Fluorescence Imaging Methodology for Oil‐in‐Water Concentration Measurements

Köpplin, Jessica; Bednarz, Lena; Hagemeier, Thomas; Thévenin, Dominique

doi:10.1002/ceat.202100023

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Normally speaking, spectral data are statistically PLS fitted to the gradients vs. intensity peaks at contain wavelengths as the previous report [24], but it is discovered that there is non-linear and greater dispersed when the oil concentration is greater than 10 mg/L and the peaks are not constant when the oil becomes thicker. Finally, we chose a RANSAC algorithm for the prediction of oil spill investigation.…”

Section: Principles Of the Ransac Algorithmmentioning

confidence: 98%

Rapid Investigation of Oil Pollution in Water-Combined Induced Fluorescence and Random Sample Consensus Algorithm

Wu,

Wang,

Zhao

2024

Sustainability

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The global issue of oil spreading in water poses a significant environmental challenge, emphasizing the critical need for the accurate determination and monitoring of oil content in aquatic environments to ensure sustainable development of the environment. However, the complexity arises from challenges such as oil dispersion, clustering, and non-uniform distribution, making it difficult to obtain real-time oil concentration data. This paper introduces a sophisticated system for acquiring induced fluorescence spectra specifically designed for the quantitative analysis of oil pollutants. The paper involved measuring the fluorescence spectra across 20 concentration gradients (ranging from 0 to 1000 mg/L) for four distinct oil samples: 92# Gasoline, Mobil Motor Oil 20w-40, Shell 10w-40 engine oil, and Soybean Oil. The research focused on establishing a relationship model between relative fluorescence intensity and concentration, determined at the optimal excitation wavelength, utilizing the segmented Random Sample Consensus (RANSAC) algorithm. Evaluation metrics, including standard addition recovery, average recovery, relative error, and average relative error, were employed to assess the accuracy of the proposed model. The experimental findings suggest that the average recovery rates for the four samples ranged between 99.61% and 101.15%, with the average relative errors falling within the range of 2.04% to 3.14%. These results underscore the accuracy and efficacy of the detection methodology presented in this paper. Importantly, this accuracy extends to scenarios involving heavier oil pollution. This paper exhibits exceptional sensitivity, enabling precise detection of diverse oil spills within the concentration range of 0~1000 mg/L in water bodies, offering valuable insights for water quality monitoring and sustainable development of the environment.

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Section: Principles Of the Ransac Algorithmmentioning

confidence: 98%