Chaukhande Paresh, Luthra Satish Kumar, Patel R N, Padhi Siddhant Ranjan, Mankar Pooja, Mangal Manisha, Ranjan Jeetendra Kumar, Solanke Amolkumar U, Mishra Gyan Prakash, Mishra Dwijesh Chandra, Singh Brajesh, Bhardwaj Rakesh, Tomar Bhoopal Singh, Riar Amritbir Singh
Division of Vegetable Science, The Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.
ICAR-Central Potato Research Institute Regional Station, Modipuram, Meerut 250110, India.
Foods. 2024 May 25;13(11):1655. doi: 10.3390/foods13111655.
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 (RSQ), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQ 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 -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.
马铃薯是一种具有全球重要意义的作物，对粮食安全和营养至关重要。评估重要的营养特性对于提高营养价值至关重要。然而，传统的湿实验室方法用于筛选大量种质资源既耗时又耗资源。为应对这一挑战，我们使用近红外反射光谱法（NIRS）对不同的马铃薯种质进行快速特性估计。它采用分子吸收原理，利用电磁光谱的近红外部分精确快速地测定生化参数，并且是非破坏性的，能够在不损害样品的情况下监测特性。我们专注于基于改进偏最小二乘法（MPLS）的NIRS预测模型，以评估八个关键营养特性。通过排列组合执行各种数学处理以进行模型校准。外部验证预测准确性基于决定系数（RSQ）、性能与偏差比（RPD）以及低性能标准误差（SEP）。蛋白质、干物质和总酚的RSQ值分别较高，为0.937、0.892和0.759。蛋白质的RPD值较高（3.982），其次是干物质（3.041）和总酚（2.000），这表明模型具有出色的可预测性。配对检验证实实验室值与预测值之间的差异不显著。本研究提出了首个针对印度马铃薯种质的多性状NIRS预测模型。所开发的NIRS模型有效地预测了本研究中的其余基因型，证明了其广泛的适用性。这项工作突出了NIRS对马铃薯种质的快速筛选潜力，这是一种识别性状变异和优化育种策略的宝贵工具，以确保在气候变化面前实现可持续的马铃薯生产。
