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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/74995
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Bhardwaj, R*., John, R., Jeyaseelan, C., Bollinedi, H., Singh, N., Gd, H., Nath, D., Arya, M., Sharma, D., Singh, S. and John, J | en_US |
dc.date.accessioned | 2022-11-10T11:42:03Z | - |
dc.date.available | 2022-11-10T11:42:03Z | - |
dc.date.issued | 2022-08-04 | - |
dc.identifier.citation | John R, Bhardwaj R, Jeyaseelan C, Bollinedi H, Singh N, Harish GD, Singh R, Nath DJ, Arya M, Sharma D, Singh S, John K J, Latha M, Rana JC, Ahlawat SP and Kumar A (2022) Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice. Front. Nutr. 2022:946255. doi: 10.3389/fnut.2022.946255 | en_US |
dc.identifier.other | doi: 10.3389/fnut.2022.946255 | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/74995 | - |
dc.description | Not Available | en_US |
dc.description.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 R2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant. | en_US |
dc.description.sponsorship | This work was funded by the support from two projects, namely, Global Environment Facility (GEF) of the United Nations Environment Program (UNEP) funded project LoA No. L19INDIA173 Dated 01.06.2019 “Mainstreaming agricultural biodiversity conservation and utilization in the agricultural sector to ensure ecosystem services and reduce vulnerability” and Department of Biotechnology (DBT) No. BT/Ag/Network/Rice/2019-20 Dated: 05.03.2020 – Government of India funded project “Mainstreaming rice landraces diversity in varietal development through genome-wide association studies: A model for large-scale utilization of gene bank collections of rice”. | en_US |
dc.language.iso | English | en_US |
dc.publisher | Frontiers | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | rice, nutrient profile, nirs prediction model, diversity | en_US |
dc.title | Germplasm Variability Assisted NIRS Chemometrics to Develop Multi-Trait Robust Prediction Models in Rice | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Frontiers in Nutrition | en_US |
dc.publication.volumeno | Not Available | en_US |
dc.publication.pagenumber | Not Available | en_US |
dc.publication.divisionUnit | Division of Germplasm Evaluation | en_US |
dc.publication.sourceUrl | https://doi.org/10.3389/fnut.2022.946255 | en_US |
dc.publication.authorAffiliation | ICAR::National Bureau of Plant Genetics Resources | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Research Institute | en_US |
dc.publication.authorAffiliation | Amity University | en_US |
dc.publication.authorAffiliation | Alliance of Bioversity International and CIAT | en_US |
dc.publication.authorAffiliation | Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh | en_US |
dc.publication.authorAffiliation | Assam Agricultural University, Jorhat, Assam | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.journaltype | NAAS rated | en_US |
dc.publication.naasrating | 12.58 | en_US |
dc.publication.impactfactor | 6.58 | en_US |
Appears in Collections: | CS-NBPGR-Publication |
Files in This Item:
File | Description | Size | Format | |
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Rice NIRS.pdf | 1.92 MB | Adobe PDF | View/Open |
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