Advanced Chemometrics without Equations (or Hardly Any)

Course Description

Advanced Chemometrics without Equations (ACWE) takes up where our popular Chemometrics without Equations course leaves off. It is assumed that participants will have a working knowledge of Principal Components Analysis (PCA) and regression with Partial Least Squares (PLS). ACWE concentrates on improving chemometric models via 1) advanced preprocessing methods and 2) variable selection.

The critical difference between inadequate and successful chemometric models is often data preprocessing, i.e. what is done to the data before using PCA, PLS etc. The goal of preprocessing is to remove variation not related to the problem of interest so that the variation of interest is more evident and can be more easily modeled. The variables selected, e.g. spectral regions, can also greatly affect the success of the application. ACWE focuses on advanced preprocessing methods, including Extended Multiplicative Scatter Correction (EMSC) and Generalized Least Squares (GLS), for improving models. Variable selection techniques, such as interval PLS (iPLS) are also considered. The effect of preprocessing and variable selection on robustness of the final models is also considered.

Target Audience

Advanced Chemometrics Without Equations (or Hardly Any) is designed for those who wish to explore the problem solving power of chemometric tools, but are discouraged by the high level of mathematics found in many software manuals and texts. Course emphasis is on proper application and interpretation of chemometric methods as applied to real-life problems. The objective is to teach in the simplest way possible so that participants will be better chemometrics practitioners and managers.

Course Outline

1. Introduction
1.1 Brief review of PCA
1.2 Brief review of PLS regression

2 Advanced Preprocessing
2.1 what are the goals of preprocessing?
2.2 mean- and median-centering, autoscaling
2.3 normalization and standard normal variate
2.4 Savitsky-Golay and filtering
2.5 generalized least squares weighting (GLS)
2.6 multiplicative scatter correction (MSC)
2.7 extended multiplicative scatter correction (EMSC)

3. Variable selection
3.1 why do variable selection?
3.2 knowledge based selection
3.3 model based, e.g. on loadings
3.4 interval PLS (iPLS)

4. Summary