Spectroscopy and Spectral Analysis, Volume. 29, Issue 11, 2959(2009)

Study on the Application of Supervised Principal Component Regression Procedure to Near-Infrared Spectroscopy Quantitative Analysis

[in Chinese]1,2, [in Chinese]1, [in Chinese]2, [in Chinese]3, and [in Chinese]
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  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    References(7)

    [2] [2] Burns Donald A, Ciurczak Emil W. Handbook of Near-Infrared Analysis. New York: Marcel Dekker Inc., 1992.

    [5] [5] Nguyen D V, Rocke D M. Bioinformatics, 2002, 18: 39.

    [6] [6] Wold H. Soft Modelling by Latent Variables: The Nonlinear Iterative Partial Least Squares (NIPALS) Approach, in Perspectives in Probability and Statistics, In Honor of Bartlett M S, 1975.

    [7] [7] Myers R H. Classical and Modern Regression with Application, Boston, Massachusetts: Duxbury, 1986.

    [8] [8] Mardia K, Kent J, Bibby J. Multivariate Analysis, Academic Press, 1979.

    [9] [9] Kerr M K, Martin M, Churchill G. A. Journal of Computational Biology, 2000, 7: 819.

    [10] [10] Bair E, Hastie T, Paul D, et al. J. Am. Statist Assoc., 2006, 101: 119.

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    [in Chinese], [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Study on the Application of Supervised Principal Component Regression Procedure to Near-Infrared Spectroscopy Quantitative Analysis[J]. Spectroscopy and Spectral Analysis, 2009, 29(11): 2959

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    Paper Information

    Received: Nov. 6, 2008

    Accepted: --

    Published Online: May. 26, 2010

    The Author Email:

    DOI:

    CSTR:32186.14.

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