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KMID : 0606320040210010042
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Journal of Phamacetical Sciences Sookmyung Women s University
2004 Volume.21 No. 1 p.42 ~ p.48
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Algorithm for finding the best multiple linear regression models using near infrared spectra
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Cho Jung-Hwan
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Abstract
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Near infrared(NIR) spectral data have been used far the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. Even though 1st or 2nd derivative data are used, it is very difficult to select the proper wavelengths of spectral data, which give the best multiple linear regression(MLR) models for the analysis of constituents of biological samples. To find the best MLR models, all-possible combinations of available variables(in this case, wavelengths of spectral data) were derived by in-house programs written in MATLAB codes. All of the extensively generated regression models were compared in terms of standard error of calibration(SEC),R2 and standard error of prediction(SEP) to find the best regression models. For the teat of the developed program, aqueous solutions of BSA(bovine seam albumin) were prepared and analyzed. As a result, the best MLR models can be found using 1st and 2nd derivative spectra and SEP criteria.
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