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Open AccessArticle

Fluorescence Hyperspectral Imaging of Oil Samples and Its Quantitative Applications in Component Analysis and Thickness Estimation

National Engineering Research Center of Optical Instrumentation, Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, China
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Author to whom correspondence should be addressed.
These authors contribute equally to this work.
Sensors 2018, 18(12), 4415; https://doi.org/10.3390/s18124415
Received: 11 November 2018 / Revised: 8 December 2018 / Accepted: 8 December 2018 / Published: 13 December 2018
(This article belongs to the Special Issue Sensing in Oil and Gas Applications)
The fast response and analysis of oil spill accidents is important but remains challenging. Here, a compact fluorescence hyperspectral system based on a grating-prism structure able to perform component analysis of oil as well as make a quantitative estimation of oil film thickness is developed. The spectrometer spectral range is 366–814 nm with a spectral resolution of 1 nm. The feasibility of the spectrometer system is demonstrated by determining the composition of three types of crude oil and various mixtures of them. The relationship between the oil film thickness and the fluorescent hyperspectral intensity is furthermore investigated and found to be linear, which demonstrates the feasibility of using the fluorescence data to quantitatively measure oil film thickness. Capable of oil identification, distribution analysis, and oil film thickness detection, the fluorescence hyperspectral imaging system presented is promising for use during oil spill accidents by mounting it on, e.g., an unmanned aerial vehicle. View Full-Text
Keywords: fluorescence hyperspectral imaging; oil detection; principal component analysis; K-means clustering fluorescence hyperspectral imaging; oil detection; principal component analysis; K-means clustering
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MDPI and ACS Style

Jiang, W.; Li, J.; Yao, X.; Forsberg, E.; He, S. Fluorescence Hyperspectral Imaging of Oil Samples and Its Quantitative Applications in Component Analysis and Thickness Estimation. Sensors 2018, 18, 4415.

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