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Published on: August 19, 2021
Variable selection using genetic algorithm for analysis of near-infrared spectral data using partial least squares
Chit Siang Soh1, Kok Meng Ong, P Raveendran
1Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, MALAYSIA. (phone: 603-79675226; fax: 603-79675316;
Abstract:
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), γ-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.
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