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http://dx.doi.org/10.4333/KPS.2007.37.6.377

Algorithm for Finding the Best Principal Component Regression Models for Quantitative Analysis using NIR Spectra  

Cho, Jung-Hwan (College of Pharmacy, Sookmyung Women's University)
Publication Information
Journal of Pharmaceutical Investigation / v.37, no.6, 2007 , pp. 377-395 More about this Journal
Abstract
Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.
Keywords
All-possible combinations; Principal component regression (PCR); Near infrared (NIR); Correlation coefficients;
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  • Reference
1 R. Kramer, Chemometric Techniques for Quantitative Analysis, Marcel Dekker, Inc., 99p (1998)
2 E. R. Malinowski, Determination of the number of factors and the experimental error in a data matrix, Anal. Chem., 49, 612-617 (1977)   DOI
3 T. Koschkinsky and L. Heinemann. Sensors for glucose monitoring: technical and clinical aspects, Diabetes Metab. Res. Rev., 17, 113-123 (2001)   DOI   ScienceOn
4 A. Savitzky and M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem., 36, 1627-1639 (1964)   DOI
5 K. Faber, B. R. Kowalski, Critical evaluation of two F-tests for selecting the number of factors in abstract factor analysis, Anal. Chim. Acta, 337, 57-71 (1997)   DOI   ScienceOn
6 U. Depczynski, V. J. Frost and K. Molt, Genetic algorithm applied to the selection of factors in principal component regression, Anal. Chim. Acta, 420, 217-227 (2000)   DOI   ScienceOn
7 N. R. Draper and H. Smith, Applied Regression Analysis, 3rd Ed., John Wiley & Sons, Inc., 41p (1998)
8 E. R. Malinowski, Factor Analysis in Chemistry, 2nd Ed., John Wiley & Sons, Inc., (1991)
9 M. Blanco, J. Coello, H. Iturriaga, S. Maspoch, J. Pages, NIR calibration in non-linear systems: different PLS apporoaches and artificial neural networks, Chemom. Intell. Lab. Syst., 50, 75-82 (2000)   DOI   ScienceOn
10 K. Faber and B. R. Kowalski, Prediction error in least squares regression: Further critique on the deviation used in The Unscrambler, Chemom. Intell. Lab. Syst., 34, 283-292 (1996)   DOI   ScienceOn