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 TITLE=Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm JOURNAL=Frontiers in Nutrition VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2022.1001551 DOI=10.3389/fnut.2022.1001551 ISSN=2296-861X ABSTRACT=

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.