An optical radiation measurement system, which measured reflectance spectra, log (1/R), from 400 to 2,000 nm, was used to quantify single wheat kernel color. Six classes of wheat samples were used for this study, including red wheat that appears white and white wheat that appears red. Partial least squares regression and multiple linear regression were used to develop classification models with three wavelength regions, 500–750, 500–1,700, and 750–1,900 nm, and three data pretreatments, log (1/R), first derivative, and second derivative. For partial least squares models, the highest classification accuracy was 98.5% with the wavelength region of 500–1,700 nm. The log (1/R) and the first derivative yielded higher classification accuracy than the second derivative. For multiple linear regression models, the highest classification accuracy was 98.1% obtained from log (1/R) spectra from the visible and near-infrared wavelength regions.
This article is in the public domain and not copyrightable. It may be freely reprinted with customary crediting of the source. American Association of Cereal Chemists, Inc., 1999.