PARTIAL LEAST-SQUARES REGRESSION AS A MULTIVARIATE TOOL FOR THE INTERPRETATION OF INFRARED-SPECTRA

被引：46

作者：

LUINGE, HJ ^{[1
]}

VANDERMAAS, JH ^{[1
]}

VISSER, T ^{[1
]}

机构：

[1] NATL INST PUBL HLTH & ENVIRONM PROTECT,ORGAN ANALYT CHEM LAB,3720 BA BILTHOVEN,NETHERLANDS

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1995年 / 28卷 / 01期

关键词：

D O I：

10.1016/0169-7439(94)00078-W

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 potentials of partial least squares regression (PLS) as a tool for the automated interpretation of infrared spectra have been investigated. Class probabilities are obtained by applying a sigmoid function to the PLS predictions. Results are compared with those obtained from artificial neural networks (ANN) applied to full spectra and to the most relevant principal component scores. The predictions obtained from all approaches appear to be comparable, but the time required for training is considerably shorter for the extended PLS approach.

引用

页码：129 / 138

页数：10

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