化学计量学基础

《化来自学计量学基础》是2010年华东理工大学出360百科版社出版的图书，作者是梁逸曾。

• 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
• 定价 38 元

内容简介

　　化学计量学在化学量测中的来自采样理论与实验设计、化学数据处理、分析信号解析360百科与分辨、化学分类决策与预肉担犯报等方面，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。

图书目录

　　Chapter 1 Introduction and Necessary Fundamental Knowledge of Mathematics

　　1.1 Chemometrics: Definition an女即革拉d Its Brie喜陈绿使振弦短班就流绍f History / 3

　　1.2 含呢激校经规显众收脱型The Relationship between Anal第何为听ytical Chemistry and C树味生超也相hemometrics / 4

　　1.3 The Relationsh然代ip between Chemometrics, Ch目守张致杂丝聚emoinformatics and Bioinformatics / 7

　　1.4 Necessary Knowledge of Mathematics / 9

　　1.4.1 Vector and Its Calculat卫局安兴ion / 10

　　1.4厂苦兵大模县经侵前采候.2 Matrix and Its Calculation / 19

　　Chapte场整万r 2 Chemical Experiment Design

　　2.1 Introduction / 39

　测倒部营永管河少　2.2 Factorial Design and Its Rational Analysis / 41

　　2.2.1 Co却探元陆前的mputation of 面溶型概杂祖解证完长Effects Using S气移史ign Tables / 44

我边技束践挥没翻有　　2.2.2 Normal Plot of Effects and Residuals / 45

聚讨期府察练调略问　　2.3 Fractional Factorial Design / 47

　　2.4 Orth营ogonal 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