Analytical and Chemometric Tools: An up-to-date Tutorial with Practical Applications to Food Security and Safety provides a one-stop tutorial in understanding the tools required for applying chemometrics to food and forensic chemistry. With a generous compilation of practical examples, the book covers a theoretical discussion of chemometric methods, summarizes up-too-date targeted and untargeted analytical methods in the field of forensic science, and presents real-life case studies applied to methods of chemometrics for food and forensic chemistry. This book is a valuable resource for chemists, forensic scientists, food scientists, students, and all those who wish to broaden their knowledge in the allied field.
1. Introduction - p. xi 2. Chapter 1. Spectral data preprocessing methods and strategies - p. 1 3. Chapter 2. Chromatographic and NMR data alignment and pretreatment - p. 41 4. Chapter 3. Exploratory data analysis: Projection and clustering strategies - p. 79 5. Chapter 4. Classical and cutting-edge regression methods - p. 107 6. 4.1 Introduction - p. 107 7. 4.2 Regression - p. 108 8. 4.3 Classification - p. 121 9. 4.4 Machine learning techniques for regression and classification - p. 122 10. 4.5 Artificial neural networks - p. 129 11. 4.6 Local models - p. 134 12. 4.7 Data fusion approaches - p. 136 13. 4.8 Validation - p. 142 14. 4.9 Conclusions - p. 146 15. Chapter 5. Looking for significant connections: An overview on classification - p. 151 16. Chapter 6. Multispectral and hyperspectral image analysis of food matrices - p. 199 17. Chapter 7. Food forensics: Chemometrics in the fight against food fraud - p. 239 18. Index - p. 261
Analytical and Chemometric Tools: An up-to-date Tutorial with Practical Applications to Food Security and Safety provides a one-stop tutorial in understanding the tools required for applying chemometrics to food and forensic chemistry. With a generous compilation of practical examples, the book covers a theoretical discussion of chemometric methods, summarizes up-too-date targeted and untargeted analytical methods in the field of forensic science, and presents real-life case studies applied to methods of chemometrics for food and forensic chemistry. This book is a valuable resource for chemists, forensic scientists, food scientists, students, and all those who wish to broaden their knowledge in the allied field.
1. Introduction - p. xi 2. Chapter 1. Spectral data preprocessing methods and strategies - p. 1 3. Chapter 2. Chromatographic and NMR data alignment and pretreatment - p. 41 4. Chapter 3. Exploratory data analysis: Projection and clustering strategies - p. 79 5. Chapter 4. Classical and cutting-edge regression methods - p. 107 6. 4.1 Introduction - p. 107 7. 4.2 Regression - p. 108 8. 4.3 Classification - p. 121 9. 4.4 Machine learning techniques for regression and classification - p. 122 10. 4.5 Artificial neural networks - p. 129 11. 4.6 Local models - p. 134 12. 4.7 Data fusion approaches - p. 136 13. 4.8 Validation - p. 142 14. 4.9 Conclusions - p. 146 15. Chapter 5. Looking for significant connections: An overview on classification - p. 151 16. Chapter 6. Multispectral and hyperspectral image analysis of food matrices - p. 199 17. Chapter 7. Food forensics: Chemometrics in the fight against food fraud - p. 239 18. Index - p. 261
