Basic Chemometrics

Price: Conferee: $450; Student: $50; Non-Conferee: $550
Date: Sunday 10/21/2018 - 9am to 4pm

Barry Wise, Eigenvector

This course concentrates on what are perhaps the two most important chemometric methods, Principal Components Analysis (PCA) and Partial Least Square (PLS) regression. PCA can be used for exploratory data analysis, pattern recognition and data prescreening/cleaning. PCA is part of many other methods and is also used for preprocessing data in a wide variety of applications (e.g. SVMs and ANNs).  This course covers the basics of PCA, concentrating on interpretation of PCA models. The course continues with the motivation regression models. Multiple Linear Regression (MLR) is introduced along with Principal Components Regression (PCR). Problems with these methods are discussed, and it is shown how Partial Least Squares (PLS) mitigates these issues. Examples of using PLS in spectroscopic calibrations is demonstrated. The course includes hands-on computer time for participants to work example problems using PLS_Toolbox.