Volume 29, Issue 1 (January 2001)

    Overcoming Multicollinearity in Near Infrared Analysis for Lycopene Content Estimation in Tomatoes by Using Ridge Regression

    (Received 28 June 1999; accepted 26 September 2000)

    CODEN: JTEOAD

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    Abstract

    High intercorrelation between absorbance at different wavelengths is common in near infrared analysis and was observed in an experiment to determine lycopene in tomatoes. Simulation analysis and experiments were conducted to estimate the effects of this problem on the estimators and on the predictive ability of linear regression and ridge regression. Applying linear regression to the experimental data resulted in very large parameter values, implying poor predictive ability. When linear regression gives very large parameter values, the estimated parameters are practically random numbers and are not correlated to the true ones. Ridge regression yielded estimators with normal values, but which are still poorly correlated with the true parameters. However, the predictive ability of the derived equation is good and may be used in practice to determine lycopene content in tomatoes since it is relatively easy to update.


    Author Information:

    Pasternak, H
    Institute of Agricultural Engineering, Volcani Center,

    Schmilovitch, Z
    Institute of Agricultural Engineering, Volcani Center,

    Fallik, E
    Institute of Postharvest Technology, Volcani Center,

    Edan, Y
    Institute of Agricultural Engineering, Volcani Center,

    Ben-Gurion University of the Negev, Beer Sheva,


    Stock #: JTE12392J

    ISSN: 0090-3973

    DOI: 10.1520/JTE12392J

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    Author
    Title Overcoming Multicollinearity in Near Infrared Analysis for Lycopene Content Estimation in Tomatoes by Using Ridge Regression
    Symposium , 0000-00-00
    Committee E13