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Description
This book offers an introductory-level guide to the complex field of multivariate analytical calibration, with particular emphasis on real applications such as near infrared spectroscopy. It presents intuitive descriptions of mathematical and statistical concepts, illustrated with a wealth of figures and diagrams, and consistently highlights physicochemical interpretation rather than mathematical issues. In addition, it describes an easy-to-use and freely available graphical interface, together with a variety of appropriate examples and exercises. Lastly, it discusses recent advances in the field (figures of merit, detection limit, non-linear calibration, method comparison), together with modern literature references.
Table of Contents
Chapter1: Chemometrics and multivariate calibration.- Chapter2: The classical least-squares model.- Chapter3: The inverse least-squares model.- Chapter4: Principal component analysis.- Chapter5: Principal component regression.- Chapter6: The optimum number of latent variables.- Chapter7: The partial least-squares model.- Chapter8: Comparison of multivariate models.- Chapter9: Data pre-processing. Part 1: samples and sensors.- Chapter10: Data pre-processing. Part 2: mathematical filters.-Chapter11: Analytical figures of merit.- Chapter12: MVC1: a software for multivariate calibration.- Chapter13: Non-linearity and artificial neural networks.- Chapter14: Solutions to exercises.
