The first state-of-the-art review of this dynamic field in a decade, Modeling Microbial Responses in Foods provides the latest information on techniques in mathematical modeling of microbial growth and survival. The comprehensive coverage includes basic approaches such as improvements in the development of primary and secondary models, statistical

The first state-of-the-art review of this dynamic field in a decade, Modeling Microbial Responses in Foods provides the latest information on techniques in mathematical modeling of microbial growth and survival. The comprehensive coverage includes basic approaches such as improvements in the development of primary and secondary models, statistical fitting strategies, and novel data collection methods. An international team of experts explore important developing areas, including specific applications, challenges in applying models to foods, variability and uncertainty, and new modeling strategies. The authors present detailed descriptions of non-linear regression fitting, methods, approaches relevant to 'real world' situations, and extensive applications of predictive models. They conclude by highlighting the strengths and weaknesses in the field and areas for future work, and attempt to resolve some of the outstanding conflicts. The book includes strategies for combining databases, improving researcher networks, and standardization of applications packages. Providing the uninitiated with enough information to begin developing their own models, Modeling Microbial Responses in Foods covers all aspects of growth and survival modeling from the primary stage of gathering data to the implementation of final models in appropriate delivery systems.

Predicting microbial inactivation under high pressure and the use of mechanistic models are also covered.

Predictive microbiology is a recent area within food microbiology, which studies the responses of microorganisms in foods to environmental factors (e.g., temperature, pH) through mathematical functions. These functions enable scientists to predict the behavior of pathogens and spoilage microorganisms under different combinations of factors. The main goal of predictive models in food science is to assure both food safety and food quality. Predictive models in foods have developed significantly in the last 20 years due to the emergence of powerful computational resources and sophisticated statistical packages. This book presents the concepts, models, most significant advances, and future trends in predictive microbiology. It will discuss the history and basic concepts of predictive microbiology. The most frequently used models will be explained, and the most significant software and databases (e.g., Combase, Sym’Previus) will be reviewed. Quantitative Risk Assessment, which uses predictive modeling to account for the transmission of foodborne pathogens across the food chain, will also be covered. ​

Four authors with backgrounds in food microbiology, food chemistry, mathematics, and statistics, explain how techniques of predictive microbiology can allow an objective evaluation of the effects of processing, distribution, and storage on the microbiological safety and quality of foods. The trick is to understand the microbial ecology of a process or of a food at a particular point in the chain, then use mathematical relationships between microbial growth and the expected environmental conditions, to predict the growth or survival of selected organisms. Annotation copyright by Book News, Inc., Portland, OR

Predictive microbiology primarily deals with the quantitative assessment of microbial responses at a macroscopic or microscopic level, but also involves the estimation of how likely an individual or population is to be exposed to a microbial hazard.This book provides an overview of the major literature in the area of predictive microbiology, with a special focus on food. The authors tackle issues related to modeling approaches and their applications in both microbial spoilage and safety.Food spoilage is presented through applications of best-before-date determination and commercial sterility. Food safety is presented through applications of risk-based safety management. The different modeling aspects are introduced through probabilistic and stochastic approaches, including model and data uncertainty, but also biological variability. - Features an extensive review of modelling terminology - Presents examples of all available microbial models (i.e., growth, inactivation, growth/no growth) and applicable software - Revisits all statistical aspects related to exposure assessment - Describes realistic examples of implementing microbial spoilage and safety modeling approaches

Microorganisms are essential for the production of many foods, including cheese, yoghurt, and bread, but they can also cause spoilage and diseases. Quantitative Microbiology of Food Processing: Modeling the Microbial Ecology explores the effects of food processing techniques on these microorganisms, the microbial ecology of food, and the surrounding issues concerning contemporary food safety and stability. Whilst literature has been written on these separate topics, this book seamlessly integrates all these concepts in a unique and comprehensive guide. Each chapter includes background information regarding a specific unit operation, discussion of quantitative aspects, and examples of food processes in which the unit operation plays a major role in microbial safety. This is the perfect text for those seeking to understand the quantitative effects of unit operations and beyond on the fate of foodborne microorganisms in different foods. Quantitative Microbiology of Food Processing is an invaluable resource for students, scientists, and professionals of both food engineering and food microbiology.

Presenting a novel view of the quantitative modeling of microbial growth and inactivation patterns in food, water, and biosystems, Advanced Quantitative Microbiology for Foods and Biosystems: Models for Predicting Growth and Inactivation describes new models for estimating microbial growth and survival. The author covers traditional and alte

Food Safety Engineering is the first reference work to provide up-to-date coverage of the advanced technologies and strategies for the engineering of safe foods. Researchers, laboratory staff and food industry professionals with an interest in food engineering safety will find a singular source containing all of the needed information required to understand this rapidly advancing topic. The text lays a solid foundation for solving microbial food safety problems, developing advanced thermal and non-thermal technologies, designing food safety preventive control processes and sustainable operation of the food safety preventive control processes. The first section of chapters presents a comprehensive overview of food microbiology from foodborne pathogens to detection methods. The next section focuses on preventative practices, detailing all of the major manufacturing processes assuring the safety of foods including Good Manufacturing Practices (GMP), Hazard Analysis and Critical Control Points (HACCP), Hazard Analysis and Risk-Based Preventive Controls (HARPC), food traceability, and recalls. Further sections provide insights into plant layout and equipment design, and maintenance. Modeling and process design are covered in depth. Conventional and novel preventive controls for food safety include the current and emerging food processing technologies. Further sections focus on such important aspects as aseptic packaging and post-packaging technologies. With its comprehensive scope of up-to-date technologies and manufacturing processes, this is a useful and first-of-its kind text for the next generation food safety engineering professionals.

The first state-of-the-art review of this dynamic field in a decade, Modeling Microbial Responses in Foods provides the latest information on techniques in mathematical modeling of microbial growth and survival. The comprehensive coverage includes basic approaches such as improvements in the development of primary and secondary models, statistical