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Recent developments in multivariate calibration
Author(s) -
Kowalski B. R.,
Seasholtz M. B.
Publication year - 1991
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180050303
Subject(s) - univariate , calibration , multivariate statistics , principal component regression , partial least squares regression , principal component analysis , chemometrics , computer science , sample (material) , linear regression , linear model , multivariate analysis , bayesian multivariate linear regression , statistics , data mining , mathematics , artificial intelligence , machine learning , chemistry , chromatography
With the goal of understanding global chemical processes, environmental chemists have some of the most complex sample analysis problems. Multivariate calibration is a tool that can be applied successfully in many situations where traditional univariate analyses cannot. The purpose of this paper is to review multivariate calibration, with an emphasis being placed on the developments in recent years. The inverse and classical models are discussed briefly, with the main emphasis on the biased calibration methods. Principal component regression (PCR) and partial least squares (PLS) are discussed, along with methods for quantitative and qualitative validation of the calibration models. Non‐linear PCR, non‐linear PLS and locally weighted regression are presented as calibration methods for non‐linear data. Finally, calibration techniques using a matrix of data per sample (second‐order calibration) are discussed briefly.