Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/25/2001
Publication Date: 9/1/2001
Citation: Wang, D., Dowell, F.E., and Chung, D.S. Assessment of Heat-Damaged Wheat Kernels Using Near-Infrared Spectroscopy. Cereal Chemistry. 2001. 78(5): 625-628. Interpretive Summary: Heat damage in wheat is associated with improper storage and drying of wet grain. Heat damage causes protein denaturation and reduces processing quality. We determined NIR spectroscopy could accurately detect heat-damaged wheat, even if there was no visual discoloration. This technology provides an objective means of detecting heat damage in wheat, thus providing buyers with additional quality information.
Technical Abstract: 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 denaturalization and reduces processing quality. The current visual method for assessing heat damage is subjective and based on color change. Denatured protein related dto 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 lease 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 test 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.