Problem-Oriented Analytical Chemistry Driven by Chemometrics covers challenges that are relevant to many fields using analytical techniques, including food quality and safety, environmental studies, product authentication, and petrochemistry using an analytical data science approach. The book's novel approach starts from case studies, allowing analytical chemists to understand how seemingly impenetrable chemometrics techniques can be applied to solve problems. It is written primarily to assist graduate and postdoc analytical chemists and early researchers, but has applicability to industry, too.
Many analytical chemists dealing with complex, real-world problems face significant difficulties extracting information from their data. In many cases, solutions are available in the chemometrics arena but relevant books are usually too technical and do not always focus on the 'general perspective' needed by the analyst when facing those complex tools.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Foreword - p. xiii 8. Preface - p. xv 9. Chapter 1. A multivariate approach to Analytical Chemistry - p. 1 10. Chapter 2. Analytical signals, analytical data, and instruments - p. 21 11. Chapter 3. Preprocessing of analytical signals and reduction of analytical data - p. 51 12. Chapter 4. Simultaneous multioptimization of operational variables - p. 75 13. Chapter 5. Multiobjective and multiresponse optimization - p. 97 14. Chapter 6. Analytical similarity analysis - p. 125 15. Chapter 7. Analytical qualification of materials - p. 151 16. Chapter 8. Analytical quantification of analytes and materials - p. 175 17. Chapter 9. Variable selection for analytical chemistry data - p. 203 18. Chapter 10. Validation of multivariate analytical methods - p. 219 19. Chapter 11. Multivariate curve resolution - p. 239 20. Chapter 12. Standardization and transfer of multivariate analytical models - p. 249 21. Chapter 13. N-way signals and data - p. 267 22. Chapter 14. A picture is worth one thousand spectra: multivariate and hyperspectral image analysis of complex matrices - p. 293 23. Chapter 15. Data analysis for microfluidic based analytical devices - p. 311 24. Chapter 16. Data analysis in applications of chromatography coupled with mass spectrometry and ion mobility spectrometry - p. 335 25. Chapter 17. Data analysis for analytical applications of nuclear magnetic resonance - p. 359 26. Index - p. 387
Problem-Oriented Analytical Chemistry Driven by Chemometrics covers challenges that are relevant to many fields using analytical techniques, including food quality and safety, environmental studies, product authentication, and petrochemistry using an analytical data science approach. The book's novel approach starts from case studies, allowing analytical chemists to understand how seemingly impenetrable chemometrics techniques can be applied to solve problems. It is written primarily to assist graduate and postdoc analytical chemists and early researchers, but has applicability to industry, too.
Many analytical chemists dealing with complex, real-world problems face significant difficulties extracting information from their data. In many cases, solutions are available in the chemometrics arena but relevant books are usually too technical and do not always focus on the 'general perspective' needed by the analyst when facing those complex tools.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Foreword - p. xiii 8. Preface - p. xv 9. Chapter 1. A multivariate approach to Analytical Chemistry - p. 1 10. Chapter 2. Analytical signals, analytical data, and instruments - p. 21 11. Chapter 3. Preprocessing of analytical signals and reduction of analytical data - p. 51 12. Chapter 4. Simultaneous multioptimization of operational variables - p. 75 13. Chapter 5. Multiobjective and multiresponse optimization - p. 97 14. Chapter 6. Analytical similarity analysis - p. 125 15. Chapter 7. Analytical qualification of materials - p. 151 16. Chapter 8. Analytical quantification of analytes and materials - p. 175 17. Chapter 9. Variable selection for analytical chemistry data - p. 203 18. Chapter 10. Validation of multivariate analytical methods - p. 219 19. Chapter 11. Multivariate curve resolution - p. 239 20. Chapter 12. Standardization and transfer of multivariate analytical models - p. 249 21. Chapter 13. N-way signals and data - p. 267 22. Chapter 14. A picture is worth one thousand spectra: multivariate and hyperspectral image analysis of complex matrices - p. 293 23. Chapter 15. Data analysis for microfluidic based analytical devices - p. 311 24. Chapter 16. Data analysis in applications of chromatography coupled with mass spectrometry and ion mobility spectrometry - p. 335 25. Chapter 17. Data analysis for analytical applications of nuclear magnetic resonance - p. 359 26. Index - p. 387
