Ridge regression for NIR analysis with multicollinearity

H. Pasternak, Z. Schmilovitch, E. Fallik, Y. Edan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationIII International Symposium on Sensors in Horticulture
PublisherInternational Society for Horticultural Science
Pages265-268
Number of pages4
ISBN (Print)9789066059542
DOIs
StatePublished - 1 Jan 2001

Publication series

NameActa Horticulturae
Volume562
ISSN (Print)0567-7572

Keywords

  • Multicollinearity
  • Near infrared spectroscopy
  • Ridge regression
  • Tomato

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