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Evaluation of a Near-Infrared Reflectance Spectrometer as a Granulation Sensor for First-Break Ground Wheat: Studies with Six Wheat Classes¹

¹Contribution no. 01-170-J from the Kansas Agricultural Experiment Station. ²Former graduate student, Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506. USDA-ARS, Grain Marketing and Production Research Center, 1515 College Avenue, Manhattan, KS 66502. Names are necessary to report factually on available data; however, the USDA and Kansas State University (KSU) neither guarantee nor warrant the standard of the product, and the use of the name by the USDA and KSU implies no approval of the product to the exclusion of others that may also be suitable. ³Corresponding author. E-mail: choypc@gmprc.ksu.edu Phone: 785-776-2727. Fax: 785-776-2792. ⁴Department of Grain Science, Kansas State University, Manhattan, KS 66506. ⁵Formerly USDA-ARS, Grain Marketing and Production Research Center, Manhattan, KS 66502. ⁶Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506. ⁷Department of Statistics, Kansas State University, Manhattan, KS 66506.

In flour milling, a granulation sensor for ground wheat is needed for automatic control of a roller mill's roll gap. A near-infrared (NIR) reflectance spectrometer was evaluated as a potential granulation sensor of first-break ground wheat using offline methods. Sixty wheat samples, ground independently, representing six classes and five roller mill gaps, were each used for calibration and validation sets. Partial least squares regression was used to develop the models with cumulative mass of size fraction as the reference value. Combinations of four data pretreatments (log (1/R), baseline correction, unit area normalization, and derivatives) and three wavelength regions (700–1,500, 800–1,600, and 600–1,700 nm) were evaluated. Unit area normalization combined with baseline correction or second derivative yielded models that predicted well each size fraction of first-break ground wheat. Standard errors of performance of 4.07, 1.75, 1.03, and 1.40 and r² of 0.93, 0.90, 0.88, and 0.38 for the >1,041-, >375-, >240-, and >136-μm size ranges, respectively, were obtained for the best model. Results indicate that the granulation sensing technique based on NIR reflectance is ready for online evaluation.