Frontiers in Nutrition (May 2022)
Testing the Linearity Assumption for Starch Structure-Property Relationships in Rices
Abstract
Many properties of starch-containing foods are significantly statistically correlated with various structural parameters. The significance of a correlation is judged by the p-value, and this evaluation is based on the assumption of linear relationships between structural parameters and properties. We here examined the linearity assumption to see if it can be used to predict properties at conditions that are not close to those under which they were measured. For this we used both common domesticated rices (DRs) and Australian wild rices (AWRs), the latter having significantly different structural parameters and properties compared to DRs. The results showed that (1) the properties were controlled by more than just the amylopectin or amylose chain-length distributions or amylose content, other structural features also being important, (2) the linear model can predict the enthalpy ΔHg of both AWRs and DRs from the structural parameters to some extent but is often not accurate; it can predict the ΔHg of indica rices with acceptable accuracy from the chain length distribution and the amount of longer amylose chains (degree of polymerization > 500), and (3) the linear model can predict the stickiness of both AWRs and DRs to acceptable accuracy in terms of the amount of longer amylose chains. Thus, the commonly used linearity assumption for structure-property correlations needs to be regarded circumspectly if also used for quantitative prediction.
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