Introduction to Multivariate Calibration

Prof. Dr. Alejandro César Olivieri has obtained his B.Sc. in Industrial Chemistry from the Catholic Faculty of Chemistry and Engineering, Argentina, in 1982, and his Ph.D. from the Faculty of Biochemical and Pharmaceutical Sciences, University of Rosario, Argentina, in 1986. He currently works in the Department of Analytical Chemistry of the latter Faculty, and is a fellow of the National Research Council of Argentina (CONICET). He has published about 200 scientific papers in international journals, several books and book chapters and supervised ten Ph.D. theses. He was John Simon Guggenheim Memorial Foundation fellow (2001-2002). His primary research field is multivariate calibration, including first- and higher-order models, analytical figures of merit and software development.

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.

Selected by Choice magazine as an Outstanding Academic Title for 2019

"Rich, concrete examples are lavishly illustrated, and ample references are provided. The text overall is challenging and  rewarding for students and specialists alike." (A. E. Viste, emeritus, Choice, Vol. 56 (10), June, 2019)