Basic Protocols in Predictive Food Microbiology (eBook, PDF)

• Format: PDF

Produktbeschreibung

This volume details well-established protocols and procedures being used by laboratories and the industry to study Predictive Microbiology in Foods. Chapters guide readers through methods to design and collect data to generate predictive models, the development of a predictive model, approaches the behavior mainly, and experiments in predictive microbiology. Written in the format of the Methods and Protocols in Food Science series, chapters list necessary materials and methods for readily reproducible protocols.

Authoritative and cutting-edge, Predictive Food Microbiology aims to be a foundation for future studies and to be a source of inspiration for new investigations in the field.

Produktdetails

• Verlag: Springer US
• Erscheinungstermin: 9. September 2023
• Englisch
• ISBN-13: 9781071634134
• Artikelnr.: 68888478

Inhaltsangabe

Basic Concepts for Predictive Microbiology.- Methods of Inoculation and Quantification for Collecting Data on Microbial Responses in Foods.- The influence of food matrices on microbial growth.- Primary Predictive Models of Microbial Growth.- A protocol for predictive modeling of microbial inactivation based on experimental data.- Growth/ no-growth microbial models in food science.- Individual-cell-based Modeling for Microbial Growth and Inactivation using Time-lapse Microscopy.- Dynamic Models for Predictive Microbiology.- Mathematical simulation of the bio-protective effect of lactic acid bacteria on foodborne pathogens.- Acceptable Prediction Zones Method for Validation of Predictive Models for Foodborne Pathogens.- Predictive Modeling for Spoilage Microorganisms.- Quantitative PCR Technique for Predictive Models.

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Basic Concepts for Predictive Microbiology.- Methods of Inoculation and Quantification for Collecting Data on Microbial Responses in Foods.- The influence of food matrices on microbial growth.- Primary Predictive Models of Microbial Growth.- A protocol for predictive modeling of microbial inactivation based on experimental data.- Growth/ no-growth microbial models in food science.- Individual-cell-based Modeling for Microbial Growth and Inactivation using Time-lapse Microscopy.- Dynamic Models for Predictive Microbiology.- Mathematical simulation of the bio-protective effect of lactic acid bacteria on foodborne pathogens.- Acceptable Prediction Zones Method for Validation of Predictive Models for Foodborne Pathogens.- Predictive Modeling for Spoilage Microorganisms.- Quantitative PCR Technique for Predictive Models.