Chemometrics uses advanced mathematical and statistical algorithms to provide maximum chemical information by analyzing chemical data, and obtain knowledge of chemical systems. Chemometrics significantly extends the possibilities of chromatography and with the technological advances of the personal computer and continuous development of open-source software, many laboratories are interested in incorporating chemometrics into their chromatographic methods. This book is an up-to-date reference that presents the most important information about each area of chemometrics used in chromatography, demonstrating its effective use when applied to a chromatographic separation.
Arvustused
"So why is this book a must purchase? It consists of seven sections, sub-divided into a number of chapters. Just the first three sections make this a book to purchase...this is a great book for all chromatographers. It will make you look and consider your experimental designs and the data you collect. Some of the methods described are not standard in your chromatographic data system. But with todays high-performance computers and the vast amount of open source software I am sure the average lab scientist can easily implement the well-described methods in the book."
- Chromatographia, August 2019
TABLE OF CONTENTS
Preface
Method development and optimization
Experimental design in chromatographic method development and validation
(ukasz Komsta, Yvan Vander Heyden)
Chromatographic response functions (Regina M. B. O. Duarte, João T. V. Matos,
Armando C. Duarte)
Chemometric strategies to characterize stationary phases (Charlene Galea,
Debby Mangelings, Yvan Vander Heyden)
Chromatographic applications of genetic algorithms and other nature-inspired
optimization methods (Mohammad Goodarzi, Yvan Vander Heyden)
Univariate analysis
Statistics and validation in quantitative chromatographic analysis
(Eliangiringa Kaale, Danstan Shewiyo, David Jenkins)
Statistical evaluation of calibration curves in chromatography (Sven
Declerck, Johan Viaene, Ines Salsinha, Yvan Vander Heyden)
Data preprocessing and unsupervised analysis
Introduction to multivariate data treatment (ukasz Komsta, Yvan Vander
Heyden)
Introduction to exploratory and clustering techniques (Ivana Stanimirova,
Micha Daszykowski)
Denoising of signals, signal enhancement and baseline correction in
chromatographic science (Zhi-Min Zhang, Hong-Mei Lu, Yi-Zeng Liang)
Alignment of one- and two-dimensional chromatographic signals (Micha
Daszykowski)
Peak purity and resolution of chromatographic data (Silvia Mas, Anna de Juan)
Modeling of peak shape and asymmetry (Jose Ramon Torres, Juan Jose
Baeza-Baeza, Maria Celia Garcia-Alvarez-Coque,)
Missing and censored data in chromatography (Ivana Stanimirova)
Classification, discrimination and calibration
Linear supervised techniques (ukasz Komsta, Yvan Vander Heyden)
Discriminant analysis, and classification of chromatographic data (Alessandra
Biancolillo, Federico Marini)
Nonlinear supervised techniques (Geert Postma, Lionel Blanchet, Frederik-Jan
van Schooten, Lutgarde Buydens)
Retention modelling
Introduction to quantitative structure-retention relationships (QSRRs)
(Krzesimir Ciura, Piotr Kawczak, Joanna Nowakowska, Tomasz Bczek)
Topological Indices in modelling chromatographic retention (Magorzata
Doowy, Katarzyna Bober, Alina Pyka-Pajk)
Application overviews
Introduction to fingerprinting in chromatography (Johan Viaene and Yvan
Vander Heyden)
Chemometric strategies in analysis of chromatographic-mass spectrometry data
(Samantha Riccadonna, Pietro Franceschi)
Chemometric strategies
?ukasz Komsta, Yvan Vander Heyden, Joseph Sherma
