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Correlation Between Cooked Rice Texture and Rapid Visco Analyser Measurements
Author(s) -
Champagne Elaine T.,
Bett Karen L.,
Vinyard Bryan T.,
McClung Anna M.,
Barton Franklin E.,
Moldenhauer Karen,
Linscombe Steve,
McKenzie Kent
Publication year - 1999
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.1999.76.5.764
Subject(s) - amylose , cultivar , chemistry , food science , texture (cosmology) , analyser , starch , agronomy , chromatography , artificial intelligence , computer science , image (mathematics) , biology
Although amylose content is considered the most important determinant of cooked rice texture, this constituent falls short as a predictor, because cultivars with similar amylose contents may differ in textural properties. Thus, amylography is used as one of a battery of tests, in addition to measuring amylose content, to improve differentiation of cultivars. The purpose of our study was to determine how well amylography conducted with a Rapid Visco Analyser (RVA) serves as a predictor of cooked rice texture, alone and in combination, with amylose content. Textural properties of 87 samples representing short‐, medium‐, and long‐grain rice cultivars were assessed by descriptive sensory and instrumental texture profile (TPA) analyses and related to RVA measurements. None of the cooked rice textural attributes, whether measured by descriptive analysis or TPA, were modeled by RVA with high accuracy (i.e., high r 2 ). Sensory texture attributes cohesiveness of mass, stickiness, and initial starchy coating and TPA attribute adhesiveness had the strongest correlations with RVA measurements. Setback explained most of the variance attributed to models describing these attributes; the strongest correlation was with cohesiveness of mass ( r = 0.69; equivalent to coefficient of determination, r 2 = 0.47). Inclusion of amylose and protein contents in regression analyses did not strengthen models. Exclusion of samples that cook atypically, based on amylose content or gelatinization temperature types, slightly improved the accuracy of RVA measurements for predicting cooked rice texture.