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Acta Horticulturae
Pasternak, H., Inst. of Agricultural Engineering, A.R.O., Volcani Center, Bet Dagan 50250, Israel
Schmilovitch, Z., Inst. of Agricultural Engineering, A.R.O., Volcani Center, Bet Dagan 50250, Israel
Fallik, E., Inst. of Agricultural Engineering, A.R.O., Volcani Center, Bet Dagan 50250, Israel
Edan, Y., Dept. of Industrial Engineering and Managemnt, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
High intercorrelation between absorbances at different wavelengths is common in near infrared (NIR) analysis. NIR reflectance analysis was conducted to predict carotene in fresh tomatoes. When linear regression is employed the estimated parameters are practically random numbers, however high correlations are obtained between the predicted and true values (R=0.78). Ridge regression yields estimators with normal values, with lower parameter correlations (R=0.74). However, ridge regression is capable of overcoming noise versus linear regression which is not capable of predicting carotene in the presence of minor noise and multicollinearity.
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Ridge regression for NIR analysis with multicollinearity
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Pasternak, H., Inst. of Agricultural Engineering, A.R.O., Volcani Center, Bet Dagan 50250, Israel
Schmilovitch, Z., Inst. of Agricultural Engineering, A.R.O., Volcani Center, Bet Dagan 50250, Israel
Fallik, E., Inst. of Agricultural Engineering, A.R.O., Volcani Center, Bet Dagan 50250, Israel
Edan, Y., Dept. of Industrial Engineering and Managemnt, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
Ridge regression for NIR analysis with multicollinearity
High intercorrelation between absorbances at different wavelengths is common in near infrared (NIR) analysis. NIR reflectance analysis was conducted to predict carotene in fresh tomatoes. When linear regression is employed the estimated parameters are practically random numbers, however high correlations are obtained between the predicted and true values (R=0.78). Ridge regression yields estimators with normal values, with lower parameter correlations (R=0.74). However, ridge regression is capable of overcoming noise versus linear regression which is not capable of predicting carotene in the presence of minor noise and multicollinearity.
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