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Title: EVALUATION OF A NEAR-INFRARED REFLECTANCE SPECTROMETER AS A GRANULAITON SENSOR FOR FIRST-BREAK GROUND WHEAT: STUDIES WITH SIX WHEAT CLASSES.

Author
item PASIKATAN, MELCHOR - FORMERLY USDA ARS GMPRC
item HAQUE, EKRAMUL - KS STATE UNIVERSITY
item STEELE, JAMES - FORMERLY USDA ARS GMPRC
item SPILLMAN, CHARLES - KS STATE UNIVERSITY
item MILLIKEN, G - KS STATE UNIVERSITY

Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/20/2000
Publication Date: 6/1/2001
Citation: Pasikatan, M. C., E. Haque, J. L. Steele, C. K. Spillman, and G. A. Milliken. 2001. Evaluation of a near-infrared reflectance spectrometer as a granulation sensor for first-break ground wheat: Studies with six wheat classes.

Interpretive Summary: In order to automate roller mills used in flour milling, a sensor for the particle size distribution of ground wheat (what millers call granulation) must be developed. Millers use granulation information to adjust the distance between roll gaps of roller mills. Thus, this information influences the overall efficiency of the flour milling system. So-called automatic roller mills' roll gaps are actually adjusted and fixed by a human operator until analysis of the product streams indicate the need to re-adjust. We studied the feasibility of developing a granulation sensor out of a near-infrared reflectance (NIR) spectrometer. First, we evaluated whether particle size could be estimated by NIR reflectance independent of wheat classes. Sixty wheat samples, representing six classes and five roller mill gaps were each used for calibration and validation sets. These sets were ground independently. Calibration models were developed by partial least squares regression with cumulative mass of size fraction as reference value. Different ways of treating the spectral data (log (1/R), baseline correction, unit area normalization, and derivatives) and three wavelength regions (700-1500, 800-1600, and 600-1700 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. For the best model the square root of the sum of squared differences between reference and predicted data were 4.07, 1.75, 1.03, and 1.40 and coefficients of determination were 0.93, 0.90, 0.88, and 0.38, respectively for the >1041, >375, >240, and >136 µm size ranges. The next step is to evaluate the NIR reflectance-granulation technique for online application.

Technical Abstract: 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 potential granulation sensor of first-break ground wheat using offline methods. Sixty wheat samples, ground independently, and 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 reference value. Combinations of four data pretreatments (log (1/R), baseline correction, unit area normalization, and derivatives) and three wavelength regions (700-1500, 800-1600, and 600-1700 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, respectively for the >1041, >375, >240, and >136 µm size ranges were obtained for the best model. Results indicate that the NIR reflectance-granulation technique is ready for online evaluation.