TY - CONF A1 - Wohlschläger, Maximilian A1 - Leiter, Nina A1 - Dietlmeier, Maximilian A1 - Löder, Martin G.J. A1 - Versen, Martin A1 - Laforsch, Christian T1 - Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials T2 - 2023 International Joint Conference on Neural Networks (IJCNN) N2 - 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. KW - Fluorescence KW - Microscopy KW - Plastics KW - FD-FLIM KW - Neural networks KW - Environment KW - Frequency-domain analysis KW - Gaussian analysis KW - MLP KW - Multilayer perceptrons KW - Random forests KW - RFC Y1 - 2023 UR - https://opus4.kobv.de/opus4-rosenheim/frontdoor/index/index/docId/2441 SP - 1 EP - 9 ER -