Observations on the Use, in Prediction of Functionality in Cereals, of Weights Derived during Development of Partial Least Squares Regression

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Abstract
Partial least squares regression is a multivariate method commonly used for the development of near infrared calibrations for prediction of composition and functionality in wheat and other cereals. Modern software enables storage of the “weights” and “loadings” realised during equation development. These can be used in the interpretation of the factors affecting the development of the calibration equations. The weights indicate areas of wavelength where variance in the optical signal has been used in the development of the calibration equation. This paper gives examples of the use of weights in interpretation of calibrations for the prediction of composition and functionality factors.

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