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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/47088
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Somsubhra Chakraborty, David C. Weindorf, Sathi Paul, Bhaswati Ghosh, Bin Li, Md. NasimAli, Rakesh Kumar Ghosh, D.P. Ray, K. Majumdar | en_US |
dc.date.accessioned | 2021-06-01T07:15:35Z | - |
dc.date.available | 2021-06-01T07:15:35Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/47088 | - |
dc.description | Not Available | en_US |
dc.description.abstract | Soil lead (Pb) contamination by anthropogenic and industrial activities is a problemof global concern. In this research the possibility to adapt mid infrared-diffuse reflectance infrared Fourier transform spectroscopy (MIRDRIFTS) approach for the quantitative estimation of Pb in polluted soils was explored. One hundred soil samples were collected from an urban landfill agricultural site and scanned by MIR-DRIFTS. The raw reflectance spectra were preprocessed using four spectral transformations for predicting soil Pb contamination using three multivariate algorithms. Partial least squares regression using Savitzky–Golay (SG) first derivative spectra (RPD=3.05) outperformed principal component regression models. The artificial neural networks-SG model using an independent validation set produced satisfactory generalization capability (RPD = 2.01). Thus, the combination of MIR-DRIFTS and multivariate models can reduce chemical analysis frequency for soil pollution monitoring, substantially reducing labor and analytical cost. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | Artificial neural networks, Diffuse reflectance infrared, Fourier transform spectroscopy, Mid infrared, Partial least squares regression, Principal component regression | en_US |
dc.title | Diffuse reflectance spectroscopy for monitoring lead in landfill agricultural soils of India | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Journal | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Geoderma Regional | en_US |
dc.publication.volumeno | 5 | en_US |
dc.publication.pagenumber | 77-85 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | http://dx.doi.org/10.1016/j.geodrs.2015.04.004 | en_US |
dc.publication.authorAffiliation | IIT, Kharagpur | en_US |
dc.publication.authorAffiliation | ICAR::National Institute of Research on Jute and Allied Fibre Technology | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | 8.67 | en_US |
dc.publication.naasrating | 8.67 | - |
Appears in Collections: | AEng-NINFET-Publication |
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