Handheld UV fluorescence spectrophotometer device for the classification and analysis of petroleum oil samples SCIE SCOPUS
DC Field | Value | Language |
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dc.contributor.author | Bills M.V. | - |
dc.contributor.author | Loh A. | - |
dc.contributor.author | Sosnowski K. | - |
dc.contributor.author | Nguyen B.T. | - |
dc.contributor.author | Ha S.Y. | - |
dc.contributor.author | Yim U.H. | - |
dc.contributor.author | Yoon J.-Y. | - |
dc.date.accessioned | 2020-12-10T07:47:35Z | - |
dc.date.available | 2020-12-10T07:47:35Z | - |
dc.date.created | 2020-05-13 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.issn | 0956-5663 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/38611 | - |
dc.description.abstract | Oil spills can be environmentally devastating and result in unintended economic and social consequences. An important element of the concerted effort to respond to spills includes the ability to rapidly classify and characterize oil spill samples, preferably on-site. An easy-to-use, handheld sensor is developed and demonstrated in this work, capable of classifying oil spills rapidly on-site. Our device uses the computational power and affordability of a Raspberry Pi microcontroller and a Pi camera, coupled with three ultraviolet light emitting diodes (UV-LEDs), a diffraction grating, and collimation slit, in order to collect a large data set of UV fluorescence fingerprints from various oil samples. Based on a 160-sample (in 5x replicates each with slightly varied dilutions) database this platform is able to classify oil samples into four broad categories: crude oil, heavy fuel oil, light fuel oil, and lubricating oil. The device uses principal component analysis (PCA) to reduce spectral dimensionality (1203 features) and support vector machine (SVM) for classification with 95% accuracy. The device is also able to predict some physiochemical properties, specifically saturate, aromatic, resin, and asphaltene percentages (SARA) based off linear relationships between different principal components (PCs) and the percentages of these residues. Sample preparation for our device is also straightforward and appropriate for field deployment, requiring little more than a Pasteur pipette and not being affected by dilution factors. These properties make our device a valuable field-deployable tool for oil sample analysis. © 2020 Elsevier B.V. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | Pergamon Press Ltd. | - |
dc.title | Handheld UV fluorescence spectrophotometer device for the classification and analysis of petroleum oil samples | - |
dc.type | Article | - |
dc.citation.title | Biosensors and Bioelectronics | - |
dc.citation.volume | 159 | - |
dc.contributor.alternativeName | Andrew | - |
dc.contributor.alternativeName | 하성용 | - |
dc.contributor.alternativeName | 임운혁 | - |
dc.identifier.bibliographicCitation | Biosensors and Bioelectronics, v.159 | - |
dc.identifier.doi | 10.1016/j.bios.2020.112193 | - |
dc.identifier.scopusid | 2-s2.0-85083671828 | - |
dc.identifier.wosid | 000531078700010 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordPlus | Classification (of information) | - |
dc.subject.keywordPlus | Coal liquefaction | - |
dc.subject.keywordPlus | Fluorescence | - |
dc.subject.keywordPlus | Fuel oils | - |
dc.subject.keywordPlus | Oil spills | - |
dc.subject.keywordPlus | Petroleum analysis | - |
dc.subject.keywordPlus | Principal component analysis | - |
dc.subject.keywordPlus | Support vector machines | - |
dc.subject.keywordPlus | Computational power | - |
dc.subject.keywordPlus | Hand-held sensor | - |
dc.subject.keywordPlus | Linear relationships | - |
dc.subject.keywordPlus | Physio-chemical properties | - |
dc.subject.keywordPlus | Principal Components | - |
dc.subject.keywordPlus | Sample preparation | - |
dc.subject.keywordPlus | Social consequences | - |
dc.subject.keywordPlus | Ultraviolet light emitting diodes | - |
dc.subject.keywordPlus | Oil field equipment | - |
dc.subject.keywordPlus | fuel oil | - |
dc.subject.keywordPlus | lubricating agent | - |
dc.subject.keywordPlus | petroleum | - |
dc.subject.keywordPlus | Article | - |
dc.subject.keywordPlus | calibration | - |
dc.subject.keywordPlus | controlled study | - |
dc.subject.keywordPlus | dilution | - |
dc.subject.keywordPlus | equipment design | - |
dc.subject.keywordPlus | measurement accuracy | - |
dc.subject.keywordPlus | oil spill | - |
dc.subject.keywordPlus | physical chemistry | - |
dc.subject.keywordPlus | prediction | - |
dc.subject.keywordPlus | principal component analysis | - |
dc.subject.keywordPlus | reproducibility | - |
dc.subject.keywordPlus | spectrofluorometry | - |
dc.subject.keywordPlus | support vector machine | - |
dc.subject.keywordAuthor | Fluorescence spectroscopy | - |
dc.subject.keywordAuthor | Oil spill | - |
dc.subject.keywordAuthor | Raspberry Pi | - |
dc.subject.keywordAuthor | Saturate, aromatic, resin, and asphaltene contents | - |
dc.subject.keywordAuthor | Support vector machine | - |
dc.subject.keywordAuthor | Ultraviolet light emitting diode | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |