Chemometrics I -- PCA

Course Description

Chemometrics I -- PCA, concentrates on what is perhaps the most important chemometric method, Principal Components Analysis. PCA can be used for exploratory data analysis, pattern recognition, data prescreening, and is part of many other methods such as SIMCA sample classification. It is also used for preprocessing data in a wide variety of applications. 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.

Prerequisites

Linear Algebra for Chemometricians and MATLAB for Chemometricians or equivalent experience.

Chemometrics I -- PCA Course Outline

Nomenclature and conventions
Data transformation-Linearization
Data centering and scaling
The PCA decomposition
Examples: Wine and Arch data sets
Interpreting scores and loadings plots
Q and T2 statistics
Outliers
Determination of number of factors to keep
Additional examples and homework

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