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Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials

  • Previous research on identifying plastic types and differentiating plastics from environmental material is promising by utilizing the specific fluorescence lifetime, but the evaluation still has to be automated. Therefore, an automated Gaussian analysis is developed for evaluating frequency-domain fluorescence lifetime images of plastics and environmental materials. Furthermore, we applied a “Multilayer Perceptron” and “Random Forest Classifier” to the data resulting from the Gaussian analysis of the frequency domain fluorescence lifetime imaging microscopy data. The classification results show high F1-scores, whereby the best “Multilayer Perceptron” and “Random Forest Classifier” achieved an F1-score of 90%. Thus, identifying and differentiating plastics and environmental materials is possible by applying a “Multilayer Perceptron” or “Random Forest Classifier” to the Gaussian-analyzed imaged fluorescence lifetime data.

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Author:Maximilian Wohlschläger, Nina Leiter, Maximilian Dietlmeier, Martin G.J. Löder, Martin Versen, Christian Laforsch
DOI:https://doi.org/10.1109/IJCNN54540.2023.10191054
Parent Title (English):2023 International Joint Conference on Neural Networks (IJCNN)
Document Type:Conference Proceeding
Language:English
Publication Year:2023
Tag:Environment; FD-FLIM; Fluorescence; Frequency-domain analysis; Gaussian analysis; MLP; Microscopy; Multilayer perceptrons; Neural networks; Plastics; RFC; Random forests
First Page:1
Last Page:9