I am looking for an overview of multivariate statistics for chemometrics. I'm reviewing a number of articles that for the most part are analyzing a large number (300+) of various spectra (IR, UV-Vis, Raman, etc.) with varying levels of multivariate data (some have hundreds of data points, some thousands per spectra). These papers are using techniques like:
- PCA—principal components analysis, for data exploration and reduction;
- HCA—hierarchical cluster analysis;
- PLS-DA—partial least squares discriminant analysis;
- SVM—support vector machines,
- SIMCA—soft independent modeling by class analogy;
- LDA / QDA—linear / quadratic discriminant analysis (and various algorithms too, such as LDA genetic algorithm vs LDA successive projections algorithm),
- or some combination of them.
The goal of the majority of the articles is to distinguish the samples from which the spectra came from, and to predict (classify) an unknown sample based on its spectra. In general the samples are spectra from ink samples to distinguish them from one another (as in a forensic application). So in most cases some amount of ink samples are examined, a model is made (using any number of techniques), and sometimes blind ink samples are tested against the model. It just seems like every author does something very different from the others with no real clear indication of why.
Are there any resources that specifically comment on this sort of decision making and how to go about selecting one or more multivariate techniques that are not aimed at statisticians?