Basic Protocols in Predictive Food Microbiology (Methods and Protocols in Food Science)

✍ Scribed by Verônica Ortiz Alvarenga (editor)

• Publisher: Humana
• Year: 2023
• Tongue: English
• Leaves: 241
• Edition: 1st ed. 2023
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Preface to the Series
Preface
Contents
Contributors
Chapter 1: Basic Concepts for Predictive Microbiology
1 Introduction
2 Application of Predictive Models
3 Concepts for Predictive Microbiology and Mathematical Modeling
3.1 Microbiological Concepts
3.2 Mathematical Concepts
3.3 Types of Predictive Models
3.3.1 Primary Models
3.3.1.1 Growth Models
3.3.1.2 Inactivation Models
3.3.2 Secondary Models
3.3.3 Tertiary Models
3.3.3.1 Types of Programing Languages, Tools, and Software Available to Predictive Microbiology
4 How to Design to Predictive Microbiology: Collect Data and Design Experiment
4.1 Design Experiment
4.1.1 Complete Factorial Design
4.1.2 Fractional Factorial Design
4.1.3 Central Composite Design
4.1.4 Doehlert Matrix
4.2 Growth Matrices and Microbial Strain
4.3 Goodness Index Fit and Model Validation
4.3.1 Goodness Fit Index
4.3.1.1 R-Squared
4.3.1.2 RSS
4.3.1.3 RMSE
5 Limitation
6 Notes
References
Chapter 2: Methods of Inoculation and Quantification for Collecting Data on Microbial Responses in Foods
1 Introduction
2 Inoculation Methods
2.1 Strain Selection
2.2 Strains Maintenance
2.3 Inoculum Preparation
2.4 Inoculum Level
2.5 Inoculation Techniques
3 Controls
4 Replicates and Number of Units
5 Microbial Enumeration
5.1 Sample Preparation
5.2 Enumeration Techniques
5.2.1 Pour Plate Technique
5.2.2 Surface Plate Technique
5.3 Calculation and Expression of Results
References
Chapter 3: The Influence of Food Matrices on Microbial Growth
1 Introduction
1.1 Microbial Growth Quantification
1.2 Food Structure
1.3 Water Activity (aw)
1.4 Growth of Microbes in Acidified and Acidic Food
1.5 Alignment with Challenge Tests
2 Materials
2.1 Determination of aw in Food
2.2 Determination of pH in Food
3 Method
3.1 Inoculum Preparation
3.2 Preparation of Test Matrix
3.3 Growth Experiment
4 Notes
References
Chapter 4: Primary Predictive Models of Microbial Growth
1 Introduction
1.1 Baranyi and Roberts Model
1.2 Gompertz Model
1.2.1 Modified Gompertz Model
1.3 Logistic Model
1.3.1 Modified Logistic Model
1.4 Buchanan Model
2 Materials
3 Methods
3.1 Microbial Analysis
3.2 Growth Curve Experiments
3.3 Primary Growth Modeling
3.3.1 DMFit
3.3.1.1 DMFit: Online Version
3.3.1.2 Excel DMFit: Offline Version
3.3.2 Goodness of Fit Index
4 Limitations
5 Notes
References
Chapter 5: A Protocol for Predictive Modeling of Microbial Inactivation Based on Experimental Data
1 Introduction
2 Protocol for Building Predictive Models for Heat Inactivation
2.1 Definition of the Scope
2.2 Literature Review
2.3 Model Definition
2.3.1 Inactivation Models for Isothermal Conditions
2.3.2 Inactivation Models for Dynamic Temperature Conditions
2.3.3 The Effect of Other Environmental Factors
2.4 Experimental Design
2.5 Data Gathering
2.5.1 Preparation of Media and Bacterial Cultures
2.5.2 Preparation of the Heating Device
2.5.3 Obtention of the Time Points and Counting
2.6 Parameter Estimation
2.6.1 Model Fitting Under Isothermal Conditions
2.6.1.1 Model Fitting of Isothermal Models Following a Two-Step Approach Using the Web Version of Bioinactivation
2.6.1.2 Model Fitting of Isothermal Models Following a One-Step Approach Using the Web Version of Bioinactivation
2.6.2 Model Fitting Under Dynamic Conditions
2.6.2.1 Model Fitting Dynamic Models Using the Bioinactivation Web Application
2.6.3 Alternatives to Bioinactivation for Model Fitting of Inactivation Experiments
2.7 Model Validation
2.7.1 Analysis of the Model Residuals
2.7.2 Evaluation of the Parameter and Prediction Uncertainty
2.7.3 Comparison Against Already Published Data
2.7.4 Comparison Against Independent Data (When Relevant/Applicable)
3 Materials
3.1 Equipment to Establish Heat Inactivation Kinetics
4 Notes and Closing Comments
References
Chapter 6: Growth/No-Growth Microbial Models in Food Science
1 Introduction
2 Methods and Principles of the Logistic Regression Model
2.1 Developing Logistic Regression Model Equations
2.2 Input Data for Logistic Regression Modeling
2.3 Adjustment in Statistical Software and Performance of Logistic Regression Models
2.4 Stepwise for the Development the Growth/No-Growth Model
3 Logistic Regression Modeling Application
3.1 Microbial Shelf Life Studies
3.2 Hazard Analysis and Critical Control Points (HACCP)
3.3 Research and Development of New Products
References
Chapter 7: Individual Cell-Based Modeling for Microbial Growth and Inactivation Using Time-Lapse Microscopy
1 Introduction
2 Materials
2.1 Laboratory Media
2.2 Stains
2.3 Phase-Contrast Microscope
2.4 Fluorescence (Confocal Laser Scanning) Microscope
2.5 Image Analysis Software
3 Methods
3.1 Phase-Contrast Microscopy
3.2 Fluorescence Microscopy
3.3 Data Analysis
4 Notes
References
Chapter 8: Dynamic Models for Predictive Microbiology
1 Introduction
2 Materials
3 Methods
3.1 Parameter Estimation
3.1.1 Case Study Under Constant Conditions
3.2 Model Simulations to Predict the Microbial Dynamic
3.2.1 Case Study of Variable Conditions
4 Notes
References
Chapter 9: Mathematical Simulation of the Bio-Protective Effect of Lactic Acid Bacteria on Foodborne Pathogens
1 Introduction
2 Methods
2.1 Data Generation
2.1.1 Media for Microbial Interaction Experiments
2.1.2 Inoculation and Incubation Conditions to Perform Mono and Coculture Experiments
2.1.3 Microbiological Analysis in Mono and Coculture Experiments
2.1.4 Physicochemical Analyses (pH and Lactic Acid)
2.1.4.1 pH Determination
2.1.4.2 Lactic Acid Determination
2.2 Development of Predictive Models
2.2.1 Primary Model for Monoculture Data
2.2.2 Secondary Models for Monoculture Data
2.2.3 Quantification of the Effect of Coculture Conditions on Kinetic Parameters
2.2.4 Microbial Interaction Models
2.2.5 Prediction Capacity and Model Validation
3 Application
4 Notes
References
Chapter 10: Acceptable Prediction Zones Method for the Validation of Predictive Models for Foodborne Pathogens
1 Introduction
2 Materials
3 Method
3.1 Terms
3.2 Validation Software Tool (ValT)
3.3 Criteria for Test Data
3.4 Criteria for Model Performance
3.5 Criteria for Model Validation
4 Example
5 Conclusion
6 Notes
References
Chapter 11: Predictive Modeling for Spoilage Microorganisms
1 Introduction
2 Materials and Methods
3 Microbial Experiments
4 Fitting the Model
5 Comparison of the Goodness of Fit of the Models
6 The Models´ Validation
7 Notes
References
Chapter 12: Quantitative PCR Technique for Predictive Models
1 Introduction
2 Materials
2.1 Inoculum Preparation
2.2 Meat Product Sample Preparation
2.3 Quantification by the Standard Plate Count Method
2.4 Genomic DNA Extraction from Blood Sausage
2.5 qPCR Assay
3 Methods
3.1 Inoculum Preparation
3.2 Meat Product Sample Preparation
3.3 Growth Curve Construction
3.4 Quantification of W. viridescens by Plate Count Method
3.5 qPCR Assay
3.5.1 Genomic DNA Extraction from Blood Sausage
3.5.2 Quantification of W. viridescens by qPCR
4 Notes
References
Index