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

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自然科学 化学 化学原理和方法

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