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Comparison of Linear Statistical Methods for Calibration of Nir Instruments

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  • T. Naes
  • C. Irgens
  • H. Martens

Abstract

The multiple linear regression, ridge regression, principal component regression and partial least squares regression approach to statistical calibration of near infrared (NIR) instruments are compared. Computations on wheat data show that when the ratio between the number of calibration samples and the number of wavelengths in the NIR spectrum is low, the latter three methods, which are biased regression methods, give much better prediction results than multiple linear regression. This is very important in NIR analysis where this ratio is often small. In addition, we consider a new transformation of NlR data. It is shown that in company with the partial least squares method the transformation leads to very good results.

Suggested Citation

  • T. Naes & C. Irgens & H. Martens, 1986. "Comparison of Linear Statistical Methods for Calibration of Nir Instruments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(2), pages 195-206, June.
    • Handle: RePEc:bla:jorssc:v:35:y:1986:i:2:p:195-206
    DOI: 10.2307/2347270
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