High-throughput screening of oil fingerprint using FT-IR coupled with chemometrics SCIE SCOPUS
DC Field | Value | Language |
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dc.contributor.author | Loh, Andrew | - |
dc.contributor.author | Soon, Zhi Yang | - |
dc.contributor.author | Ha, Sung Yong | - |
dc.contributor.author | Yim, Un Hyuk | - |
dc.date.accessioned | 2020-12-10T07:56:32Z | - |
dc.date.available | 2020-12-10T07:56:32Z | - |
dc.date.created | 2020-11-16 | - |
dc.date.issued | 2021-03-15 | - |
dc.identifier.issn | 0048-9697 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/38770 | - |
dc.description.abstract | An important element of the oil spill emergency response is the ability to rapidly identify the properties of oil spilled. Chemometrics provides large numbers of multivariate analysis tools that allow for more extensive use of data. Fourier transformed infrared spectroscopy coupled with classification and predictionmodels such as partial least square (PLS) and PLS-DA (discriminant analysis) allows the rapid identification of oil type and characteristics. By searching for the maximum covariance with the variables of interest, PLS allows the visualization of relations between samples and variables. The framework of this study is based on two main steps: The first is classification of oil and the second is prediction of physicochemical properties. Separated into fourmain categories: crude, light fuel, heavy fuel, and lubricant, spectrums of 92 oils were calibrated to predict the oil type and physicochemical properties of 26 oils. The predictability and robustness of the model was further validated using weathered oil. The classification and prediction models have accuracy of >95%. Most of the PLS models have root mean square error of calibration and prediction ranging from 0.10-3.07 and 0.3-2.8, respectively. External cross validations using weathered oils showed high prediction accuracy (relative standard deviations <5%). By increasing the number of oil type and samples, this approach is a promising method and can be included as part of the oil spill fingerprinting protocols. (C) 2020 Elsevier B.V. All rights reserved. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | ELSEVIER | - |
dc.subject | HEBEI-SPIRIT | - |
dc.title | High-throughput screening of oil fingerprint using FT-IR coupled with chemometrics | - |
dc.type | Article | - |
dc.citation.title | SCIENCE OF THE TOTAL ENVIRONMENT | - |
dc.citation.volume | 760 | - |
dc.contributor.alternativeName | Andrew | - |
dc.contributor.alternativeName | Soon Zhi | - |
dc.contributor.alternativeName | 하성용 | - |
dc.contributor.alternativeName | 임운혁 | - |
dc.identifier.bibliographicCitation | SCIENCE OF THE TOTAL ENVIRONMENT, v.760 | - |
dc.identifier.doi | 10.1016/j.scitotenv.2020.143354 | - |
dc.identifier.scopusid | 2-s2.0-85095789188 | - |
dc.identifier.wosid | 000607779400035 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordPlus | Fourier transformed infrared spectroscopy | - |
dc.subject.keywordPlus | High throughput screening | - |
dc.subject.keywordPlus | Multi variate analysis | - |
dc.subject.keywordPlus | Oil spill emergencies | - |
dc.subject.keywordPlus | Partial least square (PLS) | - |
dc.subject.keywordPlus | Relative standard deviations | - |
dc.subject.keywordPlus | Root mean square error of calibrations | - |
dc.subject.keywordPlus | Predictive analytics | - |
dc.subject.keywordPlus | Physicochemical properties | - |
dc.subject.keywordPlus | Fingerprinting protocol | - |
dc.subject.keywordPlus | Discriminant analysis | - |
dc.subject.keywordPlus | Drug products | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Infrared spectroscopy | - |
dc.subject.keywordPlus | Mean square error | - |
dc.subject.keywordPlus | Multivariant analysis | - |
dc.subject.keywordPlus | Oil spills | - |
dc.subject.keywordAuthor | Fourier transformed infrared spectroscopy | - |
dc.subject.keywordAuthor | PLS | - |
dc.subject.keywordAuthor | PLS-DA | - |
dc.subject.keywordAuthor | Oil type | - |
dc.subject.keywordAuthor | Physicochemical property | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |