Publication no. C-2002-0405-02R |  VIEW ARTICLE

Determining Vitreous Subclasses of Hard Red Spring Wheat Using Visible/Near-Infrared Spectroscopy (1).

D. Wang (2,3), F. E. Dowell (4), and R. Dempster (5). (1) Contribution No. 01-269-J from the Kansas Agricultural Experiment Station. (2) Corresponding author. Phone: 785-532-2919. Fax: 785-532-5580. E-mail: <dwang@bae.kus.edu> (3) Biological & Agricultural Engineering Dept., Kansas State University, Manhattan, KS 66506. (4) Grain Marketing & Production Research Center, USDA-ARS, Manhattan, KS 66502. (5) American Institute of Baking, Manhattan, KS 66502. Cereal Chem. 79(3):418-422. Accepted January 8, 2002. Copyright 2002 American Association of Cereal Chemists, Inc.

The percentage of dark hard vitreous (DHV) kernels in hard red spring wheat is an important grading factor that is associated with protein content, kernel hardness, milling properties, and baking quality. The current visual method of determining DHV and non-DHV (NDHV) wheat kernels is time-consuming, tedious, and subject to large errors. The objective of this research was to classify DHV and NDHV wheat kernels, including kernels that were checked, cracked, sprouted, or bleached using visible/ near-infrared (Vis/NIR) spectroscopy. Spectra from single DHV and NDHV kernels were collected using a diode-array NIR spectrometer. The dorsal and crease sides of the kernels were viewed. Three wavelength regions, 500-750 nm, 750-1,700 nm, and 500-1700 nm were compared. Spectra were analyzed by using partial least squares (PLS) regression. Results suggest that the major contributors to classifying DHV and NDHV kernels are light scattering, protein content, kernel hardness, starch content, and kernel color effects on the absorption spectrum. Bleached kernels were the most difficult to classify because of high lightness values. The sample set with bleached kernels yielded lower classification accuracies of 91.1-97.1% compared with 97.5-100% for the sample set without bleached kernels. More than 75% of misclassified kernels were bleached. For sample sets without bleached kernels, the classification models that included the dorsal side gave the highest classification accuracies (99.6-100%) for the testing sample set. Wavelengths in both the Vis/NIR regions or the NIR region alone yielded better classification accuracies than those in the visible region only.