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Evaluation of a Near-Infrared Reflectance Spectrometer as a Granulation Sensor for First-Break Ground Wheat: Studies with Hard Red Winter Wheats¹

¹Contribution no. 01-169-J from the Kansas Agricultural Experiment Station. ²Corresponding author. Email: choypc@gmprc.ksu.edu Phone: 785-776-2727. Fax: 785-776-2792. ³USDA-ARS, Grain Marketing and Production Research Center, Manhattan, KS 66502. Former graduate student, Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506. 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. ⁴Formerly USDA-ARS, Grain Marketing and Production Research Center, Manhattan, KS 66502. ⁵Department of Grain Science, Kansas State University, Manhattan, KS 66506. ⁶Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506. ⁷Department of Statistics, Kansas State University, Manhattan, KS 66506.

A single wheat class or blended wheats from two wheat classes are usually milled in a flour mill. A near-infrared (NIR) reflectance spectrometer, previously evaluated as granulation sensor for first-break ground wheat from six wheat classes, was evaluated for a single wheat class, hard red winter (HRW) wheat, using offline methods. The HRW wheats represented seven cultivars ground by an experimental roller mill at five roll gap settings (0.38, 0.51, 0.63, 0.75, and 0.88 mm) which yielded 35 ground wheat samples each for the calibration and validation sets. Granulation models based on partial least squares regression were developed with cumulative mass of size fractions as a reference value. Combinations of four data pretreatments (log 1/R, baseline correction, unit area normalization, and derivatives) and subregions of the 400–1,700 nm wavelength range were evaluated. Models that used pathlength correction (unit area normalization) predicted well each of the four size fractions of first-break ground wheat. The best model, unit area normalization and first derivative, predicted all the validation spectra with standard errors of performance of 3.80, 1.29, 0.43, and 0.68 for the >1041, >375, >240, and >136 μm size fractions, respectively. Ground HRW wheats have narrower particle size distribution and better sieving properties than ground wheat from six wheat classes. Thus, HRW wheat granulation models performed better than the previously reported models for six wheat classes.