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Relationship of cooked-rice nutritionally important starch fractions with other physicochemical properties

Patindol, James, Guraya, Harmeet, Champagne, Elaine, Chen, Ming-Hsuan, McClung, Anna
Die Stärke = 2010 v.62 no.5 pp. 246
amylose, cooked foods, cooking quality, crude protein, cultivars, gelatinization, gels, glass transition temperature, linear models, microsatellite repeats, pasting properties, physicochemical properties, principal component analysis, regression analysis, resistant starch, rice, rice starch, thermal properties, variance, viscosity
Sixteen rice cultivars representing five cytosine-thymine repeat (CTn) microsatellite genetic marker groups were analyzed for their cooked rice nutritionally important starch fractions (NISFs, which include rapidly digestible (RDS), slowly digestible (SDS), and resistant starch (RS)), basic grain quality indices (apparent amylose (AM), crude protein (CP), alkali spreading value (AS), and gel consistency (GC)), pasting characteristics, and thermal properties. Chemometric tools (bivariate correlation, principal component analysis, multiple linear regression, and partial least squares regression) were used to establish the association of NISF with other milled rice physicochemical properties. CT₁₁ was generally associated with high percentages of RS and SDS, and a low percentage of RDS. CT₁₄ was associated with low SDS; whereas, CT₁₇ and CT₁₈ were associated with low RS. The CT₂₀ cultivars were similar to CT₁₁ in SDS and RS; and to CT₁₄, CT₁₇, and CT₁₈ in RDS content. RDS, SDS, and RS were loaded on three different quadrants of the principal component similarity map. RDS was not significantly correlated with any of the physicochemical properties; whereas, SDS was positively correlated with GC. RS was positively correlated with AM, setback (SB) viscosity, total setback (TSB) viscosity, and peak gelatinization temperature; and negatively correlated with breakdown (BD) viscosity. Multivariate techniques indicated lack of robustness in predicting RDS and SDS as the models only explained <50% of the variance. More robust regression models were obtained for RS, explaining >60% of its variation. Basic grain quality indices explained NISF variations better than pasting and thermal properties.