title: Multivariate data analysis in pharmaceutics: a tutorial review.
abstract: We provide an overview of latent variable methods used in pharmaceutics and integrated with advanced characterization techniques such as vibrational spectroscopy. The basics of the most common latent variable methods, principal component analysis (PCA), principal component regression (PCR) and partial least-squares (PLS) regression, are presented. Multiple linear regression (MLR) and methods for improved interpretation, variable selection, classification and validation are also briefly discussed. Extensive use of the methods is demonstrated by compilation of the recent literature.
authors.affiliation: Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway. Tarja.Rajalahti@kj.uib.no
authors.forename: Tarja
authors.initials: T
authors.lastname: Rajalahti
authors.forename: Olav M
authors.initials: OM
authors.lastname: Kvalheim
mesh_terms: D002626:Chemistry, Pharmaceutical / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D015233:Models, Statistical; D015999:Multivariate Analysis; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism; D025341:Principal Component Analysis / Q000379:methods
chemical_list: D004364:Pharmaceutical Preparations