Zhao Yingting, Henry Robert J, Gilbert Robert G
Centre for Nutrition and Food Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia.
Jiangsu Key Laboratory of Crop Genetics and Physiology/State Key Laboratory of Hybrid Rice, College of Agriculture, Yangzhou University, Yangzhou, China.
Front Nutr. 2022 May 23;9:916751. doi: 10.3389/fnut.2022.916751. eCollection 2022.
Many properties of starch-containing foods are significantly statistically correlated with various structural parameters. The significance of a correlation is judged by the -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.
含淀粉食物的许多特性与各种结构参数在统计学上显著相关。相关性的显著性通过P值来判断，而这种评估是基于结构参数与特性之间存在线性关系的假设。我们在此检验了线性假设，以查看它是否可用于预测在与测量条件不太接近的情况下的特性。为此，我们使用了普通的驯化水稻（DR）和澳大利亚野生稻（AWR），后者与DR相比，其结构参数和特性有显著差异。结果表明：（1）这些特性不仅仅受支链淀粉或直链淀粉链长分布或直链淀粉含量的控制，其他结构特征也很重要；（2）线性模型可以在一定程度上根据结构参数预测AWR和DR的焓变ΔHg，但往往不准确；它可以根据链长分布和较长直链淀粉链的数量（聚合度>500）以可接受的准确度预测籼稻的ΔHg；（3）线性模型可以根据较长直链淀粉链的数量以可接受的准确度预测AWR和DR的粘性。因此，如果将常用的结构-特性相关性线性假设也用于定量预测，则需要谨慎对待。
