INVESTIGADORES
TYMCZYSZYN Emma Elizabeth
capítulos de libros
Título:
Application of Principal Component Analysis to Elucidate Experimental and Theorical Information
Autor/es:
ARAUJO-ANDRADE C.; FRAUSTO-REYES C.; GERBINO, E.; MOBILI, P; TYMCZYSZYN E.; ESPARZA-IBARRA E.; IVANOV-TSONCHEV R.; GOMEZ-ZAVAGLIA A.
Libro:
Principal Component Analysis.
Editorial:
Intech
Referencias:
Lugar: Rijeka; Año: 2012; p. 23 - 49
Resumen:
Principal Component Analysis has been widely used in different scientific areas and for different purposes. The versatility and potentialities of this unsupervised method for data analysis, allowed the scientific community to explore its applications in different fields. Even when the principles of PCA are the same in what algorithms and fundamentals concerns, the strategies employed to elucidate information from a specific data set (experimental and/or theoretical), 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 and experimental applications, to explain the relevant information of the data sets. These applications provide a broad overview about the versatility of PCA in data analysis and interpretation. Our main goal is to give an outline about the capabilities and strengths of PCA to elucidate specific information. The examples reported include the analysis of matured distilled beverages, the determination of heavy metals attached to bacterial surfaces and interpretation of quantum chemical calculations. They were chosen as representative 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 specific optical, chemical and/or physical properties to qualitatively discriminate samples, and the use 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 a reference for those researchers that, not being in the field, may use these methodologies to take the maximum advantage from their experimental results.