Quantitative analysis of near infrared spectra by wavelet coefficient regression using a genetic algorithm

被引：52
作者：
Depczynski, U
Jetter, K
Molt, K
Niemöller, A
机构：
[1] Univ Duisburg Gesamthsch, Fachgebeit Instrumentelle Analyt FB6, D-47048 Duisburg, Germany
[2] Univ Hohenheim, Inst Angew Math & Stat, D-70593 Stuttgart, Germany
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1999年 / 47卷 / 02期
关键词：
wavelet transform; FFT; genetic algorithm; chemometrics; multivariate analysis; calibration;
D O I：
10.1016/S0169-7439(98)00208-1
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we present wavelet coefficient regression (WCR) in combination with a genetic algorithm (GA) as a method for multicomponent analysis by Near Infrared Spectrometry. The results are compared with other multivariate calibration methods like Fourier coefficient regression (FCR), principal component regression (PCR) and absorbance value regression at selected wavelengths (AVR). It is shown that in comparison to conventional methods, WCR is quite unique by the fact that it is self-adaptive. This means that the steps of pretreatment, selection of specific wavelength regions and calibration are performed automatically in one step. (C) 1999 Elsevier Science B.V. All rights reserved.
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收藏
页码：179 / 187
页数：9
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