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United States Department of Agriculture

Agricultural Research Service

Title: Rice Quality: a Spectroscopic Database for Quality Measurements

Authors
item Barton Ii, Franklin
item Himmelsbach, David
item Meadows, Frederick
item McClung, Anna
item McClung, Anna
item Champagne, Elaine

Submitted to: United States Japan Natural Resources Protein Panel
Publication Type: Proceedings
Publication Acceptance Date: October 15, 2001
Publication Date: N/A

Interpretive Summary: Rice is traded all over the world and objective methods are needed to evaluate quality. All too often premiums and penalties are assessed on the basis of preferences. Near Infrared (NIR) spectroscopy is used to measure composition on a routine basis. This study has been undertaken to develop a spectral database in the NIR, Raman and mid-infrared (MIR) regions. With ha comprehensive database and the appropriate reference values a set of quality standards can be developed that will permit rapid non-invasive analysis.

Technical Abstract: Previous studies to develop a rice spectral database have shown that two problems exist. First the analysis for Apparent Amylose is not truly measuring amylose content. Rather it is what "appears" to be amylose to the reagents used in the assay. Second, the amylose content of rice is concentrated into three areas; that less than 3% (waxy), 10-12%, and >20% (high amylose). In order to create a good chemometric spectral database the variation of chemical components must be uniformly sampled across the spectral diversity. In the previous studies approximately 220 samples were used to represent the different drying, milling and growing conditions in the United States and another set of 100 samples of different cultivars to represent what was present in the commercial varieties. A new set of 308 rice cultivars from breeding trial has been collected to fill in the gaps in amylose and protein content. These samples have been scanned with the FOSS NIRSystems 6500 and with the Bruker FT-22N NIR interferometer. The new instrument has higher resolution which may be helpful with amylose models.

Last Modified: 9/22/2014
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