulated oyster supply chains (refrigerated and non-refrigerated). Observed and predicted V. parahaemolyticus and TVC growth rates were compared and a model over-estimation mean of 2.30 for V. parahaemolyticus and 2.40 for TVC were observed as determined by the bias factor index. Reasons for over-estimations are likely the same as those for model validation experiments. Uncertainty and variability are associated with oyster supply chains. Therefore, a stochastic model which encompassed the operations from oyster farm to the consumer was built using ModelRisk® risk analysis software. This case study generated probabilistic distributions and the percentage of oysters containing V. parahaemolyticus and TVC - 7 - Abstract during each operation of the supply chain. The results were used for an objective evaluation of the influence of short and long supply chains during summer and winter seasons. The stochastic model can help the oyster industry evaluate the performance of oyster cold chains, and potentially enable real-time decisions if coupled with suitable traceability systems. It can also provide risk managers with valuable information about V. parahaemolyticus exposure levels. Finally, in order to better understand microbial changes in oysters during distribution and storage, the dynamics of microbial communities in Pacific oysters was determined using 16S rRNA-based terminal restriction length polymorphism and clone library analyses. Significant differences in bacterial community composition were observed, and the predominant bacteria were identified for fresh and stored oysters at different temperatures. High microbial diversity in oysters was observed, with up to 73 different genera-related identified clones among all samples. The results identified Psychrilyobacter spp. as a potential spoilage indicator for future shelf-life studies, and Polynucleobacter and a bacterial group related to Alkaliflexus as possible indicators for storage temperature control in Pacific oysters. In future studies, quantitative correlations of the identified species and the freshness of oysters should be explored to determine whether the predominant microbes identified represent
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