Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm
For predicting texture suited for South and South East Asia, most of the breeding programs tend to focus on developing rice varieties with intermediate to high amylose content in indica subspecies. However, varieties within the high amylose content class may still be distinguishable by consumers, wh...
| Autores principales: | , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2018
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/164788 |
| _version_ | 1855536579888545792 |
|---|---|
| author | Cuevas, Rosa Paula O. Domingo, Cyril John Sreenivasulu, Nese |
| author_browse | Cuevas, Rosa Paula O. Domingo, Cyril John Sreenivasulu, Nese |
| author_facet | Cuevas, Rosa Paula O. Domingo, Cyril John Sreenivasulu, Nese |
| author_sort | Cuevas, Rosa Paula O. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | For predicting texture suited for South and South East Asia, most of the breeding programs tend to focus on developing rice varieties with intermediate to high amylose content in indica subspecies. However, varieties within the high amylose content class may still be distinguishable by consumers, who are able to distinguish texture that cannot be differentiated by proxy cooking quality indicators This study explored a suite of assays to capture viscosity, rheometric, and mechanical texture parameters for characterising cooked rice texture in a set of 211 rice accessions from a diversity panel and employed multivariate approaches to classify rice varieties into distinct cooking quality classes. Results suggest that when the amylose content range is narrowed to the intermediate to high classes, parameters determined by rheometry and RVA become diagnostic. Modeled parameters distinguishing cooking quality ideotypes within the same range of amylose classes differ in textural parameters scored by a descriptive sensory panel Our results reinforced the notion that it is important to define cooking quality classes in indica subtypes based on multidimensional parameters, by going beyond amylose predictions. These predictive cooking models will be handy in capturing cooking and eating quality properties that address consumer preferences in future breeding programs. Policy implications of such findings may lead to changes in criteria used in assessing grain quality in the intermediate to high amylose classes. |
| format | Journal Article |
| id | CGSpace164788 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1647882024-12-19T14:13:22Z Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm Cuevas, Rosa Paula O. Domingo, Cyril John Sreenivasulu, Nese cooking cooking quality indica rice multivariate analysis organoleptic traits For predicting texture suited for South and South East Asia, most of the breeding programs tend to focus on developing rice varieties with intermediate to high amylose content in indica subspecies. However, varieties within the high amylose content class may still be distinguishable by consumers, who are able to distinguish texture that cannot be differentiated by proxy cooking quality indicators This study explored a suite of assays to capture viscosity, rheometric, and mechanical texture parameters for characterising cooked rice texture in a set of 211 rice accessions from a diversity panel and employed multivariate approaches to classify rice varieties into distinct cooking quality classes. Results suggest that when the amylose content range is narrowed to the intermediate to high classes, parameters determined by rheometry and RVA become diagnostic. Modeled parameters distinguishing cooking quality ideotypes within the same range of amylose classes differ in textural parameters scored by a descriptive sensory panel Our results reinforced the notion that it is important to define cooking quality classes in indica subtypes based on multidimensional parameters, by going beyond amylose predictions. These predictive cooking models will be handy in capturing cooking and eating quality properties that address consumer preferences in future breeding programs. Policy implications of such findings may lead to changes in criteria used in assessing grain quality in the intermediate to high amylose classes. 2018-12 2024-12-19T12:54:19Z 2024-12-19T12:54:19Z Journal Article https://hdl.handle.net/10568/164788 en Open Access Springer Cuevas, Rosa Paula O.; Domingo, Cyril John and Sreenivasulu, Nese. 2018. Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm. Rice, Volume 11, no. 1 |
| spellingShingle | cooking cooking quality indica rice multivariate analysis organoleptic traits Cuevas, Rosa Paula O. Domingo, Cyril John Sreenivasulu, Nese Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm |
| title | Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm |
| title_full | Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm |
| title_fullStr | Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm |
| title_full_unstemmed | Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm |
| title_short | Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm |
| title_sort | multivariate based classification of predicting cooking quality ideotypes in rice oryza sativa l indica germplasm |
| topic | cooking cooking quality indica rice multivariate analysis organoleptic traits |
| url | https://hdl.handle.net/10568/164788 |
| work_keys_str_mv | AT cuevasrosapaulao multivariatebasedclassificationofpredictingcookingqualityideotypesinriceoryzasativalindicagermplasm AT domingocyriljohn multivariatebasedclassificationofpredictingcookingqualityideotypesinriceoryzasativalindicagermplasm AT sreenivasulunese multivariatebasedclassificationofpredictingcookingqualityideotypesinriceoryzasativalindicagermplasm |