Variable selection using genetic algorithm for analysis of near-infrared spectral data using partial least squares

被引：0
|
作者：
Soh, Chit Siang [1 ]
Ong, Kok Meng [1 ]
Raveendran, P. [1 ]
机构：
[1] Univ Malaya, Dept Elect Engn, Fac Engn, Kuala Lumpur 50603, Malaysia
来源：
2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 | 2005年
关键词：
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Genetic algorithm is used to perform variable selection to determine the ranges of wavelengths in NIR spectral data suitable to be used as predictors in multivariate calibration model via partial least squares. The NIR spectral data consists of three components of active substances, namely human serum albumin (HSA), gamma-globulin and glucose. The wavelength selection is able to improve the calibration model by selecting the wavelengths that contains information or correlated with the concentration of substances, while others non-chosen wavelengths, which contribute no information or contain noises, are excluded from the calibration model.
引用
收藏
页码：1178 / 1181
页数：4
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