Near-infrared reflectance spectroscopy (NIRS): An innovative, rapid, economical, easy and non-destructive whole grain analysis method for nutritional profiling of pearl millet genotypes
Pearl millet, known for its nutritional excellence and climatic resilience, is becoming important in addressing food and nutritional security Current work introduces Near Infrared Spectroscopy models to estimate nutrients in pearl millet grains. The model is quick, economic and non-destructive alter...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Elsevier
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/177262 |
| _version_ | 1855538759697694720 |
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| author | Yadav, Sunaina Tomar, Maharishi Singhal, Tripti Joshi, Nilesh Bhargavi, H. A. Aavula, Naveen Langyan, Sapna Joshi, Tanay Satyavathi, C Tara Rana, Jai Chand Singh, Sumer Pal Bhardwaj, Rakesh Riar, Amritbir |
| author_browse | Aavula, Naveen Bhardwaj, Rakesh Bhargavi, H. A. Joshi, Nilesh Joshi, Tanay Langyan, Sapna Rana, Jai Chand Riar, Amritbir Satyavathi, C Tara Singh, Sumer Pal Singhal, Tripti Tomar, Maharishi Yadav, Sunaina |
| author_facet | Yadav, Sunaina Tomar, Maharishi Singhal, Tripti Joshi, Nilesh Bhargavi, H. A. Aavula, Naveen Langyan, Sapna Joshi, Tanay Satyavathi, C Tara Rana, Jai Chand Singh, Sumer Pal Bhardwaj, Rakesh Riar, Amritbir |
| author_sort | Yadav, Sunaina |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Pearl millet, known for its nutritional excellence and climatic resilience, is becoming important in addressing food and nutritional security Current work introduces Near Infrared Spectroscopy models to estimate nutrients in pearl millet grains. The model is quick, economic and non-destructive alternative to traditional methods, useful in advancing the single plant progenies for improving nutrient content in segregating generations. Spectra were acquired from 403 varied genotypes, and mathematical optimizations using derivatives were performed to enhance the models. The optimal configurations were "2,36,6,2" (order of derivatives, gap, first smoothing and second smoothing, respectively) for amylose, "2,32,6,2" for starch, "2,32,8,2" for oil and protein, and "3,36,6,2" for phytic acid. The models were refined using modified partial least squares (MPLS) regression on spectra processed to eliminate variations with standard normal variate (SNV) and detrending (DT) techniques. The adjusted MPLS models exhibited impressive coefficients of determination of 0.985, 0.984, 0.986, 0.969 and 0.993 for amylose, protein, oil, starch and phytic acid, respectively. The SEP(C) values for amylose (0.347), starch (0.732), protein (0.313), phytic acid (0.014), and oil (0.162) suggest variable levels of predictive precision. Validation with independent samples showed superior predictive performance with coefficients of determination values ranging from 0.878 for phytic acid to 0.976 for protein, minimal bias, high ratios of prediction to deviation (2.93–5.81), and no significant differences between the predicted and reference values (p > 0.05). These advanced Near-Infrared Spectroscopy models allow quick and cost-effective nutritional assessment of pearl millet germplasm and breeding lines, supporting biofortification initiatives and enhancing nutritional security. |
| format | Journal Article |
| id | CGSpace177262 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1772622025-12-08T09:54:28Z Near-infrared reflectance spectroscopy (NIRS): An innovative, rapid, economical, easy and non-destructive whole grain analysis method for nutritional profiling of pearl millet genotypes Yadav, Sunaina Tomar, Maharishi Singhal, Tripti Joshi, Nilesh Bhargavi, H. A. Aavula, Naveen Langyan, Sapna Joshi, Tanay Satyavathi, C Tara Rana, Jai Chand Singh, Sumer Pal Bhardwaj, Rakesh Riar, Amritbir pearl millet Pearl millet, known for its nutritional excellence and climatic resilience, is becoming important in addressing food and nutritional security Current work introduces Near Infrared Spectroscopy models to estimate nutrients in pearl millet grains. The model is quick, economic and non-destructive alternative to traditional methods, useful in advancing the single plant progenies for improving nutrient content in segregating generations. Spectra were acquired from 403 varied genotypes, and mathematical optimizations using derivatives were performed to enhance the models. The optimal configurations were "2,36,6,2" (order of derivatives, gap, first smoothing and second smoothing, respectively) for amylose, "2,32,6,2" for starch, "2,32,8,2" for oil and protein, and "3,36,6,2" for phytic acid. The models were refined using modified partial least squares (MPLS) regression on spectra processed to eliminate variations with standard normal variate (SNV) and detrending (DT) techniques. The adjusted MPLS models exhibited impressive coefficients of determination of 0.985, 0.984, 0.986, 0.969 and 0.993 for amylose, protein, oil, starch and phytic acid, respectively. The SEP(C) values for amylose (0.347), starch (0.732), protein (0.313), phytic acid (0.014), and oil (0.162) suggest variable levels of predictive precision. Validation with independent samples showed superior predictive performance with coefficients of determination values ranging from 0.878 for phytic acid to 0.976 for protein, minimal bias, high ratios of prediction to deviation (2.93–5.81), and no significant differences between the predicted and reference values (p > 0.05). These advanced Near-Infrared Spectroscopy models allow quick and cost-effective nutritional assessment of pearl millet germplasm and breeding lines, supporting biofortification initiatives and enhancing nutritional security. 2025-06 2025-10-22T09:43:13Z 2025-10-22T09:43:13Z Journal Article https://hdl.handle.net/10568/177262 en Open Access application/pdf Elsevier Yadav, S.; Tomar, M.; Singhal, T.; Joshi, N.; Bhargavi, H.A.; Aavula, N.; Langyan, S.; Joshi, T.; Satyavathi, C.T.; Rana, J.C.; Singh, S.P.; Bhardwaj, R.; Riar, A. (2025) Near-infrared reflectance spectroscopy (NIRS): An innovative, rapid, economical, easy and non-destructive whole grain analysis method for nutritional profiling of pearl millet genotypes. Journal of Food Composition and Analysis 142: 107373. ISSN: 0889-1575 |
| spellingShingle | pearl millet Yadav, Sunaina Tomar, Maharishi Singhal, Tripti Joshi, Nilesh Bhargavi, H. A. Aavula, Naveen Langyan, Sapna Joshi, Tanay Satyavathi, C Tara Rana, Jai Chand Singh, Sumer Pal Bhardwaj, Rakesh Riar, Amritbir Near-infrared reflectance spectroscopy (NIRS): An innovative, rapid, economical, easy and non-destructive whole grain analysis method for nutritional profiling of pearl millet genotypes |
| title | Near-infrared reflectance spectroscopy (NIRS): An innovative, rapid, economical, easy and non-destructive whole grain analysis method for nutritional profiling of pearl millet genotypes |
| title_full | Near-infrared reflectance spectroscopy (NIRS): An innovative, rapid, economical, easy and non-destructive whole grain analysis method for nutritional profiling of pearl millet genotypes |
| title_fullStr | Near-infrared reflectance spectroscopy (NIRS): An innovative, rapid, economical, easy and non-destructive whole grain analysis method for nutritional profiling of pearl millet genotypes |
| title_full_unstemmed | Near-infrared reflectance spectroscopy (NIRS): An innovative, rapid, economical, easy and non-destructive whole grain analysis method for nutritional profiling of pearl millet genotypes |
| title_short | Near-infrared reflectance spectroscopy (NIRS): An innovative, rapid, economical, easy and non-destructive whole grain analysis method for nutritional profiling of pearl millet genotypes |
| title_sort | near infrared reflectance spectroscopy nirs an innovative rapid economical easy and non destructive whole grain analysis method for nutritional profiling of pearl millet genotypes |
| topic | pearl millet |
| url | https://hdl.handle.net/10568/177262 |
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