Comprehensive Chemometrics: Chemical and Biochemical Data Analysis

Steven Brown , Roma Tauler , Beata Walczak 지음

elsevier 2020년 06월 01일 출간

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책소개

Comprehensive Chemometrics, Second Edition features expanded and updated coverage, along with new content that covers advances in the field since the previous edition published in 2009. Subject of note include updates in the fields of multidimensional and megavariate data analysis, omics data analysis, big chemical and biochemical data analysis, data fusion and sparse methods. The book follows a similar structure to the previous edition, using the same section titles to frame articles. Many chapters from the previous edition are updated, but there are also many new chapters on the latest developments. Presents integrated reviews of each chemical and biological method, examining their merits and limitations through practical examples and extensive visuals Bridges a gap in knowledge, covering developments in the field since the first edition published in 2009Meticulously organized, with articles split into 4 sections and 12 sub-sections on key topics to allow students, researchers and professionals to find relevant information quickly and easilyWritten by academics and practitioners from various fields and regions to ensure that the knowledge within is easily understood and applicable to a large audiencePresents integrated reviews of each chemical and biological method, examining their merits and limitations through practical examples and extensive visuals Bridges a gap in knowledge, covering developments in the field since the first edition published in 2009Meticulously organized, with articles split into 4 sections and 12 sub-sections on key topics to allow students, researchers and professionals to find relevant information quickly and easilyWritten by academics and practitioners from various fields and regions to ensure that the knowledge within is easily understood and applicable to a large audience

목차

Volume 1 (Statistics, Experimental Design, Optimization) a)?Statistics: This section covers the more fundamental aspects of statistics used in chemical and biochemical labs and industries, with chapters covering the sampling theory, quality and proficiency testing and control, and the introduction of statistical resampling, Bayesian methodologies and robust statistical methods.? Some of these last chapters of the section, for example Bayesian and robust approaches, should be updated and extended in the new edition of this section. b)?Experimental Design This section gives a good coverage of the field of experimental design including methods for initial screening, quantitative study of the effects of the factors, response surface methodologies, mixture designs and non-classical strategies. These chapters help the experimenter in solving most of the situations encountered in practice. Either in this section or in others, there is the need of including more explicitly a discussion of multivariate extension of ANOVA for the study of the effects in well-designed experiments, an area of special importance nowadays, for instance in omics type of studies c)?Optimization Many chemistry and chemometric data analysis problems can be formulated using numerical optimization strategies. Several chapters devoted to different optimization strategies are described in this section, including state of the art approaches for constrained and unconstrained optimization, sequential optimization, multi-criteria decision-making, and genetic algorithms. These methods could be complemented in the next edition of the work by including coverage of other methods such as particle swarm optimization and machine learning methods. Volume 2 (Data preprocessing, Linear modeling, Unsupervised data mining) d)?Data preprocessing Data preprocessing is a fundamental step in the chemometrics workflow which is increasingly recognized. In

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저자(글) Steven Brown

저자(글) Roma Tauler

저자(글) Beata Walczak