Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Series Preface -- Preface -- Editors -- List of Contributors -- SECTION I: IMAGING SYSTEMS -- Chapter 1 Fundamentals -- 1.1 Introduction -- 1.1.1 The Need for Speed in Food Analysis -- 1.2 Introduction to Spectroscopy -- 1.3 Integration of Spectroscopy and Imaging -- 1.4 Introduction to Hyperspectral Imaging -- 1.4.1 Brief History -- 1.4.2 Principles of Hyperspectral Imaging -- 1.4.3 Basic Hyperspectral System Components and Set-Up -- 1.5 Multispectral Imaging -- 1.6 Analysing Hyperspectral Images -- 1.7 Advantages and Disadvantages -- 1.7.1 Advantages -- 1.7.2 Disadvantages -- 1.8 Conclusion -- References -- Chapter 2 Optimization of Hyperspectral Image Cube Acquisition: A Case Study on Meat and Bone Meal -- 2.1 Introduction -- 2.2 Material and Methods -- 2.2.1 Hyperspectral Imaging Equipment -- 2.2.2 Comparison of Dark Reference Materials -- 2.2.3 Scanning Frequency for Dark and White References -- 2.2.4 Ambient Light -- 2.2.5 Sample Presentation -- 2.2.6 Mathematical Data Treatment -- 2.3 Results and Discussion -- 2.3.1 Dark Reference Material -- 2.3.2 Scanning Frequency of Dark and White References -- 2.3.3 Ambient Light -- 2.3.4 Sample Presentation -- 2.4 Conclusions -- References -- Chapter 3 Image Segmentation -- 3.1 Introduction -- 3.2 Pre-processing -- 3.3 Segmentation -- 3.3.1 Supervised Segmentation -- 3.3.1.1 Hierarchical Segmentation -- 3.3.1.2 Bayesian Framework Segmentation -- 3.3.1.3 Evolutionary Cellular Automata-Based Segmentation -- 3.3.1.4 Minimum Spanning Forest -- 3.3.1.5 k-Nearest Neighbor Segmentation -- 3.3.2 Unsupervised Segmentation -- 3.3.2.1 End Member Threshold Selection -- 3.3.2.2 Watershed -- 3.3.2.3 Spatial-Spectral Graph -- 3.3.2.4 k-Means Clustering -- 3.3.2.5 Superpixel Segmentation -- 3.4 Performance Metrics.

3.5 Conclusion -- References -- Chapter 4 Data Extraction and Treatment -- 4.1 Introduction -- 4.2 Spectral Extraction and Treatment -- 4.2.1 Spectral Extraction -- 4.2.2 Spectral Treatment -- 4.2.2.1 Spectral Transforms -- 4.2.2.2 Spectral Pretreatment -- 4.3 Image Feature Extraction -- 4.3.1 Run Length Matrices -- 4.3.2 GLCM -- 4.3.2.1 Gabor Filter -- 4.4 Conclusions -- References -- SECTION II: CHEMOMETRICS -- Chapter 5 Multivariate Analysis and Techniques -- 5.1 Introduction -- 5.2 Multivariate Analysis of Hyperspectral Data -- 5.2.1 Classification Methods -- 5.2.2 Multivariate Regression -- 5.3 Validation of Multivariate Calibration Model -- 5.4 Evaluation of the Multivariate Classification Models -- 5.5 Evaluation of the Multivariate Regression Models -- 5.6 Multivariate Image Processing -- 5.7 Development of Classification Map -- 5.8 Prediction Map -- 5.9 Software for Multivariate Analysis and Image Processing -- 5.10 Application of Multivariate Analysis in Some Selected Applications of Meat -- 5.11 Conclusion -- References -- Chapter 6 Principal Component Analysis -- 6.1 Introduction: The Importance of Exploratory Methods -- 6.2 PCA as a Projection Method -- 6.3 How to Execute PCA -- 6.3.1 The Singular Value Decomposition (SVD) Method -- 6.3.2 The Nonlinear Iterative Partial Least Squares (NIPALS) Algorithm -- 6.3.3 Advantages and Disadvantages of the Two Approaches -- 6.4 PCA as a Compression Method -- 6.5 Unfolding and Refolding Hyperspectral Data -- 6.6 Approaches for PCA-Based Image Processing -- 6.6.1 Image-Based Approach -- 6.6.2 Object-Based Approach -- 6.6.3 Pixel-Based Approach -- 6.7 PCA-Based Image Texture Analysis -- 6.8 Conclusions -- Acknowledgement -- References -- Chapter 7 Partial Least Squares Regression -- 7.1 Meat -- 7.2 Bacteria -- 7.3 Fruits -- 7.4 Seeds -- 7.5 Melamine -- References.

Chapter 8 Linear Discriminant Analysis -- 8.1 Wheat -- 8.2 Fruits and Vegetables -- References -- Chapter 9 Support Vector Machines -- 9.1 Seafood -- 9.2 Fruits -- 9.3 Seeds -- References -- Chapter 10 Decision Trees -- References -- Chapter 11 Artificial Neural Networks and Hyperspectral Images for Quality Control in Foods -- 11.1 Introduction -- 11.2 Artificial Neural Networks -- 11.2.1 Neuron and Models -- 11.2.1.1 McCulloch-Pitts Model -- 11.2.1.2 Perceptron Model -- 11.2.2 Architectures or Topology -- 11.2.2.1 Multilayer Feed-Forward Neural Networks -- 11.2.2.2 Radial Basis Function Neural Networks -- 11.2.2.3 Self-Organizing Maps -- 11.3 Applications in Food Quality Control -- 11.3.1 Exploratory Analysis and Classification -- 11.3.1.1 Detection of Defects and Damages -- 11.3.1.2 Detection of Contaminant -- 11.3.2 Regression Analysis -- 11.3.2.1 Analytical Quantitation -- 11.4 An Example in Open Access Software-Octave -- 11.4.1 Texture Analysis -- 11.4.2 Image Acquisition -- 11.4.3 Preprocessing -- 11.4.4 Data Extraction -- 11.4.5 Dimensionality Reduction -- 11.4.6 Artificial Neural Network Modeling -- Appendices -- References -- SECTION III: APPLICATIONS -- Chapter 12 Recent Advances for Rapid Detection of Quality and Safety of Fish by Hyperspectral Imaging Analysis -- 12.1 Introduction: Background and Driving Forces -- 12.2 Freshness -- 12.3 Physical Properties -- 12.4 Chemical Compositions -- 12.5 Nematodes Inspection -- 12.6 Microbial Spoilage Inspection -- 12.7 Conclusions -- Acknowledgments -- References -- Chapter 13 Applications of Hyperspectral Imaging for Meat Quality and Authenticity -- 13.1 Introduction -- 13.2 Overview of the Chapter -- 13.3 Application of Hyperspectral Imaging for Red Meat Quality -- 13.3.1 Real-Time Multispectral Imaging System for Predicting Color Parameters in Red Meat.

13.3.2 Real-Time Multispectral Imaging System for Predicting Water Holding Capacity in Red Meat -- 13.3.3 Real-Time Multispectral Imaging System for Predicting Water Holding Capacity in Red Meat -- 13.3.3.1 Hyperspectral Imaging for Predicting Quality and Authenticity of Lamb Meat -- 13.3.3.2 Application of Hyperspectral Imaging for Adulteration Detection in Minced Meat -- 13.3.3.3 Adulteration Detection in Minced Lamb -- 13.3.3.4 Hyperspectral Imaging for Adulteration Detection in Beef -- 13.4 Conclusions -- References -- Chapter 14 Hyperspectral Imaging: Applications in Analysis of Fruits for Quality and Safety -- 14.1 Introduction -- 14.2 Application of HSI in Fruit Quality -- 14.2.1 HSI System -- 14.2.2 Application in Analysis of Quality of Fruits -- 14.3 Conclusion -- References -- Chapter 15 Applications in Vegetables -- 15.1 Soybean -- 15.2 Mushroom -- 15.3 Potato -- 15.4 Tomato -- 15.5 Cucumber -- 15.6 Lettuce -- 15.7 Spinach -- 15.8 Peppers -- 15.9 Onion -- 15.10 Broccoli -- Abbreviations -- References -- Chapter 16 Applications in Medicinal Herbs and Pharmaceuticals -- 16.1 Medicinal Herbs and Teas -- 16.2 Pharmaceuticals -- References -- Chapter 17 Hyperspectral Imaging in Dairy Products Analysis -- 17.1 Introduction -- 17.2 Cheese Analysis -- 17.3 Authentication of Dairy Products -- 17.4 Conclusions and Future Trends -- References -- Chapter 18 Hyperspectral Imaging: Application in Quality and Safety of Beverages -- 18.1 Introduction -- 18.2 Process for Hyper Spectral Imaging Analysis of Food -- 18.3 Application of HSI in Safety and Quality Beverages -- 18.3.1 Alcoholic Beverages -- 18.3.1.1 Beer -- 18.3.1.2 Wine -- 18.3.1.3 Whisky -- 18.3.2 Nonalcoholic Beverages -- 18.3.2.1 Coffee -- 18.3.2.2 Tea -- 18.3.2.3 Fruit-Based Beverages -- 18.4 Conclusion -- References.

Chapter 19 Raman Hyperspectral Imaging: Application in Food Additives' Quality and Safety -- 19.1 Introduction -- 19.2 Raman Hyperspectral Imaging -- 19.2.1 Instrumentation -- 19.2.2 System Calibration -- 19.2.2.1 Spectral Calibration -- 19.2.2.2 Spatial Calibration -- 19.2.3 Image Acquisition and Spectral Extraction -- 19.3 Characterization of Food Additives -- 19.4 Impurity Profile of Food Additives -- 19.5 Nanoparticle Food Additives -- 19.6 Conclusion -- References -- Index.