Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture
Acceptance 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 var...
| Autores principales: | , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2021
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/164280 |
| _version_ | 1855530950632407040 |
|---|---|
| author | Buenafe, Reuben James Q. Kumanduri, Vasudev Sreenivasulu, Nese |
| author_browse | Buenafe, Reuben James Q. Kumanduri, Vasudev Sreenivasulu, Nese |
| author_facet | Buenafe, Reuben James Q. Kumanduri, Vasudev Sreenivasulu, Nese |
| author_sort | Buenafe, Reuben James Q. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Acceptance 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. |
| format | Journal Article |
| id | CGSpace164280 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1642802024-12-19T14:13:00Z Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture Buenafe, Reuben James Q. Kumanduri, Vasudev Sreenivasulu, Nese organic chemistry materials chemistry polymers and plastics Acceptance 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. 2021-05 2024-12-19T12:53:42Z 2024-12-19T12:53:42Z Journal Article https://hdl.handle.net/10568/164280 en Open Access Elsevier Buenafe, 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 |
| spellingShingle | organic chemistry materials chemistry polymers and plastics Buenafe, Reuben James Q. Kumanduri, Vasudev Sreenivasulu, Nese Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture |
| title | Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture |
| title_full | Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture |
| title_fullStr | Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture |
| title_full_unstemmed | Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture |
| title_short | Deploying viscosity and starch polymer properties to predict cooking and eating quality models: A novel breeding tool to predict texture |
| title_sort | deploying viscosity and starch polymer properties to predict cooking and eating quality models a novel breeding tool to predict texture |
| topic | organic chemistry materials chemistry polymers and plastics |
| url | https://hdl.handle.net/10568/164280 |
| work_keys_str_mv | AT buenafereubenjamesq deployingviscosityandstarchpolymerpropertiestopredictcookingandeatingqualitymodelsanovelbreedingtooltopredicttexture AT kumandurivasudev deployingviscosityandstarchpolymerpropertiestopredictcookingandeatingqualitymodelsanovelbreedingtooltopredicttexture AT sreenivasulunese deployingviscosityandstarchpolymerpropertiestopredictcookingandeatingqualitymodelsanovelbreedingtooltopredicttexture |