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.
MartensH. and NæsT., “Mutivariate Calibration by Data Compression”, in Near Infrared Technology in the Agriculture and Food Industries, Ed by WilliamsP. and NorrisK.American Association of Cereal Chemists, St Paul, MN, pp. 57–87 (1987).
2.
HruschkaW.R., “Data Analysis: Wavelength Selection Methods”, in Near Infrared Technology in the Agriculture and Food Industries, Ed by WilliamsP. and NorrisK.American Association of Cereal Chemists, St Paul, MN, pp. 35–55 (1987).
3.
WilliamsP.C., “Variables Affecting Near Infrared Spectroscopic Analysis”, in Near Infrared Technology in the Agriculture and Food Industries, Ed by WilliamsP. and NorrisK.American Association of Cereal Chemists, St Paul, MN, pp. 143–167 (1987).
4.
LawD.P. and TkachukR., Cereal Chem.54, 874 (1977).
5.
WilliamsP.C.CordeiroH.M. and HarndenT., Cereal Foods World36, 571 (1991).
6.
MossH.J., Aust. J. Agric. Res.29, 1117 (1978).
7.
WilliamsP.C., Cereal Chem.44, 383 (1967).
8.
WilliamsP.C.KilbornR.H.VoiseyP.W. and KloekM., Cereal Chem.64, 422 (1987).
9.
Approved Method No. 55-30.Approved Methods of the Association, 9th Ed.The American Association of Cereal Chemists, St Paul, MN (1995).
10.
CutlerG.H. and BrinsonG.A., Cereal Chem.12, 120 (1935).
