Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture

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dc.creatorBuenafe, Reuben James Q.
dc.creatorKumanduri, Vasudev
dc.creatorSreenivasulu, Nese
dc.date2021-05
dc.date2024-12-19T12:53:42Z
dc.date2024-12-19T12:53:42Z
dc.date.accessioned2026-06-27T04:12:28Z
dc.descriptionAcceptance of new rice genotypes demanded by rice value chain depends on premium value of varieties that match consumer demands of regional preferences. High throughput prediction tools are not available to breeders to classify cooking and eating quality (CEQ) ideotypes and to capture texture of varieties. The pasting properties in combination with starch properties were used to develop two layered models in order to classify the rice varieties into twelve distinct CEQ ideotypes with unique sensory profiles. Classification models developed using random forest method depicted the overall accuracy of 96 %. These CEQ models were found to be robust to predict ideotypes in both Indica and Japonica diversity panels grown under dry and wet seasons and across the years. We conducted random forest modeling using 1.8 million high density SNPs and identified top 1000 SNP features which explained CEQ model classification with the accuracy of 0.81. Furthermore these CEQ models were found to be valuable to predict textural preferences of IRRI breeding lines released during 1960–2013 and mega varieties preferred in South and South East Asia.
dc.identifierhttps://hdl.handle.net/10568/164280
dc.identifier.urihttp://hdl.handle.net/123456789/25113
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceBuenafe, Reuben James Q.; Kumanduri, Vasudev and Sreenivasulu, Nese. 2021. Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture. Carbohydrate Polymers, Volume 260 p. 117766
dc.subjectorganic chemistry
dc.subjectmaterials chemistry
dc.subjectpolymers and plastics
dc.titleDeploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture
dc.typeJournal Article

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Instituto Internacional de Investigación del Arroz (IRRI)