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Applied chemometrics for scientists
Abstract
1. Introduction : Development of chemometrics 1; Application areas 3; 2. Experimental design : Why design experiments in chemistry? 9; Degrees of freedom and sources of error 12; Analysis of variance and interpretation of errors 16; Matrices, vectors and the pseudoinverse 20; Design matrices 22; Factorial designs 25 … Designs for multivariate calibration 58; 3.
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Statistical concepts : Statistics for chemists 63; Errors 64; Describing data 67; The normal distribution 73; Is a distribution normal? 76; Hypothesis tests 80; Comparison of means: the t-test 81; F-test for comparison of variances 85; Confidence in linear regression 89; More about confidence 93; Consequences of outliers and how to deal with them 96; Detection of outliers 100; Shewhart charts 104; More about control charts 106; 4. Sequential methods : Sequential data 111; Correlograms 112; Linear smoothing functions and filters 116; Fourier transforms 120; Maximum entropy and Bayesian methods 124; Fourier filters 128; Peakshapes in chromatography and spectroscopy 134; Derivatives in spectroscopy and chromatography 138; Wavelets 142; 5. Pattern recognition 145 : Principal components analysis 147; Graphical representation of scores and loadings 154; Comparing multivariate patterns 159; Preprocessing 160; Unsupervised pattern recognition: cluster analysis 167; Supervised pattern recognition 171; Statistical classification techniques 174; K nearest neighbour method 182; How many components characterize a dataset? 185; Multiway pattern recognition 187; 6. Calibration 193 : Univariate calibration 195; Multivariate calibration and the spectroscopy of mixtures 202; Multiple linear regression 206; Principal components regression 208; Partial least squares 211; How good is the calibration and what is the most appropriate model? 214; Multiway calibration 217; 7. Coupled chromatography 221 : Preparing the data 222; Chemical composition of sequential data 228; Univariate purity curves 230; Similarity based methods 234; Evolving and window factor analysis 236; Derivative based methods 239; Deconvolution of evolutionary signals 241; Noniterative methods for resolution 242; Iterative methods for resolution 246; 8. Equilibria, reactions and process analytics : The study of equilibria using spectroscopy 249; Spectroscopic monitoring of reactions 252; Kinetics and multivariate models for the quantitative study of reactions 257; Developments in the analysis of reactions using on-line spectroscopy 261; The process analytical technology initiative 263; 9. Improving yields and processes using experimental designs 267 : Use of statistical designs for improving the performance of synthetic reactions 269; Screening for factors that influence the performance of a reaction 271; Optimizing the process variables 275; Handling mixture variables using simplex designs 278; More about mixture variables 283; 10. Biological and medical applications of chemometrics 287 : Taxonomy 289; Discrimination 291; Mahalanobis distance 297; Bayesian methods and contingency tables 300; Support vector machines 303; Discriminant partial least squares 306; Micro-organisms 308; Medical diagnosis using spectroscopy 313; metabolomics using coupled chromatography and nuclear magnetic resonance 314; 11. Biological macromolecules 319 : Sequence alignment and scoring matches 320; Sequence similarity 322; Tree diagrams 324; Phylogenetic trees 327; 12. Multivariate image analysis 331 : Scaling images 333; Filtering and smoothing the image 335; Principal components for the enhancement of images 337; Regression of images 340; Alternating least squares as employed in image analysis 345; Multiway methods in image analysis 347; 13. Food 351 : How to determine the origin of a food product using chromatography 353; Near infrared spectroscopy 354; Other information 356; Sensory analysis: linking composition to properties 357; Varimax rotation 359; Calibrating sensory descriptors to composition 365; Index 369; Contents V.
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