Bayesian regularization: application to calibration in NIR spectroscopy

被引：5
|
作者：
Alciaturi, C. E. [1 ,2 ]
Quevedo, G. [2 ]
机构：
[1] INZIT, Maracaibo, Venezuela
[2] Univ Zulia, LUZ, Maracaibo 4011, Venezuela
来源：
JOURNAL OF CHEMOMETRICS | 2009年 / 23卷 / 11-12期
关键词：
Bayesian regularization; linear models; calibration; near-infrared spectroscopy; PARTIAL LEAST-SQUARES; PLS; REGRESSION; TUTORIAL; NETWORKS;
D O I：
10.1002/cem.1253
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 (C) 2009 John Wiley & Sons, Ltd.
引用
收藏
页码：562 / 568
页数：7
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