Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm
Cowpea (Vigna unguiculata (L.) Walp.) is one such legume that can facilitate achieving sustainable nutrition and climate change goals. Assessing nutritional traits conventionally can be laborious and time-consuming. NIRS is a technique used to rapidly determine biochemical parameters for large germp...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Frontiers Media
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/128704 |
| _version_ | 1855534196979662848 |
|---|---|
| author | Padhi, Siddhant Ranjan John, Racheal Bartwal, Arti Tripathi, Kuldeep Gupta, Kavita Wankhede, Dhammaprakash Pandhari Mishra, Gyan Prakash Kumar, Sanjeev Rana, Jai Chand Riar, Amritbir Bhardwaj, Rakesh |
| author_browse | Bartwal, Arti Bhardwaj, Rakesh Gupta, Kavita John, Racheal Kumar, Sanjeev Mishra, Gyan Prakash Padhi, Siddhant Ranjan Rana, Jai Chand Riar, Amritbir Tripathi, Kuldeep Wankhede, Dhammaprakash Pandhari |
| author_facet | Padhi, Siddhant Ranjan John, Racheal Bartwal, Arti Tripathi, Kuldeep Gupta, Kavita Wankhede, Dhammaprakash Pandhari Mishra, Gyan Prakash Kumar, Sanjeev Rana, Jai Chand Riar, Amritbir Bhardwaj, Rakesh |
| author_sort | Padhi, Siddhant Ranjan |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Cowpea (Vigna unguiculata (L.) Walp.) is one such legume that can facilitate achieving sustainable nutrition and climate change goals. Assessing nutritional traits conventionally can be laborious and time-consuming. NIRS is a technique used to rapidly determine biochemical parameters for large germplasm. NIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression. Higher RSQexternal values such as 0.903, 0.997, 0.901, 0.706, and 0.955 were obtained for protein, starch, TDF, phenols, and phytic acid respectively. Models for all the traits displayed RPD values of >2.5 except phenols and low SEP indicating the excellent prediction of models. For all the traits worked, p-value ≥ 0.05 implied the accuracy and reliability score >0.8 (except phenol) ensured the applicability of the models. These prediction models will facilitate high throughput screening of large cowpea germplasm in a non-destructive way and the selection of desirable chemotypes in any genetic background with huge application in cowpea crop improvement programs across the world. |
| format | Journal Article |
| id | CGSpace128704 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1287042025-12-08T10:29:22Z Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm Padhi, Siddhant Ranjan John, Racheal Bartwal, Arti Tripathi, Kuldeep Gupta, Kavita Wankhede, Dhammaprakash Pandhari Mishra, Gyan Prakash Kumar, Sanjeev Rana, Jai Chand Riar, Amritbir Bhardwaj, Rakesh germplasm nutritional requirements evaluation techniques germoplasma necesidades de nutrientes técnicas de evaluación Cowpea (Vigna unguiculata (L.) Walp.) is one such legume that can facilitate achieving sustainable nutrition and climate change goals. Assessing nutritional traits conventionally can be laborious and time-consuming. NIRS is a technique used to rapidly determine biochemical parameters for large germplasm. NIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression. Higher RSQexternal values such as 0.903, 0.997, 0.901, 0.706, and 0.955 were obtained for protein, starch, TDF, phenols, and phytic acid respectively. Models for all the traits displayed RPD values of >2.5 except phenols and low SEP indicating the excellent prediction of models. For all the traits worked, p-value ≥ 0.05 implied the accuracy and reliability score >0.8 (except phenol) ensured the applicability of the models. These prediction models will facilitate high throughput screening of large cowpea germplasm in a non-destructive way and the selection of desirable chemotypes in any genetic background with huge application in cowpea crop improvement programs across the world. 2022-09-23 2023-02-14T11:17:13Z 2023-02-14T11:17:13Z Journal Article https://hdl.handle.net/10568/128704 en Open Access application/pdf Frontiers Media Padhi, S.R.; John, R.; Bartwal, A.; Tripathi, K.; Gupta, K.; Wankhede, D.P.; Mishra, G.P.; Kumar, S.; Rana, J.C.; Riar, A.; Bhardwaj, R. (2022) Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm. Frontiers in Nutrition 9 12 p. ISSN: 2296-861X |
| spellingShingle | germplasm nutritional requirements evaluation techniques germoplasma necesidades de nutrientes técnicas de evaluación Padhi, Siddhant Ranjan John, Racheal Bartwal, Arti Tripathi, Kuldeep Gupta, Kavita Wankhede, Dhammaprakash Pandhari Mishra, Gyan Prakash Kumar, Sanjeev Rana, Jai Chand Riar, Amritbir Bhardwaj, Rakesh Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm |
| title | Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm |
| title_full | Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm |
| title_fullStr | Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm |
| title_full_unstemmed | Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm |
| title_short | Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm |
| title_sort | development and optimization of nirs prediction models for simultaneous multi trait assessment in diverse cowpea germplasm |
| topic | germplasm nutritional requirements evaluation techniques germoplasma necesidades de nutrientes técnicas de evaluación |
| url | https://hdl.handle.net/10568/128704 |
| work_keys_str_mv | AT padhisiddhantranjan developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT johnracheal developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT bartwalarti developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT tripathikuldeep developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT guptakavita developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT wankhededhammaprakashpandhari developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT mishragyanprakash developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT kumarsanjeev developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT ranajaichand developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT riaramritbir developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm AT bhardwajrakesh developmentandoptimizationofnirspredictionmodelsforsimultaneousmultitraitassessmentindiversecowpeagermplasm |