Starch Quality Matrix (SQM): An Analytical Model for Predicting Resistant Starch (RS) in Rice (Oryza sativa.L)

Abstract Fine-tuning of starch digestibility to have a superior digestion-resistant fraction, i.e., resistant starch (RS), is of great interest in the agriculture, food, and nutrition domains, as it not only limits the glycemic amplitude but is also good for gut health. Traditionally, RS quantification has been done using human digestion simulation assays, which are technically demanding, time-intensive, and expensive. Considering the correlation of starch digestibility with the structural milieu, bestowed due to components, as well as other attributes, the present study aimed to develop a fundamental link among the reported explanatory variables affecting starch digestibility (microstructure, gelatinization temperature, total starch, amylose, amylopectin, and RS) in the form of a prediction model, the starch quality matrix (SQM). SQM was created using Pearson's correlation and Cramer's V statistics, and a regression model was created using the most significant variables (total starch and amylose) for RS prediction. The correlation between RS and inherent glycemic potential (IGP) was further validated using in-house developed in-vitro starch hydrolyzation kinetics. This study demonstrated for the first time a perspective relationship affecting starch digestibility and developed the SQM tool, which will aid in future trials to breed high-RS rice varieties with a low glycemic index.