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<p>Foreword xi</p> <p>Acknowledgements xv</p> <p>About the Companion Website xvii</p> <p><b>CHAPTER 1 Introduction 1</b></p> <p>1.1 Chemometrics 1</p> <p>1.2 Metabolomics 9</p> <p>1.3 Case Studies 14</p> <p>1.4 Software 15</p> <p><b>CHAPTER 2 Instrumental Methods 26</b></p> <p>2.1 Introduction 26</p> <p>2.2 Coupled Chromatography Mass Spectrometry 27</p> <p>2.3 Single Wavelength HPLC 47</p> <p>2.4 Nuclear Magnetic Resonance 50</p> <p>2.5 Vibrational Spectroscopy 61</p> <p><b>CHAPTER 3 Case Studies 66</b></p> <p>3.1 Introduction 66</p> <p>3.2 Case Study 1: Presymptomatic Study of Humans with Rheumatoid Arthritis Using Blood Plasma and LCMS 67</p> <p>3.3 Case Study 2: Diagnosis of Malaria in Human Blood Plasma of Children Using GCMS 69</p> <p>3.4 Case Study 3: Measurement of Triglyclerides In Children's Blood Serum Using NMR 70</p> <p>3.5 Case Study 4: Glucose Intolerance and Diabetes in Humans as Assessed by Blood Serum Using NMR 71</p> <p>3.6 Case Study 5: Metabolic Changes in Maize Due to Cold as Assessed By NMR 72</p> <p>3.7 Case Study 6: Effect of Nitrates on Different Parts of Wheat Leaves as Analysed by FTIR 74</p> <p>3.8 Case Study 7: Rapid Discrimination of Enterococcal Bacteria in Faecal Isolates by Raman Spectroscopy 75</p> <p>3.9 Case Study 8: Effects of Salinity, Temperature and Hypoxia on Daphnia Magna Metabolism as Studied by GCMS 76</p> <p>3.10 Case Study 9: Bioactivity in a Chinese Herbal Medicine Studies Using HPLC 77</p> <p>3.11 Case Study 10: Diabetes in Mice Studied by LCMS 78</p> <p><b>CHAPTER 4 Principal Component Analysis 80</b></p> <p>4.1 A Simple Example: Matrices, Vectors and Scalars 80</p> <p>4.2 Visualising the Data Direct 81</p> <p>4.3 Principal Components Analysis: Scores, Loadings and Eigenvalues 84</p> <p>4.4 Exploration by PCA of Case Study 5 in Detail: NMR Studyof the Effect of Temperature on Maize 92</p> <p>4.5 PCA of Different Case Studies 98</p> <p>4.6 Transforming the Data 114</p> <p>4.7 Common Issues 144</p> <p><b>CHAPTER 5 Statistical Basics 151</b></p> <p>5.1 Use of P Values and Hypothesis Testing 151</p> <p>5.2 Distributions and Significance 152</p> <p>5.3 Multivariate Calculation of P Values and the Mahalanobis Distance 182</p> <p>5.4 Discriminatory Variables 190</p> <p>5.5 Conclusions 194</p> <p><b>CHAPTER 6 Choosing Samples 195</b></p> <p>6.1 Motivation 195</p> <p>6.2 Design of Experiments 196</p> <p>6.3 Sampling Designs 206</p> <p><b>CHAPTER 7 Determining the Provenance of a Sample 210</b></p> <p>7.1 Pattern Recognition 210</p> <p>7.2 Preliminary Processing Prior to Classification 211</p> <p>7.3 Simulated Case Studies 212</p> <p>7.4 Two-Class Classifiers 218</p> <p>7.5 One-Class Classifiers 244</p> <p>7.6 Multiclass Classifiers 272</p> <p>7.7 Validation, Optimisation and Performance Indicators 288</p> <p><b>CHAPTER 8 Multivariate Calibration 305</b></p> <p>8.1 Introduction 305</p> <p>8.2 Partial Least Squares Regression 306</p> <p>8.3 Training and Test Sets 310</p> <p>8.4 Optimisation: Number of PLS Components 317</p> <p><b>CHAPTER 9 Selecting the Most Significant Variables and Markers 320</b></p> <p>9.1 Introduction 320</p> <p>9.2 Univariate Approaches 320</p> <p>9.3 Loadings, Weights and VIP Scores 324</p> <p>9.4 Selectivity Ratios 346</p> <p>9.5 Volcano Plots 349</p> <p><b>CHAPTER 10 Which Factors are Most Significant 352</b></p> <p>10.1 Introduction 352</p> <p>10.2 Terminology and Definitions 353</p> <p>10.3 Single Factor (One-Way - One-Factor) ANOVA Test and Regression 357</p> <p>10.4 Multiple Factor (Multiway) ANOVA Test and Regression 379</p> <p>10.5 ASCA 389</p> <p>Index 406</p>

<p><b>Richard G. Brereton, PhD,</b> is Professor Emeritus at the University of Bristol, UK, and Director of Brereton Consultancy. He has published widely on chemometrics and related subjects, and serves as Editor-in-Chief for Heritage Science.

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