化学计量学基础【2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载】

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Chapter 1 Introduction and Necessary Fundamental Knowledge of Mathematics3

1.1 Chemometrics:Definition and Its Brief History3

1.2　The Relationship between Analytical Chemistry and Chemometries4

1.3　The Relationship between Chemometrics,Chemoinformatics and Bioinformatic7

1.4　Necessary Knowledge of Mathematics9

1.4.1　Vector and Its Calculation10

1.4.2　Matrix and Its Calculation19

Chapter 2　Chemical Experiment Design39

2.1 Introduction39

2.2　Factorial Design and Its Rational Analysis41

2.2.1 Computation of Effects Using Sign Tables44

2.2.2　Normal Plot of Effects and Residuals45

2.3　Fractional Factorial Design47

2.4　Orthogonal Design and Orthogonal Array52

2.4.1　Definition of Orthogonal Design Table53

2.4.2　Orthogonal Arrays and Their Inter-effect Tables54

2.4.3　Linear Graphs of Orthogonal Array and Its Applications55

2.5　Uniform Experimental Design and Uniform Design Table55

2.5.1 Uniform Design Table and Its Construction56

2.5.2　Uniformity Criterion and Accessory Tables for Uniform Design59

2.5.3　Uniform Design for Pseudo-level60

2.5.4　An Example for Optimization of Eleetropherotic Separation Using Uniform Design61

2.6　D-Optimal Experiment Design65

2.7　Optimization Based on Simplex and Experiment Design68

2.7.1　Constructing an Initial Simplex to Start the Experiment Design69

2.7.2　Simplex Searching and Optimization70

Chapter 3　Processing of Analytic Signals77

3.1 Smoothing Methods of Analytical Signals77

3.1.1　Moving-Window Average Smoothing Method77

3.1.2 Savitsky-Golay Filter77

3.2　Derivative Methods of Analytical Signals83

3.2.1 Simple Difference Method83

3.2.2　Moving-Window Polynomial Least-Squares Fitting Method84

3.3　Background Correction Method of Analytical Signals89

3.3.1　Penalized Least Squares Algorithm89

3.3.2　Adaptive Iteratively Reweighted Procedure90

3.3.3　Some Examples for Correcting the Baseline from Different Instruments92

3.4　Transformation Methods of Analytical Signals94

3.4.1　Physical Meaning of the Convolution Algorithm94

3.4.2 Multichannel Advantage in Spectroscopy and Hadamard Transformation96

3.4.3　Fourier Transformation99

Appendix 1:A Matlab Program for Smoothing the Analytical Signals108

Appendix 2:A Matlab Program for Demonstration of FT Applied to Smoothing112

Chapter 4　Multivariate Calibration and Multivariate Resolution116

4.1　Multivariate Calibration Methods for White Analytical Systems116

4.1.1　Direct Calibration Methods116

4.1.2　Indirect Calibration Methods121

4.2　Multivariate Calibration Methods for Grey Analytical Systems126

4.2.1　Veetoral Calibration Methods127

4.2.2　Matrix Calibration Methods127

4.3　Multivariate Resolution Methods for Black Analytical Systems129

4.3.1　Self-modeling Curve Resolution Method131

4.3.2　Iterative Target Transformation Factor Analysis134

4.3.3　Evolving Factor Analysis and Related Methods137

4.3.4　Window Factor Analysis141

4.3.5　Heuristic Evolving Latent Projections145

4.3.6　Subwindow Factor Analysis152

4.4　Multivariate Calibration Methods for Generalized Grey Analytical Systems154

4.4.1　Principal Component Regression(PCR)156

4.4.2　Partial Least Squares(PLS)157

4.4.3　Leave-one-out Cross-validation159

Chapter 5　Pattern Recognition and Pattern Analysis for Chemical Analytical Data5.1 Introduction169

5.1.1 Chemieal Pattern Space169

5.1.2　Distance in Pattern Space and Measures of Similarity171

5.1.3 Feature Extraction Methods173

5.1.4 Pretreatment Methods for Pattern Recognition173

5.2 Supervised Pattern Recognition Methods:Discriminant Analysis Methods174

5.2.1 Discrimination Method Based on Euclidean Distance175

5.2.2 Discrimination Method Based on Mahalanobis Distance175

5.2.3 Linear Learning Machine176

5.2.4 k-Nearest Neighbors Discrimination Method177

5.3 Unsupervised Pattern Recognition Methods:Clustering Analysis Methods179

5.3.1 Minimum Spanning Tree Method179

5.3.2 k-means Clustering Method181

5.4 Visual Dimensional Reduction Based on Latent Proiections183

5.4.1 Proj ection Discrimination Method Based on Principal Component Analysis183

5.4.2 SMICA Method Based on Principal Component Analysis186

5.4.3 Classification Method Based on Partial Least Squares193