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ARS Home » Southeast Area » New Orleans, Louisiana » Southern Regional Research Center » Food Processing and Sensory Quality Research » Research » Publications at this Location » Publication #131785

Title: Sensory characteristics of diverse rice cultivars

item Champagne, Elaine
item Bett Garber, Karen
item Grimm, Casey
item McClung, Anna
item McClung, Anna
item Bergman, Christine

Submitted to: American Association of Cereal Chemists Meetings
Publication Type: Abstract Only
Publication Acceptance Date: 3/15/2012
Publication Date: 10/13/2002
Citation: Champagne, E.T., Bett Garber, K.L., Grimm, C.C., Mcclung, A.M., Bergman, C.J. 2002. Sensory characteristics of diverse rice cultivars. American Association of Cereal Chemists Meetings. 161.

Interpretive Summary:

Technical Abstract: Lack of a knowledge-base for predicting how genetic, pre-harvest, and post-harvest factors affect the sensory characteristics of rice results in producers and processors not having control over the sensory quality of their products. In this study, differences in the texture and flavor of seventeen diverse cultivars related to genetic differences were characterized. Sensory attributes of cooked rice were measured by panelists using descriptive sensory analysis methodology. Cooked texture of the cultivars varied widely and correlated well with amylose content with correlation coefficient (r) values in the range 0.76 - 0.97 for eleven of the fourteen attributes. Flavor attributes intensities were low and similar, with the exception of grain flavor. Grain flavor ranged in intensity from 2.5 - 4.8 and correlated highly and negatively with amylose content r=-.95). Principal Component Analysis (PCA) and Ward's Cluster Analysis grouped the cultivars into three and/or five clusters (Ward's Cluster Analysis) with cultivars belonging to each cluster having common texture and flavor characteristics. These clusters provide insight into similarities and differences in both texture and flavor of cultivars which cannot be gleamed from physicochemical data (e.g., amylose and protein contents).