Chemometrics in Analytical Spectroscopy provides students and practising analysts with a tutorial guide to the use and application of the more commonly encountered techniques used in processing and interpreting analytical spectroscopic data. In detail the book covers the basic elements of univariate and multivariate data analysis, the acquisition of digital data and signal enhancement by filtering and smoothing, feature selection and extraction, pattern recognition, exploratory data analysis by clustering, and common algorithms in use for multivariate calibration techniques. An appendix is included which serves as an introduction or refresher in matrix algebra. The extensive use of worked examples throughout gives Chemometrics in Analytical Spectroscopy special relevance in teaching and introducing chemometrics to undergraduates and post-graduates undertaking analytical science courses. It assumes only a very moderate level of mathematics, making the material far more accessible than other publications on chemometrics. The book is also ideal for analysts with little specialist background in statistics or mathematical methods, who wish to appreciate the wealth of material published in chemometrics.
Table of Content
Descriptive Statistics: The Acquisition and Enhancement of Data: Feature Selection and Extraction: Pattern Recognition I – Unsupervised Analysis: Pattern Recognition II – Supervised Learning: Calibration and Regression Analysis: Matrix Tools and Operations: Subject Index.
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