The development of near infrared wheat quality models by locally weighted regressions

A large data base of near infrared and protein data was used to examine the utility of a data base regression technique know as LOCAL in the ISI Inter national software package. A total of 2203 samples of wheat from five class es with protein values by combustion analysis comprised the data base. One half of the samples came from hard red spring and hard red winter wheats, T he number of samples in the individual classes ranged from 235 for Durum to 694 for hard red spring. These samples were collected over a five year per iod and represented wheat in commercial trade. Calibrations mere determined for each class, the entire data base as a "global" calibration, a data bas e regression which selected appropriate samples for a unique calibration fo r each sample and the standard equations of MR spectroscopy regulatory anal ysis. Results will be reported which show data base regression technique to be as good as specific calibrations by partial least squares regression fo r sub-classes of products and precise enough for use for regulatory purpose s. The use of this data base regression technique over normal learning sets has other advantages, such as easier calibration update, easier transferab ility and the possibility to include authentication and classification as p art of the model.