Exploratory multivariate data analysis chemometrics

We have previously underlined that the absence of a common linear intensity axis (spectra not properly normalized) would lead to erroneous predictions in quantitative spectral analyses and in meaningless interpretations for exploratory data analyses. Another prerequisite for multivariate data analysis is that the data conform to the selected model. An assumption that applies to most of the multivariate methods is that the data are low rank bilinear. For most multivariate methods, this implies that the spectral axis must remain constant, that is, the signal(s) for a given chemical compound must appear at the same position in all the spectra. We will see how a different interval-based approach, not aimed at building chemometric models, but rather at spectral data preprocessing can effectively contribute to achieve an efficient and comprehensive horizontal signal alignment. 

The tasks that require multivariate statistics can be divided into descriptive, predictive, and classification. The term "exploratory data analysis" (EDA) is sometimes used to describe such multivariate applications. The discipline within chemistry that focuses on the analysis of chemical data, EDA, and modeling is called chemometrics. 

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