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化学计量学基础 - 中国高校教材图书网
书名: 化学计量学基础
ISBN:978-7-5628-2871-6 条码:
作者: 梁逸曾 易伦朝编著  相关图书 装订:平装
印次:1-1 开本:16开
定价: ¥38.00  折扣价:¥34.20
折扣:0.90 节省了3.8元
字数: 340千字
出版社: 华东理工大学出版社 页数:
发行编号: 每包册数:
出版日期: 2010-10-01
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内容简介:
本书以双语形式介绍化学计量学的大部分内容,要强调化学计量学中的基本概念和化学计量学方法的基本思路,对一些方法的数学推导均以矩阵运算的形式给出。为了便于学生学习,在介绍化学计量学的同时,专门开辟章节介绍必要的有关矩阵运算的基础知识,及其重要数学概念的物理化学意义。让学生理解和熟悉矩阵运算的符号系统及其化学和物理意义。

作者简介:
 
章节目录:
目录





Chapter 1Introduction and Necessary Fundamental Knowledge of Mathematics

1.1Chemometrics: Definition and Its Brief History /

1.2The Relationship between Analytical Chemistry and Chemometrics /

1.3The Relationship between Chemometrics, Chemoinformatics and Bioinformatics /

1.4Necessary Knowledge of Mathematics /

1.4.1Vector and Its Calculation /

1.4.2Matrix and Its Calculation /



Chapter 2Chemical Experiment Design

2.1Introduction /

2.2Factorial Design and Its Rational Analysis /

2.2.1Computation of Effects Using Sign Tables /

2.2.2Normal Plot of Effects and Residuals /

2.3Fractional Factorial Design /

2.4Orthogonal Design and Orthogonal Array /

2.4.1Definition of Orthogonal Design Table /

2.4.2Orthogonal Arrays and Their Intereffect Tables /

2.4.3Linear Graphs of Orthogonal Array and Its Applications /

2.5Uniform Experimental Design and Uniform Design Table /

2.5.1Uniform Design Table and Its Construction /

2.5.2Uniformity Criterion and Accessory Tables for Uniform Design /

2.5.3Uniform Design for Pseudolevel /

2.5.4An Example for Optimization of Electropherotic Separation Using

Uniform Design /

2.6DOptimal Experiment Design /

2.7Optimization Based on Simplex and Experiment Design /

2.7.1Constructing an Initial Simplex to Start the Experiment Design /

2.7.2Simplex Searching and Optimization /



Chapter 3Processing of Analytic Signals

3.1Smoothing Methods of Analytical Signals /

3.1.1MovingWindow Average Smoothing Method /

3.1.2SavitskyGolay Filter /

3.2Derivative Methods of Analytical Signals /

3.2.1Simple Difference Method /

3.2.2MovingWindow Polynomial LeastSquares Fitting Method /

3.3Background Correction Method of Analytical Signals /

3.3.1Penalized Least Squares Algorithm /

3.3.2Adaptive Iteratively Reweighted Procedure /

3.3.3Some Examples for Correcting the Baseline from Different Instruments /

3.4Transformation Methods of Analytical Signals /

3.4.1Physical Meaning of the Convolution Algorithm /

3.4.2Multichannel Advantage in Spectroscopy and Hadamard Transformation /

3.4.3Fourier Transformation /

Appendix 1: A Matlab Program for Smoothing the Analytical Signals /

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



Chapter 4Multivariate Calibration and Multivariate Resolution

4.1Multivariate Calibration Methods for White Analytical Systems /

4.1.1Direct Calibration Methods /

4.1.2Indirect Calibration Methods /

4.2Multivariate Calibration Methods for Grey Analytical Systems /

4.2.1Vectoral Calibration Methods /

4.2.2Matrix Calibration Methods /

4.3Multivariate Resolution Methods for Black Analytical Systems /

4.3.1Selfmodeling Curve Resolution Method /

4.3.2Iterative Target Transformation Factor Analysis /

4.3.3Evolving Factor Analysis and Related Methods /

4.3.4Window Factor Analysis /

4.3.5Heuristic Evolving Latent Projections /

4.3.6Subwindow Factor Analysis /

4.4Multivariate Calibration Methods for Generalized Grey Analytical Systems /

4.4.1Principal Component Regression (PCR) /

4.4.2Partial Least Squares (PLS) /

4.4.3Leaveoneout Crossvalidation /



Chapter 5Pattern Recognition and Pattern Analysis for Chemical Analytical Data

5.1Introduction /

5.1.1Chemical Pattern Space /

5.1.2Distance in Pattern Space and Measures of Similarity /

5.1.3Feature Extraction Methods /

5.1.4Pretreatment Methods for Pattern Recognition /

5.2Supervised Pattern Recognition Methods: Discriminant Analysis Methods /

5.2.1Discrimination Method Based on Euclidean Distance /

5.2.2Discrimination Method Based on Mahalanobis Distance /

5.2.3Linear Learning Machine /

5.2.4kNearest Neighbors Discrimination Method /

5.3Unsupervised Pattern Recognition Methods: Clustering Analysis Methods /

5.3.1Minimum Spanning Tree Method /

5.3.2kmeans Clustering Method /

5.4Visual Dimensional Reduction Based on Latent Projections /

5.4.1Projection Discrimination Method Based on Principal

Component Analysis /

5.4.2SMICA Method Based on Principal Component Analysis /

5.4.3Classification Method Based on Partial Least Squares /
精彩片段:
Chapter 1Introduction and Necessary Fundamental



Knowledge of Mathematics



1.1Chemometrics: Definition and Its Brief History



The term chemometrics was first introduced by Svante Wold in the early 1970s when he applied a scientific project from Swedish government. Terms like biometrics and econometrics were also introduced into the fields of biological science and economics. Afterward, the International Chemometrics Society was established when Svante Wold and Bruce R. Kowalski met in 1974 [11]. Since then, chemometrics has been developing and is now widely applied to different fields of chemistry, especially analytical chemistry in view of the numbers of papers published, conferences and workshops being organized, and related activities.

“A reasonable definition of chemometrics remains as how do we get chemical relevant information out of measured chemical data, how do we represent and display this information, and how do we get such information into data?” as mentioned by Wold [11]. Both the academic and industrial sectors have benefited greatly in employing this new tool in different areas. As pointed out by Professor Yu Ruqin [12], a renowned analytical chemist and also a member of Chinese academy, “Chemometrics with the use of statistics and related mathematical techniques forms a new area in chemistry. According to D. L. Massart, its targets are to design or select optimal measurement procedures and experiments as well as to extract a maximum of information from chemical data. With these unique features and applications, some believe that chemometrics provides an important theoretical background for analytical chemistry”.

According to the International Chemometrics Society, chemometrics can be defined as “chemometrics is a new chemical discipline that uses the theory and methods from mathematics, statistics, computer science and other related disciplines to optimize the procedure of chemical measurement, and to extract chemical information as much as possible from chemical data.”

Howery and Hirsch [13] in the early 1980s classified the development of the chemometrics discipline into different stages. The first stage is before 1970. A number of mathematical methodologies were developed and standardized in different fields of mathematics, behavioral science, and engineering sciences. In this period, chemists limited themselves mainly to data analysis, including computation of statistical parameters such as the mean, standard deviation, and level of confidence. Howery and Hirsch, in particular, appreciated the research on correlating vast amounts of chemical data to relevant molecular properties. These pioneering works form the basis of an important area of the quantitative structureactivity relationship (QSAR) developed more recently.

The second stage of chemometrics falls in the 1970s, when the term chemometrics was coined. This new discipline of chemistry (or subdiscipline of analytical chemistry by some) caught the attention of chemists, especially analytical chemists, who not only applied the methods available for data analysis but also developed new methodologies to meet their needs. There are two main reasons why chemometrics developed so rapidly at that time: (1) large piles of data not available before could be acquired from advanced chemical instruments (for the first time, chemists faced bottlenecks similar to those encountered by social scientists or economists years before on how to obtain useful information from these large amounts of data) and (2) advancements in microelectronics technology within that period. The abilities of chemists in signal processing and data interpretation were enhanced with the increasing computer power.

The future evolution of chemometrics was also predicted by Howery and Hirsch in their article [13] and later by Brown [11]. Starting from the early 1980s, chemometrics was amalgamated into chemistry courses for graduates and postgraduates in American and European universities. In addition, it became a common tool to chemists. Since the early 1980s, development of the discipline of chemometrics verified the original predictions. Chemometrics has become a mainstay of chemistry in many universities of America and Europe and some in China and other countries. Workshops and courses related to chemometrics are held regularly at conferences such as the National Meetings of American Chemical Society (ACS) and the Gordon Conferences, as well as at symposia and meetings of the Royal Society of Chemistry and International Chemometrics Society. For instance, four courses were offered under the title “Statistics/Experimental Design /Chemometrics” in the 226th ACS National Meeting held in New York in September 2003 [http://www.acs.org]. The course titles are “Chemometric Techniques for Qualitative Analysis”,“Experimental Design for Combinatorial and HighThrougput Materials Development”,“Experimental Design for Productivity and Quality in R&D,” and “Statistical Analysis of Laboratory Data”. Furthermore, chemometrics training courses are held regularly by software companies like such as CAMO [14] and PRS [15]. In a review article [16] on the 25 most frequently cited books in analytical chemistry (1980—1999), four are related to chemometrics: Factor Analysis in Chemistry by Malinowski [17], Data Reduction and Error Analysis for the Physical Sciences by Bevington and Robinson [18], Applied Regression Analysis by Draper and Smith [19], and Multivariate Calibration by Martens and Naes [110] with rankings of 4, 5, 7 and 16, respectively. The textbook Chemometrics: Statistics and Computer Applications in Analytical Chemistry [111] by Otto was the second most popular “bestseller” on analytical chemistry according to the Internet source www.amazon.com on February 16, 2001. The Internet source www.chemistry.co.nz listed “Statistics for Analytical Chemistry” by J. Miller and J. Miller as one of the eight analytical chemistry bestsellers on January 21, 2002 and February 10, 2003. ”

More importantly, there were two international journals, named as “Journal of Chemometrics” and “Chemometrics and Intelligent Laboratory Systems” appeared in 1987 from both American and Europe. It is a mark that chemometrics has been growing as a mature chemical discipline in chemistry.

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