ORIGINAL RESEARCH article
Front. Nutr.
Sec. Nutrition and Food Science Technology
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1632421
This article is part of the Research TopicImpact of Soil Health on Nutritional Quality of Crops and Human HealthView all 3 articles
Use of Near-Infrared Spectroscopy for Screening the oil content, protein, phytic acid, glucosinolates and fatty acid profile in oilseed brassica species
Provisionally accepted
- 1Indian Council of Agricultural Research- Indian Institute of Rapeseed mustard Research, Bharatpur, India
- 2Department of Biotechnology, GLA University, Mathura, Uttar Pradesh, India
- 3Agriculture University, Jodhpur, Jodhpur, Rajasthan, India
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The escalating global demand for vegetable oils underscores the need to enhance the quality and yield of oilseed crops, with Brassica species due to their rich oil content and nutritional benefits. Traditional methods for assessing seed quality traits are often slow and destructive, limiting their scalability in breeding programs. This study presents Fourier Transform Near-Infrared (FT-NIR) spectroscopy as a rapid, non-destructive alternative to evaluate these critical traits across 80 diverse Brassica genotypes including three species namely B.juncea, B.napus and B. rapa. By integrating FT-NIR with Principal Component Analysis and Partial Least Squares regression, we developed robust calibration models, achieving high predictive accuracy (R² > 0.85 for key fatty acids; R² = 0.92 for oil content) and low error rates (MAE < 1.8). Our results reveal significant genetic variability, with oil content showing remarkable stability (CV = 0.68%) and erucic acid exhibiting the highest variation (CV = 9.18%), offering promising avenues for targeted breeding. PCA elucidated 68% of the total variance, spotlighting oleic acid, erucic acid and oil content as key drivers of genetic differentiation. Pearson correlation analysis also revealed a strong inverse relationship between oleic acid and erucic acid, suggesting potential genetic linkages that could be exploited in breeding programs. The FT-NIR models demonstrated superior throughput and reliability compared to conventional wet chemistry. These findings not only streamline seed quality assessment but also pave the way for breeding Brassica cultivars with optimized nutritional profiles high in beneficial polyunsaturated fatty acids and low in anti-nutritional factors.
Keywords: spectroscopy, Seed quality traits, Brassica accessions, Calibration models, Prediction efficiency, Pearson Correlation. Abbreviations Principal Component Analysis (PCA), partial least squares (PLS), Fourier transform near-infrared (FT-NIR)
Received: 21 May 2025; Accepted: 05 Aug 2025.
Copyright: © 2025 Sharma, Sogarwal, Kumar and Choudhary. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Anubhuti Sharma, Indian Council of Agricultural Research- Indian Institute of Rapeseed mustard Research, Bharatpur, India
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