Chemometrics I: Principal Components Analysis (PCA)

Chemometrics I — PCA concentrates on what is perhaps the most important chemometric method, Principal Components Analysis. PCA is important on its own for exploratory data analysis, pattern recognition and data prescreening. It is also part of many other modern Machine Learning methods (e.g. SVMs and ANNs) where it is used for preprocessing and data compression. A good understanding of PCA also provides a good basis for understanding many of the factor based chemometric methods (PCR, PLS, MCR, etc.). This course covers the basics of PCA in depth, concentrating on interpretation of PCA models. The course includes hands-on computer time for participants to work example problems using PLS_Toolbox and Solo.