Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm
Lablab bean (Lablab purpureus L.) is a multipurpose crop, commonly used for food, feed, and fodder, and its potential as a plant-based meat alternative. Its nutritional diversity, including high protein, starch, and phenolic content, makes it a suitable candidate for nutritional profiling, which is...
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Elsevier
2024-12-01
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| Series: | Applied Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772502224002178 |
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| author | Simardeep Kaur Naseeb Singh Ernieca L. Nongbri Mithra T Veerendra Kumar Verma Amit Kumar Tanay Joshi Jai Chand Rana Rakesh Bhardwaj Amritbir Riar |
| author_facet | Simardeep Kaur Naseeb Singh Ernieca L. Nongbri Mithra T Veerendra Kumar Verma Amit Kumar Tanay Joshi Jai Chand Rana Rakesh Bhardwaj Amritbir Riar |
| author_sort | Simardeep Kaur |
| collection | DOAJ |
| description | Lablab bean (Lablab purpureus L.) is a multipurpose crop, commonly used for food, feed, and fodder, and its potential as a plant-based meat alternative. Its nutritional diversity, including high protein, starch, and phenolic content, makes it a suitable candidate for nutritional profiling, which is essential for developing nutritionally enhanced varieties. Traditional methods for analyzing its nutritional parameters are labor-intensive, time-consuming, and expensive. This study employs Near-Infrared Reflectance Spectroscopy (NIRS) as a rapid, non-destructive alternative to evaluate 112 Lablab bean genotypes. We developed prediction models for starch, amylose, protein, fat, and phenols using a Modified Partial Least Squares (MPLS) approach, with spectral pre-processing using Standard Normal Variate (SNV) to remove scatter effects and Detrending (DT) to reduce baseline shifts and noise. The models were optimized for derivatives, gap selection, and smoothing, and evaluated using independent test data and key performance metrics including coefficient of determination (R²), bias, and Residual Prediction Deviation (RPD). The best-performing models were: starch (R² = 0.959, RPD = 4.57), amylose (R² = 0.737, RPD = 1.76), protein (R² = 0.911, RPD = 3.09), fat (R² = 0.894, RPD = 2.92), and phenols (R² = 0.816, RPD = 2.36). Statistical tests, including paired t-tests, correlation, and reliability analysis, confirmed the robustness of these models. This study presents a first report offering rapid, multi-trait assessment method for evaluating Lablab bean germplasm, demonstrating high predictive accuracy for pre-breeding practices. It has broad applications in developing nutritionally enhanced varieties, supporting plant-based protein alternatives, and optimizing food production processes to meet the growing demand for healthier, sustainable foods. |
| format | Article |
| id | doaj-art-b66d3fea08e545668cb818a178a2d680 |
| institution | DOAJ |
| issn | 2772-5022 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Food Research |
| spelling | doaj-art-b66d3fea08e545668cb818a178a2d6802025-08-20T02:49:00ZengElsevierApplied Food Research2772-50222024-12-014210060710.1016/j.afres.2024.100607Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) GermplasmSimardeep Kaur0Naseeb Singh1Ernieca L. Nongbri2Mithra T3Veerendra Kumar Verma4Amit Kumar5Tanay Joshi6Jai Chand Rana7Rakesh Bhardwaj8Amritbir Riar9ICAR- Research Complex for North Eastern Hill Region, Umiam, 793103, Meghalaya, IndiaICAR- Research Complex for North Eastern Hill Region, Umiam, 793103, Meghalaya, India; Corresponding authors.ICAR- Research Complex for North Eastern Hill Region, Umiam, 793103, Meghalaya, IndiaICAR- National Bureau of Plant Genetic Resources, New Delhi, 110012, IndiaICAR- Research Complex for North Eastern Hill Region, Umiam, 793103, Meghalaya, IndiaICAR- Research Complex for North Eastern Hill Region, Umiam, 793103, Meghalaya, IndiaDepartment of International Cooperation, Research Institute of Organic Agriculture FiBL, Frick, SwitzerlandThe Alliance of Bioversity International & CIAT- India, Office, New Delhi 110012, IndiaICAR- National Bureau of Plant Genetic Resources, New Delhi, 110012, India; Corresponding authors.Department of International Cooperation, Research Institute of Organic Agriculture FiBL, Frick, SwitzerlandLablab bean (Lablab purpureus L.) is a multipurpose crop, commonly used for food, feed, and fodder, and its potential as a plant-based meat alternative. Its nutritional diversity, including high protein, starch, and phenolic content, makes it a suitable candidate for nutritional profiling, which is essential for developing nutritionally enhanced varieties. Traditional methods for analyzing its nutritional parameters are labor-intensive, time-consuming, and expensive. This study employs Near-Infrared Reflectance Spectroscopy (NIRS) as a rapid, non-destructive alternative to evaluate 112 Lablab bean genotypes. We developed prediction models for starch, amylose, protein, fat, and phenols using a Modified Partial Least Squares (MPLS) approach, with spectral pre-processing using Standard Normal Variate (SNV) to remove scatter effects and Detrending (DT) to reduce baseline shifts and noise. The models were optimized for derivatives, gap selection, and smoothing, and evaluated using independent test data and key performance metrics including coefficient of determination (R²), bias, and Residual Prediction Deviation (RPD). The best-performing models were: starch (R² = 0.959, RPD = 4.57), amylose (R² = 0.737, RPD = 1.76), protein (R² = 0.911, RPD = 3.09), fat (R² = 0.894, RPD = 2.92), and phenols (R² = 0.816, RPD = 2.36). Statistical tests, including paired t-tests, correlation, and reliability analysis, confirmed the robustness of these models. This study presents a first report offering rapid, multi-trait assessment method for evaluating Lablab bean germplasm, demonstrating high predictive accuracy for pre-breeding practices. It has broad applications in developing nutritionally enhanced varieties, supporting plant-based protein alternatives, and optimizing food production processes to meet the growing demand for healthier, sustainable foods.http://www.sciencedirect.com/science/article/pii/S2772502224002178Lablab beanNIRSChemometricsMPLSScatter correctionProtein |
| spellingShingle | Simardeep Kaur Naseeb Singh Ernieca L. Nongbri Mithra T Veerendra Kumar Verma Amit Kumar Tanay Joshi Jai Chand Rana Rakesh Bhardwaj Amritbir Riar Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm Applied Food Research Lablab bean NIRS Chemometrics MPLS Scatter correction Protein |
| title | Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm |
| title_full | Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm |
| title_fullStr | Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm |
| title_full_unstemmed | Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm |
| title_short | Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm |
| title_sort | near infrared reflectance spectroscopy driven chemometric modeling for predicting key quality traits in lablab bean lablab purpureus l germplasm |
| topic | Lablab bean NIRS Chemometrics MPLS Scatter correction Protein |
| url | http://www.sciencedirect.com/science/article/pii/S2772502224002178 |
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