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Bayesian regularization: application to calibration in NIR spectroscopy
Author(s) -
Alciaturi C. E.,
Quevedo G.
Publication year - 2009
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.1253
Subject(s) - calibration , regularization (linguistics) , chemometrics , bayesian probability , curse of dimensionality , bayesian inference , near infrared spectroscopy , spectroscopy , inference , dimensionality reduction , computer science , algorithm , generalization , artificial neural network , artificial intelligence , mathematics , machine learning , physics , statistics , optics , mathematical analysis , quantum mechanics
The use of a Bayesian regularization algorithm is proposed for calibration in near‐infrared spectroscopy (NIR) with linear models. The algorithm used in this work is based upon the concepts developed by MacKay for inference and model comparison in artificial neural networks. It is demonstrated that this algorithm is fast, easy to use, and shows good generalization properties without previous dimensionality reduction. Examples are shown for NIR spectroscopy calibration and synthetic data. Copyright © 2009 John Wiley & Sons, Ltd.