《化学计量学基础》内容简介：化学计量学在化学量测中的采样理论与实验设计、化学数据处理、分析信号解析与分辨、化学分类决策与预报等方面，解决了大量传统的化学研究方法难以解决的复杂问题，显示了其强大的生命力，已受到化学尤其是分析化学工作者的极大关注。

图书信息

图书目录

图书信息

书 名: 化学计量学基础

作　者：梁逸曾

出版社： 华东理工大学出版社

出版时间： 2010年10月1日

ISBN: 9787562828716

开本： 16开

定价: 38.00元

图书目录

Chapter 1 Introduction and Necessary Fundamental Knowledge of Mathematics

1.1 Chemometrics: Definition and Its Brief History / 3

1.2 The Relationship between Analytical Chemistry and Chemometrics / 4

1.3 The Relationship between Chemometrics, Chemoinformatics and Bioinformatics / 7

1.4 Necessary Knowledge of Mathematics / 9

1.4.1 Vector and Its Calculation / 10

1.4.2 Matrix and Its Calculation / 19

Chapter 2 Chemical Experiment Design

2.1 Introduction / 39

2.2 Factorial Design and Its Rational Analysis / 41

2.2.1 Computation of Effects Using Sign Tables / 44

2.2.2 Normal Plot of Effects and Residuals / 45

2.3 Fractional Factorial Design / 47

2.4 Orthogonal Design and Orthogonal Array / 52

2.4.1 Definition of Orthogonal Design Table / 53

2.4.2 Orthogonal Arrays and Their Inter-effect Tables / 54

2.4.3 Linear Graphs of Orthogonal Array and Its Applications / 55

2.5 Uniform Experimental Design and Uniform Design Table / 55

2.5.1 Uniform Design Table and Its Construction / 56

2.5.2 Uniformity Criterion and Accessory Tables for Uniform Design / 59

2.5.3 Uniform Design for Pseudo-level / 60

2.5.4 An Example for Optimization of Electropherotic Separation Using Uniform Design / 61

2.6 D-Optimal Experiment Design / 65

2.7 Optimization Based on Simplex and Experiment Design / 68

2.7.1 Constructing an Initial Simplex to Start the Experiment Design / 69

2.7.2 Simplex Searching and Optimization / 70

Chapter 3 Processing of Analytic Signals

3.1 Smoothing Methods of Analytical Signals / 77

3.1.1 Moving-Window Average Smoothing Method / 77

3.1.2 Savitsky-Golay Filter / 77

3.2 Derivative Methods of Analytical Signals / 83

3.2.1 Simple Difference Method / 83

3.2.2 Moving-Window Polynomial Least-Squares Fitting Method / 84

3.3 Background Correction Method of Analytical Signals / 89

3.3.1 Penalized Least Squares Algorithm / 89

3.3.2 Adaptive Iteratively Reweighted Procedure / 90

3.3.3 Some Examples for Correcting the Baseline from Different Instruments / 92

3.4 Transformation Methods of Analytical Signals / 94

3.4.1 Physical Meaning of the Convolution Algorithm / 94

3.4.2 Multichannel Advantage in Spectroscopy and Hadamard Transformation / 96

3.4.3 Fourier Transformation / 99

Appendix 1.A Matlab Program for Smoothing the Analytical Signals / 108

Appendix 2 :A Matlab Program for Demonstration of FT Applied to Smoothing / 112

Chapter 4 Multivariate Calibration and Multivariate Resolution

4.1 Multivariate Calibration Methods for White Analytical Systems / 116

4.1.1 Direct Calibration Methods / 116

4.1.2 Indirect Calibration Methods / 121

4.2 Multivariate Calibration Methods for Grey Analytical Systems / 126

4.2.1 Vectoral Calibration Methods / 127

4.2.2 Matrix Calibration Methods / 127

4.3 Multivariate Resolution Methods for Black Analytical Systems / 129

4.3.1 Self-modeling Curve Resolution Method / 131

4.3.2 Iterative Target Transformation Factor Analysis / 134

4.3.3 Evolving Factor Analysis and Related Methods / 137

4.3.4 Window Factor Analysis / 141

4.3.5 Heuristic Evolving Latent Projections / 145

4.3.6 Subwindow Factor Analysis / 152

4.4 Multivariate Calibration Methods for Generalized Grey Analytical Systems / 154

4.4.1 Principal Component Regression （PCR） / 156

4.4.2 Partial Least Squares （PLS） / 157

4.4.3 Leave-one-out Cross-validation / 159

Chapter 5 Pattern Recognition and Pattern Analysis for Chemical Analytical Data

5.1 Introduction / 169

5.1.1 Chemical Pattern Space / 169

5.1.2 Distance in Pattern Space and Measures of Similarity / 171

5.1.3 Feature Extraction Methods / 173

5.1.4 Pretreatment Methods for Pattern Recognition / 173

5.2 Supervised Pattern Recognition Methods: Discriminant Analysis Methods / 174

5.2.1 Discrimination Method Based on Euclidean Distance / 175

5.2.2 Discrimination Method Based on Mahaianobis Distance / 175

5.2.3 Linear Learning Machine / 176

5.2.4 k-Nearest Neighbors Discrimination Method / 177

5.3 Unsupervised Pattern Recognition Methods: Clustering Analysis Methods / 179

5.3.1 Minimum Spanning Tree Method / 179

5.3.2 k-means Clustering Method / 181

5.4 Visual Dimensional Reduction Based on Latent Projections / 183

5.4.1 Projection Discrimination Method Based on Principal Component Analysis / 183

5.4.2 SMICA Method Based on Principal Component Analysis / 186

5.4.3 Classification Method Based on Partial Least Squares / 193