Open AccessMultiblock methods in Analytical Chemistry
Author(s) -
Douglas N. Rutledge
Publication year - 2021
Publication title -
brjac brazilian journal of analytical chemistry
Language(s) - English
Resource type - Journals
eISSN - 2179-3433
pISSN - 2179-3425
DOI - 10.30744/brjac.2179-3425.letter-dnrutledge
Subject(s) - chemometrics , multivariate statistics , data matrix , exploratory data analysis , linear discriminant analysis , principal component analysis , matrix (chemical analysis) , multivariate analysis , computer science , data mining , statistics , mathematics , chemistry , artificial intelligence , machine learning , chromatography , clade , biochemistry , gene , phylogenetic tree
Chemometrics, and multivariate data analysis in particular, has become a significant component of Analytical Chemistry. This is because of the need to have mathematical methods capable of extracting the pertinent information from the ever-increasing amounts of data generated by modern instruments. Usually, these multivariate data analysis methods are concerned with the exploratory analysis of a single data matrix, as in PCA, or with relating one explanatory matrix to another descriptive matrix, as in regression methods such as PCR and PLS, or discriminant methods, such as FDA and PLS-DA.