化学计量学基础 本书特色
《化学计量学基础》：“十一五”国家重点图书化学与应用化学丛书,普通高等教育化学类专业规划教材,国家级双语教学示范课程配套教材
化学计量学基础 目录
Chapter 1 Introduction and Necessary Fundamental Knowledge of Mathematics1.1 Chemometrics: Definition and Its Brief History / 31.2 The Relationship between Analytical Chemistry and Chemometrics / 41.3 The Relationship between Chemometrics, Chemoinformatics and Bioinformatics / 71.4 Necessary Knowledge of Mathematics / 91.4.1 Vector and Its Calculation / 101.4.2 Matrix and Its Calculation / 19Chapter 2 Chemical Experiment Design2.1 Introduction / 392.2 Factorial Design and Its Rational Analysis / 412.2.1 Computation of Effects Using Sign Tables / 442.2.2 Normal Plot of Effects and Residuals / 452.3 Fractional Factorial Design / 472.4 Orthogonal Design and Orthogonal Array / 522.4.1 Definition of Orthogonal Design Table / 532.4.2 Orthogonal Arrays and Their Inter-effect Tables / 542.4.3 Linear Graphs of Orthogonal Array and Its Applications / 552.5 Uniform Experimental Design and Uniform Design Table / 552.5.1 Uniform Design Table and Its Construction / 562.5.2 Uniformity Criterion and Accessory Tables for Uniform Design / 592.5.3 Uniform Design for Pseudo-level / 602.5.4 An Example for Optimization of Electropherotic Separation Using Uniform Design / 612.6 D-Optimal Experiment Design / 652.7 Optimization Based on Simplex and Experiment Design / 682.7.1 Constructing an Initial Simplex to Start the Experiment Design / 692.7.2 Simplex Searching and Optimization / 70Chapter 3 Processing of Analytic Signals3.1 Smoothing Methods of Analytical Signals / 773.1.1 Moving-Window Average Smoothing Method / 773.1.2 Savitsky-Golay Filter / 773.2 Derivative Methods of Analytical Signals / 833.2.1 Simple Difference Method / 833.2.2 Moving-Window Polynomial Least-Squares Fitting Method / 843.3 Background Correction Method of Analytical Signals / 893.3.1 Penalized Least Squares Algorithm / 893.3.2 Adaptive Iteratively Reweighted Procedure / 903.3.3 Some Examples for Correcting the Baseline from Different Instruments / 923.4 Transformation Methods of Analytical Signals / 943.4.1 Physical Meaning of the Convolution Algorithm / 943.4.2 Multichannel Advantage in Spectroscopy and Hadamard Transformation / 963.4.3 Fourier Transformation / 99Appendix 1.A Matlab Program for Smoothing the Analytical Signals / 108Appendix 2 :A Matlab Program for Demonstration of FT Applied to Smoothing / 112Chapter 4 Multivariate Calibration and Multivariate Resolution4.1 Multivariate Calibration Methods for White Analytical Systems / 1164.1.1 Direct Calibration Methods / 1164.1.2 Indirect Calibration Methods / 1214.2 Multivariate Calibration Methods for Grey Analytical Systems / 1264.2.1 Vectoral Calibration Methods / 1274.2.2 Matrix Calibration Methods / 1274.3 Multivariate Resolution Methods for Black Analytical Systems / 1294.3.1 Self-modeling Curve Resolution Method / 1314.3.2 Iterative Target Transformation Factor Analysis / 1344.3.3 Evolving Factor Analysis and Related Methods / 1374.3.4 Window Factor Analysis / 1414.3.5 Heuristic Evolving Latent Projections / 1454.3.6 Subwindow Factor Analysis / 1524.4 Multivariate Calibration Methods for Generalized Grey Analytical Systems / 1544.4.1 Principal Component Regression （PCR） / 1564.4.2 Partial Least Squares （PLS） / 1574.4.3 Leave-one-out Cross-validation / 159Chapter 5 Pattern Recognition and Pattern Analysis for Chemical Analytical Data5.1 Introduction / 1695.1.1 Chemical Pattern Space / 1695.1.2 Distance in Pattern Space and Measures of Similarity / 1715.1.3 Feature Extraction Methods / 1735.1.4 Pretreatment Methods for Pattern Recognition / 1735.2 Supervised Pattern Recognition Methods: Discriminant Analysis Methods / 1745.2.1 Discrimination Method Based on Euclidean Distance / 1755.2.2 Discrimination Method Based on Mahaianobis Distance / 1755.2.3 Linear Learning Machine / 1765.2.4 k-Nearest Neighbors Discrimination Method / 1775.3 Unsupervised Pattern Recognition Methods: Clustering Analysis Methods / 1795.3.1 Minimum Spanning Tree Method / 1795.3.2 k-means Clustering Method / 1815.4 Visual Dimensional Reduction Based on Latent Projections / 1835.4.1 Projection Discrimination Method Based on Principal Component Analysis / 1835.4.2 SMICA Method Based on Principal Component Analysis / 1865.4.3 Classification Method Based on Partial Least Squares / 193
化学计量学基础 节选
《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。
化学计量学基础 相关资料
插图：More recently, a new discipline named chemoinformatics has newly developed. The markof appearance of this discipline is the publication of several books after 2000 [1-28-1-31].There are also several different definitions for this discipline. Here we just list three famousones as follows.The first one was given by F. K. Brown[1-32], that is, "The use of information technologyand management has become a critical part of the drug discovery process. Chemoirfformatics isthe mixing of information resources to transform data into information and information intoknowledge, for the intended purpose of making decisions faster in the arena of drug leadidentification and optimization. " G. Paris [1-33] also gave a definition, that "chemoinformaticsis a generic term that encompasses the design, creation, organization, management, retrieval,analysis, dissemination, visualization and use of chemical information. " The last one defined byGasteiger was a much more broad definition. It defines that "chemoirdormatics, the application ofinformafics methods to the solution of chemical problems. "[1-32] From this definition, one can seethat this definition wants to encompass everything working with computer for chemistry in it. In fact,from a point of historical view, one can see chemometrics and chemoinformatics have some differencesbetween them （see Fig. 1.4）. In one word, chemometrics was mainly from the developing requirementfrom analytical chemistry, while chemoinformatics was mainly developed in order to meet the demandfrom physical chemistry and drug industry. Of course, there is much overlapping between the two sub-disdplines. It is also possible that the two chemical sub-disdplines could maybe merge togethersomeday in the future, since their targets are all to find some way to solve the chemical problems withthe help of mathematics, statistics and computer sdence.
