Introduction to Multivariate Calibration: A Practical Approach

by Alejandro C. Olivieri

Overview

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.

Product Details

ISBN-13:	9783319970974
Publisher:	Springer-Verlag New York, LLC
Publication date:	08/20/2018
Sold by:	Barnes & Noble
Format:	eBook
File size:	51 MB
Note:	This product may take a few minutes to download.

About the Author

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.

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.

Editorial Reviews

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)

From the Publisher