ArticleFrontiers in nutrition2022
Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice.
Article in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Multispectral imaging for zeaxanthin content in the exocarp of chili peppers.Food chemistry: X · 2026Article
- Genome-wide association studies for identification of QTLs and key candidate genes to improve grain quality in rice (Food chemistry. Molecular sciences · 2025Article
- NIRS chemometrics for rapid nutritional profiling of vegetable pea (Pisum sativum L.) germplasm.Scientific reports · 2025Article
- Nutritional profiling of horse gram through NIRS-based multi-trait prediction modelling.Scientific reports · 2025Article
- Biochemical and agro-morphological traits-based mining for Malt Barley Germplasm.Frontiers in nutrition · 2025Article
- Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology.Sensors (Basel, Switzerland) · 2023Article
- Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm.Frontiers in nutrition · 2023Article
- Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm.Frontiers in nutrition · 2022Article
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16 authors.
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Abstract
Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with
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