Cooking and eating quality of ethnic Bora rice ( <i>Oryza sativa</i> L.): machine learning‐based prediction of resistant starch content in ready‐to‐eat products
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.
Authors
- Pradip Chandra Dey
- Suraj Panja (ORCID: https://orcid.org/0009-0000-9382-0398)
- Narottam Dey (ORCID: https://orcid.org/0000-0002-2761-5473)
- Rup Kumar Kar (ORCID: https://orcid.org/0000-0001-8065-436X)
- Aswini Kumar Patra
Institutions
- Visva-Bharati University (IN)
- North Eastern Regional Institute of Science and Technology (IN)
- Assam Agricultural University (IN)
Publication Details
- Journal
- Journal of the Science of Food and Agriculture
- Published
- 2026-01-21
- DOI
- https://doi.org/10.1002/jsfa.70457
- Primary Topic
- Food composition and properties
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Gauhati University