Publication no. C-2001-0808-06R |  VIEW ARTICLE

Assessment of Heat-Damaged Wheat Kernels Using Near-Infrared Spectroscopy (1).

D. Wang (2,3), F. E. Dowell (4), and D. S. Chung (2). (1) Contribution No. 01-140-J from the Kansas Agricultural Experiment Station; (2) Biological & Agricultural Engineering Dept., Kansas State University, Manhattan, KS 66506; (3) Corresponding author. Phone: 785-532-2919. Fax: 785-532-5825. E-mail: <dwang@bae.ksu.edu>; (4) Grain Marketing and Production Research Center, USDA-ARS, Manhattan, KS 66502. Names are necessary to report factually on available data; however, the USDA neither guarantees nor warrants the standard of the product, and the use of the name by the USDA implies no approval of the product to the exclusion of others that may also be suitable. Cereal Chem. 78(5):625-628. Accepted May 25, 2001. Copyright 2001 American Association of Cereal Chemists, Inc.

Heat damage is a serious problem frequently associated with wet har­vests because of improper storage of damp grain or artificial drying of moist grain at high temperatures. Heat damage causes protein denatura­tion and reduces processing quality. The current visual method for asses­sing heat damage is subjective and based on color change. Denatured pro­tein related to heat damage does not always cause a color change in ker­nels. The objective of this research was to evaluate the use of near-infrared (NIR) reflectance spectroscopy to identify heat-damaged wheat kernels. A diode-array NIR spectrometer, which measured reflectance spectra (log (1/R)) from 400 to 1,700 nm, was used to differentiate single kernels of heat-damaged and undamaged wheats. Results showed that light scatter­ing was the major contributor to the spectral characteristics of heat-damaged kernels. For partial least squares (PLS) models, the NIR wavelength region of 750-1,700 nm provided the highest classification accuracy (100%) for both cross-validation of the calibration sample set and prediction of the test sample set. The visible wavelength region (400-750 nm) gave the low­est classification accuracy. For two-wavelength models, the average of correct classification for the classification sample set was >97%. The average of correct classification for the test sample set was generally >96% using two-wavelength models. Although the classification accuracies of two-wavelength models were lower than those of the PLS models, they may meet the requirements for industry and grain inspection applications.