Experimental study of fluorescence spectra of oil pollutions on the terrain surface excited at 355 nm

Fedotov, Yu. V.; Kravtsov, D A; Белов, М. Л.; Городничев, В. А.

doi:10.1117/12.2502049

Cited by 2 publications

(1 citation statement)

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“…Common laser induced fluorescence detection techniques include fluorescence intensity analysis, fluorescence spectrum analysis, fluorescence lifetime analysis and so on according to different fluorescence parameters. The fluorescence intensity is the most direct physical quantity and has been widely used to produce the fluorescence of laser-induced substances [12]. Furthermore, its application for the analysis of species for classification and recognition [13] has gained considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Recognition of multi-component compounds based on occurrence time of secondary crest in the fluorescence lifetime attenuation curve

Min,

Ningchao,

Bingqi

et al. 2024

Meas. Sci. Technol.

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Fluorescence lifetime is the main characteristic parameter of fluorescence. It is a widely used to draw fluorescence lifetime attenuation curves and to fit fluorescence lifetime parameters by using gated detection methods to identify the species of substances. However, the fluorescence attenuation of each fluorophore in a multi-component compound interferes with one another, affecting the accuracy of identification. In this paper, we propose a method to accurately identify substances by using the occurrence time of the secondary crest of the fluorescence lifetime attenuation curve based on the principle of gated detection to measure the fluorescence lifetime. Furthermore, we design a fluorescence lifetime imaging measurement system and select the same areas of interest in the images for analysis and comparison. The average lifetime of the fluorescence and the occurrence time of the secondary crest are considered as the characteristic parameters. We use five commercially available motor engine oils as the experimental samples and compare the recognition performance of different kernel functions based on a support vector machine (SVM). The radial basis kernel function (RBF) presents the best performance in terms of recognition accuracy and speed. The recognition rates of the SVM model with the average fluorescence lifetime and the occurrence time of the secondary crest in the attenuation curve of the fluorescence lifetime as a feature vector are 76.24% and 74.65%, respectively. The recognition rate of the SVM model which combines them as feature vectors reaches 91.88%. The experimental results demonstrate that the occurrence time of the secondary crest in the attenuation curve of the fluorescence lifetime can be employed as the basis for substance identification in the analysis of the fluorescence characteristics of multi-component compounds, whose recognition accuracy is similar to the average fluorescence lifetime parameter. Moreover, the occurrence time of the secondary crest of the fluorescence lifetime attenuation curve can be implemented to identify multi-component compounds when it is used as a characteristic parameter.

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Section: Introductionmentioning

confidence: 99%