Optimizing near Infrared Reflectance Spectroscopy to Predict Nutritional Quality of Chickpea Straw for Livestock Feeding

Quantitative assessment of phytochemicals in chickpea beverages using NIR spectroscopy

The prospects of near-infrared (NIR) spectroscopy combined with effective variable selection algorithms for quantifying phytochemical compounds in chickpea beverages were investigated in this study. As reference measurement analysis, the phytochemicals were extracted and identified via high-performance liquid chromatography. Multivariate algorithms were then applied, analyzed, and evaluated using correlation coefficients of validation set (Rp), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). Accordingly, the competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model achieved superior performance for biochanin A (Rp = 0.933, RPD = 3.63), chlorogenic acid (Rp = 0.928, RPD = 3.52), p-coumaric acid (Rp = 0.900, RPD = 2.37), and stigmasterol (Rp = 0.932, RPD = 3.15), respectively. Hence, this study demonstrated that NIR spectroscopy paired with CARS-PLS could be used for nondestructive quantitative prediction of phytochemicals in chickpea beverages during manufacture and storage.

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 germplasm. NIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression. Higher RSQ_external 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.