Title :
Variable Selection Using Genetic Algorithm for Analysis of Near-Infrared Spectral Data Using Partial Least Squares
Author :
Siang Soh, Chit ; Meng Ong, Kok ; Raveendran, P.
Author_Institution :
Dept. of Electr. Eng., Malaya Univ., Kuala Lumpur
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), 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
Keywords :
bio-optics; biochemistry; biomedical measurement; blood; calibration; genetic algorithms; infrared spectra; least squares approximations; medical computing; molecular biophysics; proteins; gamma-globulin; genetic algorithm; glucose; human serum albumin; multivariate calibration model; near-infrared spectral data analysis; partial least squares; variable selection; wavelength selection; Algorithm design and analysis; Blood; Calibration; Data engineering; Genetic algorithms; Input variables; Least squares methods; Predictive models; Spectral analysis; Sugar;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1616633
