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Optimal Geometries for the Development of Rice Quality Spectroscopic Chemometric Models

¹USDA-Agricultural Research Service, Richard B. Russell Agricultural Research Center, P.O. Box 5677, Athens, GA 30604-5677. Mention of trademark or proprietary products does not constitute a guarantee or warranty by the U.S. Department of Agriculture and does not imply its approval to the exclusion of other products that may also be suitable. ²Corresponding author. E-mail: wbarton@athens.net ³USDA-Agricultural Research Service, Southern Regional Research Center P. O. Box 19887, New Orleans, LA 70179.

Three sample geometries, two different instrument types, and two spectral collection modes (reflectance and transmission) were used to assess rice quality and develop chemometric models for composition and sensory characteristics. Rice samples (120) including three cultivars, two growing locations, five drying treatments, two moisture levels, and two levels of milling were scanned in two locations. Data collected for modeling included amylose, protein, moisture, whiteness, transparency, and milling degree. Taste and texture were determined with the use of separate trained sensory panels. The NIR models show that composition is best modeled in the 1,100–2,500 nm range, while the physical properties of whiteness, transparency and milling degree are best modeled in the 750–1,050 nm range. Additional models were developed using limited data subsets of the spectral data points. In some cases, adequate models were generated with as few as 20 wavelength data points. Results show that no one spectroscopic protocol is best for all analytes in rice and that for any complex food matrix more than one preprocessing or spectral range protocol is needed.