Handheld UV fluorescence spectrophotometer device for the classification and analysis of petroleum oil samples SCIE SCOPUS

Cited 4 time in WEB OF SCIENCE Cited 5 time in Scopus
Title
Handheld UV fluorescence spectrophotometer device for the classification and analysis of petroleum oil samples
Author(s)
Bills M.V.; Loh A.; Sosnowski K.; Nguyen B.T.; Ha S.Y.; Yim U.H.; Yoon J.-Y.
KIOST Author(s)
Loh, Andrew(Loh, Andrew)Ha, Sung Yong(하성용)Yim, Un Hyuk(임운혁)
Publication Year
2020-07
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.
ISSN
0956-5663
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/38611
DOI
10.1016/j.bios.2020.112193
Bibliographic Citation
Biosensors and Bioelectronics, v.159, 2020
Publisher
Pergamon Press Ltd.
Keywords
Fluorescence spectroscopy; Oil spill; Raspberry Pi; Saturate, aromatic, resin, and asphaltene contents; Support vector machine; Ultraviolet light emitting diode
Type
Article
Language
English
Document Type
Article
Publisher
Pergamon Press Ltd.
Related Researcher
Research Interests

Oil Spill Environmental Forensics,Persistent Organic Pollutants,Marine Aerosol,유류오염 환경법과학,지속성유기오염물질,해양 에어로졸

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