Cooking and eating quality of ethnic Bora rice ( Oryza sativa L.): machine learning‐based prediction of resistant starch content in ready‐to‐eat products

Abstract BACKGROUND Assamese glutinous Bora rice ( Oryza sativa L.) is widely used for various ethnic food preparations. However, its resistant starch (RS) content, which influences the glycemic index (GI), remains poorly characterized. This exploratory study examined nine popular cooking and eating quality (CEQ) traits in 21 Bora rice lines, and performed molecular characterization and expression profiling during grain development, emphasizing machine learning (ML)‐based prediction of RS content. RESULTS The endosperm of Bora rice lines contains 80% to 90% starch, predominantly amylopectin, with a lower proportion of RS. Low gelatinization temperature, shorter cooking times at boiling temperatures, and soft gel length are key physicochemical traits of this group. Oryza sativa L. ‘Aghani Bora’ requires 68 minutes to prepare fully at room temperature. This reflects its low gelatinization temperature and soft gel formation, which are characteristic of Bora rice. Glycemic index‐linked polymorphic markers can support molecular breeding of Bora rice for low GI. GBSSI and SSIIa transcripts were downregulated in genotypes exhibiting low RS content. Significant correlations were observed among CEQ traits. The radial basis function network model for predicting RS content in Bora rice yielded a high R 2 (0.9155) and a low mean squared error (0.0690). CONCLUSION Amylose appears to have a critical role in determining most CEQ characteristics but has less influence on readiness to eat. Bora rice requires genetic improvement because its low RS content can lead to a high GI. The low‐cost machine learning (ML) model developed in this study provides an effective tool for rapid prediction of RS content in rice and other starchy cereal crops. © 2026 Society of Chemical Industry.