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ARS Home » Research » Publications at this Location » Publication #123150
Title: QUALITY EVALUATION OF RICE BY SPECTROSCOPY

Author
item Barton Ii, Franklin
item Himmelsbach, David
item Meadows, Frederick

Submitted to: International Conference on Agricultural Machinery Engineering
Publication Type: Proceedings
Publication Acceptance Date: 6/18/2001
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Three things are required for any Spectroscopic Chemometric technique to be successful. First, the reference data must have a real relationship to the spectral data so that the appropriate statistical data treatment must be employed. Second, You must have a well behaved instrument, i.e., high S/N and wavelength precession. Finally, the spectra must be obtained in an optimal geometry to permit the first two criteria to achieve the best results. 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. Results show that no one spectroscopic protocol is best for all analytes in rice and that for any complex food matrix more than one protocol is needed. Two hundred and forty rice samples comprising three cultivars, five drying treatments, two moisture levels and two levels sof milling were scanned in both locations. Data collected for modeling included protein, moisture, amylose, free fatty acid, whiteness, transparency, milling degree and taste and texture by trained sensory panels. Preliminary data suggest that moisture level at milling plays a large role in taste differences and milling level (normal and deep) affect texture. The NIR models show that composition is best modeled in the 1100- 2500 nm range, while the physical properties of whiteness, transparency and milling degree are best modeled in the 750-1050 nm range. The types of spectroscopy used to evaluate rice cover the electromagnetic spectrum from visible to Mid Infrared and Raman. In addition two dimensional regression techniques have been used to assist in the interpretation of the spectra and their relationship to quality and rheological parameters.