Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm

Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.

Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm

In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.