LEADER 08277nam 22004093 450 001 9910877060303321 005 20240706060244.0 010 $a1-119-63939-5 010 $a1-119-63937-9 035 $a(MiAaPQ)EBC31516123 035 $a(Au-PeEL)EBL31516123 035 $a(CKB)32644616900041 035 $a(EXLCZ)9932644616900041 100 $a20240706d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Analysis and Chemometrics for Metabolomics 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2024. 210 4$d©2024. 215 $a1 online resource (435 pages) 311 $a1-119-63938-7 327 $aCover -- Title Page -- Copyright Page -- Contents -- Foreword -- Acknowledgments -- About the Companion Website -- Chapter 1 Introduction -- 1.1 Chemometrics -- 1.2 Metabolomics -- 1.3 Case Studies -- 1.4 Software -- References -- Chapter 2 Instrumental Methods -- 2.1 Introduction -- 2.2 Coupled Chromatography Mass Spectrometry -- 2.2.1 Chromatography -- 2.2.2 Ionisation and Detection -- 2.2.2.1 GCMS -- 2.2.2.2 LCMS -- 2.2.3 Data Matrices and Peak Tables -- 2.2.4 Step 1 of Creating a Peak Table: Transforming Chromatographic Data to an Aligned Matrix of Peaks -- 2.2.4.1 XCMS -- 2.2.4.2 Multivariate Curve Resolution -- 2.2.4.3 AMDIS -- 2.2.4.4 MZmine -- 2.2.4.5 Other Approaches -- 2.2.4.6 Which Approach is Most Suitable? -- 2.2.5 Step 2 of Creating a Peak Table: Manual Inspection -- 2.2.6 Step 3 of Creating a Peak Table: Identifying Metabolites and Annotating the Peaks -- 2.2.6.1 Experimental Libraries -- 2.2.6.2 Computational Mass Spectral and Retention Libraries -- 2.2.6.3 Expert Systems -- 2.3 Single Wavelength HPLC -- 2.4 Nuclear Magnetic Resonance -- 2.4.1 Fourier Transform Techniques -- 2.4.1.1 FT Principles -- 2.4.1.2 Resolution and Signal-to-Noise -- 2.4.2 Preparing the Transformed Spectra -- 2.4.3 Preparing the Data Table -- 2.4.3.1 Chemometric Approach -- 2.4.3.2 Deconvolution and Identification -- 2.4.4 Identification of Metabolites -- 2.5 Vibrational Spectroscopy -- 2.5.1 Raman Spectroscopy -- 2.5.2 Fourier Transform Infrared Spectroscopy -- Chapter 3 Case Studies -- 3.1 Introduction -- 3.2 Case Study 1: Presymptomatic Study of Humans With Rheumatoid Arthritis Using Blood Plasma and LCMS -- 3.3 Case Study 2: Diagnosis of Malaria in Human Blood Plasma of Children Using GCMS -- 3.4 Case Study 3: Measurement of Triglyclerides in Children's Blood Serum Using NMR. 327 $a3.5 Case Study 4: Glucose Intolerance and Diabetes in Humans as Assessed by Blood Serum Using NMR -- 3.6 Case Study 5: Metabolic Changes in Maize Due to Cold as Assessed by NMR -- 3.7 Case Study 6: Effect of Nitrates on Different Parts of Wheat Leaves as Analysed by FTIR -- 3.8 Case Study 7: Rapid Discrimination of Enterococcal Bacteria in Faecal Isolates by Raman Spectroscopy -- 3.9 Case Study 8: Effects of Salinity, Temperature and Hypoxia on Daphnia Magna Metabolism as Studied by GCMS -- 3.10 Case Study 9: Bioactivity in a Chinese Herbal Medicine Studies Using HPLC -- 3.11 Case Study 10: Diabetes in Mice Studied by LCMS -- Chapter 4 Principal Component Analysis -- 4.1 A Simple Example: Matrices, Vectors and Scalars -- 4.2 Visualising the Data Direct -- 4.3 Principal Components Analysis: Scores, Loadings and Eigenvalues -- 4.3.1 PCA -- 4.3.2 Scores -- 4.3.3 Loadings -- 4.3.4 Relationship Between Scores and Loadings -- 4.3.5 Eigenvalues -- 4.3.6 Reducing the Number of PCs -- 4.4 Exploration by PCA of Case Study 5 in Detail: NMR Study of the Effect of Temperature on Maize -- 4.4.1 Variable Plots -- 4.4.2 Scores and Loadings Plots of the Whole Standardised Data -- 4.4.3 Scores and Loadings of the Low-temperature Data -- 4.5 PCA of Different Case Studies -- 4.5.1 Case Study 1: LCMS Studies of Pre-arthritis -- 4.5.2 Case Study 4: NMR of Human Diabetes -- 4.5.3 Case Study 10: LCMS of Diabetes in Mice -- 4.5.4 Case Study 6: FTIR of Effect of Nitrates on Wheat -- 4.5.5 Case Study 3: NMR for Triglycerides in Serum -- 4.5.6 Case Study 7: Raman of Bacterial Faecal Isolates -- 4.6 Transforming the Data -- 4.6.1 Row Scaling -- 4.6.1.1 Row Scaling to Constant Total -- 4.6.1.2 Standard Normal Variates -- 4.6.1.3 Scaling to Reference Standards -- 4.6.2 Column Centring -- 4.6.3 Column Standardisation -- 4.6.4 Logarithmic Transformation -- 4.7 Common Issues. 327 $a4.7.1 Missing Data -- 4.7.2 Quality Control Samples -- 4.7.3 Variable Reduction -- Chapter 5 Statistical Basics -- 5.1 Use of P Values and Hypothesis Testing -- 5.2 Distributions and Significance -- 5.2.1 Simulated Case Study -- 5.2.2 The Normal (z) Distribution and p Values -- 5.2.3 t-Distribution and Degrees of Freedom -- 5.2.4 ?2-Distribution -- 5.2.5 F-Distribution and Hotelling's T2 -- 5.3 Multivariate Calculation of P Values and the Mahalanobis Distance -- 5.4 Discriminatory Variables -- 5.5 Conclusions -- Chapter 6 Choosing Samples -- 6.1 Motivation -- 6.2 Design of Experiments -- 6.2.1 Factors, Response and Coding -- 6.2.2 Replicates -- 6.2.3 Statistical Designs -- 6.2.3.1 Fully Crossed Designs -- 6.2.3.2 Two-level Full Factorial Designs -- 6.2.3.3 Fractional Factorial Designs -- 6.3 Sampling Designs -- 6.3.1 Simple Random Sampling -- 6.3.2 Systematic Sampling -- 6.3.3 Stratified Sampling -- 6.3.4 Cluster Sampling -- 6.3.5 Multi-stage Sampling -- Chapter 7 Determining the Provenance of a Sample -- 7.1 Pattern Recognition -- 7.2 Preliminary Processing Prior to Classification -- 7.3 Simulated Case Studies -- 7.4 Two-Class Classifiers -- 7.4.1 Linear Discriminant Analysis -- 7.4.2 Partial Least Squares Discriminant Analysis -- 7.4.2.1 PLSDA for Equal Class Sizes -- 7.4.2.2 PLSDA for Unequal Class Sizes -- 7.4.2.3 OPLS -- 7.5 One-Class Classifiers -- 7.5.1 Quadratic Discriminant Analysis -- 7.5.2 SIMCA -- 7.5.2.1 Disjoint PCA -- 7.5.2.2 D- and Q-statistics -- 7.5.2.3 Limits and Decisions -- 7.6 Multiclass Classifiers -- 7.6.1 LDA as a Multiclass Classifier -- 7.6.2 PLSDA as a Multiclass Classifier -- 7.6.2.1 One Versus All -- 7.6.2.2 One Versus One -- 7.6.2.3 PLS2DA -- 7.6.3 Multilevel PLSDA -- 7.7 Validation, Optimisation and Performance Indicators -- 7.7.1 Classification Performance -- 7.7.1.1 Two Classes -- 7.7.1.2 Multiclasses. 327 $a7.7.1.3 One-Class Models -- 7.7.2 Validation -- 7.7.3 Optimisation -- Chapter 8 Multivariate Calibration -- 8.1 Introduction -- 8.2 Partial Least Squares Regression -- 8.3 Training and Test Sets -- 8.4 Optimisation: Number of PLS Components -- Chapter 9 Selecting the Most Significant Variables and Markers -- 9.1 Introduction -- 9.2 Univariate Approaches -- 9.3 Loadings, Weights And Vip Scores -- 9.3.1 Principal Component Loadings -- 9.3.2 PLSDA Loadings and Weights -- 9.3.3 VIP Scores -- 9.3.4 P Values -- 9.3.5 Multilevel PLSDA -- 9.4 Selectivity Ratios -- 9.5 Volcano Plots -- Chapter 10 Which Factors are Most Significant -- 10.1 Introduction -- 10.2 Terminology and Definitions -- 10.3 Single Factor (One-Way - One-Factor) Anova Test And Regression -- 10.3.1 Balanced Design at Two Levels -- 10.3.1.1 Degrees of Freedom -- 10.3.1.2 ANOVA Test -- 10.3.1.3 Regression -- 10.3.1.4 The t-test -- 10.3.2 Unbalanced Design at Two Levels -- 10.3.3 Multiple One-Way Design with Two Levels: Multilinear Regression -- 10.3.4 Multilevel Designs -- 10.3.4.1 One-Way Multilevel ANOVA Test -- 10.3.4.2 One-Way Multilevel Regression with Dummy Variables: Unrelated Groups -- 10.3.4.3 One-Way Multilevel Multilinear Regression: Related Groups -- 10.3.4.4 Comparison and Interpretation -- 10.4 Multiple Factor (Multiway) ANOVA Test and Regression -- 10.4.1 Simulated 2 × 3 Case Study: ANOVA Test and Regression -- 10.4.1.1 ANOVA Test -- 10.4.1.2 Regression with Dummy Variables -- 10.4.2 Two-level Multiway Factorial Designs -- 10.5 ASCA -- 10.5.1 Simulated Dataset -- 10.5.2 Case Study: Environmental Effect on Daphnia -- Index -- EULA. 700 $aBrereton$b Richard G$0283014 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910877060303321 996 $aData Analysis and Chemometrics for Metabolomics$94200120 997 $aUNINA