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Evaluation of a Near‐Infrared Reflectance Spectrometer as a Granulation Sensor for First‐Break Ground Wheat: Studies with Six Wheat Classes
Author(s) -
Pasikatan M. C.,
Haque E.,
Steele J. L.,
Spillman C. K.,
Milliken G. A.
Publication year - 2001
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cchem.2001.78.6.730
Subject(s) - granulation , partial least squares regression , normalization (sociology) , chemistry , analytical chemistry (journal) , calibration , near infrared spectroscopy , remote sensing , second derivative , reflectivity , spectrometer , wheat flour , wavelength , mathematics , statistics , chromatography , food science , materials science , optics , composite material , geology , physics , mathematical analysis , sociology , anthropology
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 r 2 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.