Food Quality Analysis: Applications of Analytical Methods Coupled With Artificial Intelligence

Description

Food Quality Analysis: Applications of Analytical Methods Coupled With Artificial Intelligence provides different spectroscopic techniques and their application to food quality analysis, with the unique approach of adding multivariate analysis as well as artificial intelligence applications. It also brings chromatographic methods for the analysis of a wide range of food items including wheat flours, oils and fats. Written by a team of interdisciplinary experts, this is a valuable resource for researchers and personnel involved in food industries. Front Cover Food Quality Analysis Copyright Page Contents List of contributors Preface 1. Importance of food quality analysis in relation to food safety and human health and COVID-19 in particular 1.1 Introduction 1.2 Importance of food quality analysis 1.3 Compositional analysis of foods 1.3.1 Moisture analysis 1.3.2 Fat analysis 1.3.3 Protein analysis 1.3.4 Carbohydrate analysis 1.3.5 Vitamin analysis 1.3.6 Mineral analysis 1.4 Chemical analysis of foods 1.4.1 pH analysis 1.4.2 Enzyme analysis 1.4.3 Food contaminants analysis 1.4.3.1 Pesticide analysis 1.4.3.2 Mycotoxin analysis 1.5 Importance of food analysis in relation to COVID-19 1.5.1 Food safety management in COVID-19 era 1.5.2 SARS-CoV-2 virus analysis 1.6 Future prospects 1.7 Conclusions References 2. Fourier transform infrared spectroscopy combined with multivariate analysis for quality analysis of fats and oils 2.1 Introduction 2.2 Fourier transform infrared spectroscopy and chemometrics 2.3 Analysis of fat and oil components 2.4 Determination of fat and oil parameters using Fourier transform infrared spectroscopy References 3. Fluorescence spectroscopy for beer quality analysis 3.1 Introduction 3.2 Concept of fluorescence 3.3 Characteristics of fluorescence 3.4 Factors affecting fluorescence phenomenon 3.5 Fluorescence spectra and its types 3.5.1 Total luminescence spectra 3.5.2 Excitation spectra 3.5.3 Emission spectra 3.5.4 3D fluorescence spectra 3.5.5 Synchronous fluorescence spectra 3.5.5.1 Right-angled fluorescence 3.5.5.2 Front-face fluorescence 3.6 Phenomena of fluorescence scattering 3.6.1 Raman scattering 3.6.2 Rayleigh scattering 3.7 Fluorescence geometry of beer 3.8 Beer constituents having fluorescent properties 3.9 Application of fluorescence spectroscopy in beer 3.9.1 Stratification of beer 3.9.2 Bitterness of beer 3.9.3 Vitamin B analysis of beer 3.9.4 Storage analysis of beer 3.10 Quality analysis of beer using PARAFAC model 3.11 Conclusions References 4. Raman spectroscopy combined with multivariate analysis in quality analysis of food and pharmaceutical materials 4.1 Introduction 4.2 Principles of Raman spectroscopy 4.3 Raman signal 4.4 Types of Raman spectroscopy used for food analysis 4.4.1 Micro Raman spectroscopy 4.4.2 Raman imaging 4.4.3 Surface-enhanced Raman spectroscopy 4.4.4 Near-infrared Raman spectroscopy 4.4.5 Fourier transform Raman spectroscopy 4.4.6 Spatial offset Raman spectroscopy 4.4.7 Raman spectral analysis 4.5 Raman spectroscopy to detect the hazards caused by biological agents 4.6 Multivariate analysis 4.7 Raman spectroscopy for detection of food 4.7.1 Raman for milk analysis 4.7.2 Raman spectroscopy for butter and margarine 4.7.3 Raman spectroscopy for yogurt 4.7.4 Raman spectroscopy for meat analysis 4.7.5 Raman for olive oil 4.7.6 Raman spectroscopy for beverages 4.7.7 Wine analysis 4.8 Detection of chemicals using Raman analysis 4.9 Raman detection for pharmaceuticals 4.10 Raman to detect nanomaterials 4.11 Raman analysis to prevent physical hazards 4.12 Future perspective Acknowledgments References 5. Chromatographic methods for the analysis of oils and fats 5.1 Introduction 5.2 Quality assessment of oils and fats 5.3 Chromatographic based techniques 5.3.1 Application of TLC for quality control of edible fats and oils 5.3.1.1 Qualitative analysis 5.3.1.2 Quantitative analysis 5.3.2 GC-FID and GC-MS for quality assessment of fats and oils 5.3.2.1 General principle 5.3.2.2 General official methods 5.3.2.2.1 Fatty acid composition 5.3.2.2.1.1 USP-NF 5.3.2.2.1.2 EP 10 5.3.2.2.2 Sterols composition 5.3.2.2.2.1 USP-NF 5.3.2.2.2.2 EP 10 5.3.2.2.3 Omega-3 fatty acid determination and profiles 5.3.2.3 Application of gas chromatographic methods for quality assessment of fats and oils 5.3.3 LC and related techniques for analysis of fats and oils 5.3.3.1 Standard methods for quality assessment 5.3.3.2 Application of liquid chromatographic methods for quality assessment of fats and oils 5.4 Conclusions, recommendations, and future trends Acknowledgement References 6. Gas chromatography and multivariate analysis for wheat flours 6.1 Introduction 6.2 Wheat grain compositions 6.3 Wheat flour 6.4 The standard for wheat flour 6.5 Quality assessment of wheat and wheat-based products 6.6 Application of gas chromatography coupled with mass spectroscopy 6.6.1 Gas chromatography coupled with mass spectroscopy analytical procedures 6.6.1.1 Extraction techniques 6.6.1.2 Derivatization 6.6.1.3 Chromatographic techniques 6.6.1.4 GC–MS data processing and statistical analysis 6.7 Application of gas chromatography coupled with mass spectroscopy and multivariate analysis in quality analysis of wheat... 6.7.1 Gas chromatography coupled with mass spectroscopy in analysis of wheat components/chemical composition 6.7.2 Gas chromatography coupled with mass spectroscopy analysis of volatile organic compounds in wheat flour and wheat-bas... 6.8 Gas chromatography coupled with mass spectroscopy-based metabolomics in wheat study 6.9 Gas chromatography coupled with mass spectroscopy wheat authentication 6.10 Gas chromatography coupled with mass spectroscopy in wheat and wheat-based food spoilage and storage 6.11 Future gas chromatography coupled with mass spectroscopy application in wheat food safety 6.12 Conclusions References 7. Electrochemical sensors coupled with machine learning for food safety and quality inspection 7.1 Introduction 7.2 Electrochemical sensor types used in food science and technology 7.3 Machine learning techniques used in food science and technology 7.4 Machine learning algorithms applied to food safety and quality inspection 7.5 Machine learning applied to electrochemical sensors for monitoring food contaminants 7.6 Conclusions and perspectives References Index Back Cover