Table_1_Testing the Linearity Assumption for Starch Structure-Property Relationships in Rices.DOCX

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.

众多淀粉类食品的特性与各种结构参数之间存在着显著的统计学相关性。相关性的显著性通过p值进行评判，该评估基于结构参数与特性之间线性关系的假设。本研究旨在检验该线性假设，以探讨其是否能够预测在测量条件之外的特性。为此，我们使用了常见的驯化水稻（DRs）和澳大利亚野生水稻（AWRs），后者与DRs相比在结构参数和特性上存在显著差异。研究结果揭示了：（1）特性不仅受支链淀粉或直链淀粉链长度分布或直链淀粉含量控制，其他结构特征亦至关重要；（2）线性模型在一定程度上可以预测AWRs和DRs的焓变ΔHg，但通常并不精确；它可以从链长度分布和较长直链淀粉链（聚合度>500）的数量预测出令人满意的 indica 水稻的ΔHg；（3）在较长直链淀粉链的数量方面，线性模型可以以可接受的准确性预测AWRs和DRs的粘性。因此，对于结构-特性相关性，若亦用于定量预测，则通常采用的线性假设需持审慎态度。