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.

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.