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 and Riar, Amritbir Singh (2024) Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato. Foods, 13 (1655), pp. 1-14.
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Document available online at: https://www.mdpi.com/2304-8158/13/11/1655
Summary in the original language of the document
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.
EPrint Type: | Journal paper |
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Keywords: | near-infrared reflectance spectroscopy (NIRS), nutritional variability, MPLS regression model, RSQexternal/internal, RPD, Abacus, FiBL65213, CROPS4HD |
Agrovoc keywords: | Language Value URI English infrared spectrophotometry http://aims.fao.org/aos/agrovoc/c_28568 English potatoes http://aims.fao.org/aos/agrovoc/c_13551 English health diets -> therapeutic diets http://aims.fao.org/aos/agrovoc/c_7714 English food security http://aims.fao.org/aos/agrovoc/c_10967 |
Subjects: | Crop husbandry > Production systems Food systems > Food security, food quality and human health "Organics" in general > Countries and regions > Africa "Organics" in general > Countries and regions > Asia |
Research affiliation: | Switzerland > FiBL - Research Institute of Organic Agriculture Switzerland > International > Regions > Africa Switzerland > FiBL - Research Institute of Organic Agriculture Switzerland > International > Regions > Asia Switzerland > FiBL - Research Institute of Organic Agriculture Switzerland > Society > Agri-food policy > Food security Switzerland > FiBL - Research Institute of Organic Agriculture Switzerland > Crops > Seeds and breeding > Varitey testing India |
DOI: | 10.3390/foods13111655 |
Related Links: | https://www.fibl.org/en/themes/projectdatabase/projectitem/project/1961 |
Deposited By: | Forschungsinstitut für biologischen Landbau, FiBL |
ID Code: | 53128 |
Deposited On: | 15 Apr 2024 12:45 |
Last Modified: | 20 Jun 2024 08:38 |
Document Language: | English |
Status: | Published |
Refereed: | Not peer-reviewed |
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