Application of Principal Component Analysis to Elucidate Experimental and Theoretical Information

Abstract

Principal Component Analysis has been widely used in different scientific areas and fordifferent purposes. The versatility and potentialities of this unsupervised method for dataanalysis, allowed the scientific community to explore its applications in different fields. Evenwhen the principles of PCA are the same in what algorithms and fundamentals concerns, thestrategies employed to elucidate information from a specific data set (experimental and/ortheoretical), mainly depend on the expertise and needs of each researcher.In this chapter, we will describe how PCA has been used in three different theoretical andexperimental applications, to explain the relevant information of the data sets. Theseapplications provide a broad overview about the versatility of PCA in data analysis andinterpretation. Our main goal is to give an outline about the capabilities and strengths ofPCA to elucidate specific information. The examples reported include the analysis ofmatured distilled beverages, the determination of heavy metals attached to bacterialsurfaces and interpretation of quantum chemical calculations. They were chosen asrepresentative examples of the application of three different approaches for data analysis:the influence of data pre-treatments in the scores and loadings values, the use of specificoptical, chemical and/or physical properties to qualitatively discriminate samples, and theuse of spatial orientations to group conformers correlating structures and relative energies.This reason fully justifies their selection as case studies. This chapter also pretends to be areference for those researchers that, not being in the field, may use these methodologies totake the maximum advantage from their experimental results.