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Quantitative analysis by derivative spectrophotometry (III)—Simultaneous quantitation of vitamin B group and vitamin C in by multiple linear regression analysis—

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Abstract

The feature of resolution enhancement by derivative operation is linked to one of the multivariate analysis, which is multiple linear regression with two options, allpossible and stepwise regression. Examined samples were synthetic mixtures of 5 vitamins, thiamine mononitrate, riboflavin phosphate, nicotinamide, pyridoxine hydrochloride and ascorbic acid. All components in mixture were quantified with reasonably good accuracy and precision. Whole data processing procedure was accomplished on-line by the development of three computer programs written in APPLESOFT BASIC language.

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Literature Cited

  1. Park, M.K., Cho, Y.H., and Cho, J.H.: Quantitative analysis by derivative spectrophotometry (I): Simultaneous quantitation of pyridoxine HCl and nicotinamide in the mixture. Yakhak Hoeji,30, 185 (1986).

    CAS  Google Scholar 

  2. Park, M.K., and Cho, J.H.: Quantitative Analysis by Derivative Spectrophotometry (II): Derivative Spectrophotometry and Methods for the Reduction of High Frequency Noises.Arch. Pharm. Res.,10, 1 (1987).

    Article  CAS  Google Scholar 

  3. Park, M.K., Cho, J.H., and Park, Y.H.: Qualitative Analysis by Derivative UV Spectrophotometry (I): Identification of Penicillins and Cephalosporins. SNUJ. Pharm. Sci., in press (1987).

  4. Sternberg, J.C., Stillo, H.S., and Schwendeman, R.H.: Spectrophotometric Analysis of Multicomponent Systems Using the Least Squares Method in Matrix Form: The Ergosterol Irradiation System.Anal. Chem.,32, 84 (1960).

    Article  CAS  Google Scholar 

  5. Barnett, H.A., and Bartoli, A.: Least-Squares Treatment of Spectrometric Data.Anal. Chem.,32, 1153 (1960).

    Article  CAS  Google Scholar 

  6. Brown, C.W., Lynch, P.F., Obremski, R.J., and Lavery, D.S.: Matrix Representations and Criteria for Selecting Analytical Wavelengths for Multicomponent Spectroscopic Analysis.Anal. Chem.,54, 1472 (1982).

    Article  CAS  Google Scholar 

  7. Kisner, H.J., Brown, C.W., and Kavarnos, G.J.: Simultaneous Determination of Triglycerides, Phospholipids, and Cholesteryl Esters by Infrared Spectrometry.Anal. Chem.,54, 1479 (1982).

    Article  PubMed  CAS  Google Scholar 

  8. Kisner, H.J., Brown, C.W., and Kavarnos, G.J.: Multiple Analytical Frequencies and Standards for the Least-Squares Spectrometric Analysis of Serum Lipids.Anal. Chem.,55, 1703 (1983).

    Article  PubMed  CAS  Google Scholar 

  9. Haaland, D.M., Easterling, R.G., and Vopicka, D.A.: Multivariate Least Squares Methods Applied to the Quantitative Spectral Analysis of Multicomponent Samples.Appl. Spectro.,39, 73 (1985).

    Article  CAS  Google Scholar 

  10. Birth, G.S.: Evaluation of Correlation Coefficients Obtained with a Stepwise Regression Analysis.Appl. Spectro.,39, 729 (1985).

    Article  CAS  Google Scholar 

  11. Burkhard, L.P., and Weininger, D.: Determination of Polychlorinated Biphenyls Using Multiple Regression with Outlier Detection and Elimination.Anal. Chem.,59, 1187 (1987).

    Article  PubMed  CAS  Google Scholar 

  12. Beebe, K.R., and Kowalski, B.R.: An Introduction to Multivariate Calibration and Analysis.Anal. Chem.,59, 1007A (1987).

    Article  CAS  Google Scholar 

  13. Blackburn, J.A.: Computer Program for Multicomponent Spectrum Analysis Using Least-Squares Method.Anal. Chem.,37, 1000 (1965).

    Article  CAS  Google Scholar 

  14. Macnaughtan, D., Jr., Rogers, L.B., and Wernimont, G.: Principal-Component Analysis Applied to Chromatographic Data.Anal. Chem.,44, 1421 (1972).

    Article  CAS  Google Scholar 

  15. Ohta, N.: Estimating Absorption Bands of Component Dyes by Means of Principal-Component Analysis.Anal. Chem.,45, 553 (1973).

    Article  CAS  Google Scholar 

  16. Warner, I.M., Christian, G.D., Davidson, E.R., and Callis, J.B.: Analysis of Multicomponent Fluorescence Data.Anal. Chem.,49, 564 (1977).

    Article  CAS  Google Scholar 

  17. Painter, P.C., Rimmer, S.M., Snyder, R.W., and Davis, A.: A Fourier Transform Infrared Study of Mineral Matter in Coal: The Application of a Least Squares Curve-Fitting Program.Appl. Spectro.,35, 102 (1981).

    Article  CAS  Google Scholar 

  18. Sharaf, M.A., and Kowalski, B.R.: Quantitative Resolution of Fused Chromatographic Peaks in Gas Chromatography/Mass Spectrometry.Anal. Chem.,54, 1291 (1982).

    Article  CAS  Google Scholar 

  19. Sasaki, K., Kawata, S., and Minami, S.: Constrained Nonlinear Method for Estimating Component Spectra from Multicomponent Mixtures.Appl. Opt.,22, 3599 (1983).

    Article  PubMed  CAS  Google Scholar 

  20. Hemel, J.B., van der Voet, H., Hindriks, F.R., and van der Slik, W.: Stepwise Deletion: A Technique for Missing-Data Handling in Multivariate Analysis.Anal. Chim. Acta,193, 255 (1987).

    Article  CAS  Google Scholar 

  21. Rutan, S.C., and Motley, C.B.: Factor Analysis and Kalman Filter Studies of Severely Overlapped Amino Acid Derivatives in Thin-Layer Chromatography.Anal. Chem.,59, 2045 (1987).

    Article  PubMed  CAS  Google Scholar 

  22. Robert, P., Bertrand, D., Devaux, M.F., and Grappin, R.: Multivariate Analysis Applied to Near-Infrared Spectra of Milk.Anal. Chem.,59, 2187 (1987).

    Article  PubMed  CAS  Google Scholar 

  23. Friedrich, H.R., and Yu, J.P.: Combinations of Orthogonal Spectra to Estimate Component Spectra in Multicomponent Mixture.Appl. Spectro.,41, 227 (1987).

    Article  CAS  Google Scholar 

  24. Lindberg, W., Persson, J.A., and Wold, S.: partial Least-Squares Method for Spectrofluorimetric Analysis of Mixtures of Humic Acid and Ligninsulfonate.Anal. Chem.,55, 643 (1983).

    Article  CAS  Google Scholar 

  25. Frank, I.E., Kalivas, J.H., and Kowalski, B.R.: Partial Least Squares Solutions for Multicomponent Analysis.Anal. Chem.,55, 643 (1983).

    Article  Google Scholar 

  26. Ho, C.N., Christian, G.D., and Davidson, E.R.: Application of the Method of Rank Annihilation to Fluorescent Multicomponent Mixtures of Polynuclear Aromatic Hydrocarbons.Anal. Chem.,52, 1071 (1980).

    Article  CAS  Google Scholar 

  27. Gampp, H., Maeder, M., Meyer, C.J., and Zuberbuehler, A.D.: Quantification of a known Component in an Unknown Mixture.Anal. Chim. Acta,193, 287 (1987).

    Article  CAS  Google Scholar 

  28. Phillips, G.R., and Eyring, E.M.: Comparison of Conventional and Robust Regression in Analysis of Chemical Data.Anal. Chem.,55, 1134 (1983).

    Article  CAS  Google Scholar 

  29. Maris, M.A., Brown, C.W., and Lavery, D.S.: Nonlinear Multicomponent Analysis by Infrared Spectrophotometry.Anal. Chem.,55, 1694 (1983).

    Article  CAS  Google Scholar 

  30. De Levie, R.: When, Why, and How to Use Weighted Least Squares.J. Chem. Educ.,63(1), 10 (1986).

    Article  Google Scholar 

  31. Wolfe, P.M., and Koeling, C.P.: “BASIC Engineering and Scientific Programs.” A Prentice-Hall publishing and Communications Company, 71 (1983).

  32. Draper, N.R., and Smith, H.: “Applied Regression Analysis”. 2nd ed. John Wiley&Sons, Inc., New York (1981).

    Google Scholar 

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Park, M., Cho, J. Quantitative analysis by derivative spectrophotometry (III)—Simultaneous quantitation of vitamin B group and vitamin C in by multiple linear regression analysis—. Arch. Pharm. Res. 11, 45–51 (1988). https://doi.org/10.1007/BF02884767

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  • DOI: https://doi.org/10.1007/BF02884767

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