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

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
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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