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Detecting and adjusting for non‐linearities in calibration of near‐infrared data using principal components
Author(s) -
Oman Samuel D.,
Naes Tormod,
Zube Anan
Publication year - 1993
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.1180070306
Subject(s) - principal component analysis , principal component regression , linear regression , calibration , partial least squares regression , simple linear regression , mathematics , regression , least squares function approximation , linear model , regression analysis , statistics , computer science , estimator
A new regression method for non‐linear near‐infrared spectroscopic data is proposed. The technique is based on a model which is linear in the principal components and simple functions (squares and products) of them. Added variable plots are used to determine which squares and products to incorporate into the model. The regression coefficients are estimated by a Stein estimate which shrinks towards the estimate determined by the first several principal components and the selected non‐linear terms. The technique is not computationally intensive and is appropriate for routine predictions of chemical concentrations. The method is tested on three data sets and in all cases gives more accurate predictions than does linear principal components regression.