TY - CHAP A1 - Püschel, M. A1 - Crämer, P. A1 - Kipfelsberger, S. A1 - Versen, M. T1 - Embedded Sensor System mit EtherCAT zur Bestimmung der Raumluftqualität T2 - Tagungsband 4. Symposium Elektronik und Systemintegration, Landshut 2024 KW - Embedded Sensor System Y1 - 2024 ER - TY - JOUR A1 - Wohlschläger, Maximilian A1 - Versen, Martin A1 - Löder, Martin G. J. A1 - Laforsch, Christian T1 - Identification of different plastic types and natural materials from terrestrial environments using fluorescence lifetime imaging microscopy. JF - Analytical and Bioanalytical Chemistry N2 - Environmental pollution by plastics is a global issue of increasing concern. However, microplastic analysis in complex environmental matrices, such as soil samples, remains an analytical challenge. Destructive mass-based methods for microplastic analysis do not determine plastics’ shape and size, which are essential parameters for reliable ecological risk assessment. By contrast, nondestructive particle-based methods produce such data but require elaborate, time-consuming sample preparation. Thus, time-efficient and reliable methods for microplastic analysis are needed. The present study explored the potential of frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) for rapidly and reliably identifying as well as differentiating plastics and natural materials from terrestrial environments. We investigated the fluorescence spectra of ten natural materials from terrestrial environments, tire wear particles, and eleven different transparent plastic granulates <5 mm to determine the optimal excitation wavelength for identification and differentiation via FD-FLIM under laboratory conditions. Our comparison of different excitation wavelengths showed that 445 nm excitation exhibited the highest fluorescence intensities. 445 nm excitation was also superior for identifying plastic types and distinguishing them from natural materials from terrestrial environments with a high probability using FD-FLIM. We could demonstrate that FD-FLIM analysis has the potential to contribute to a streamlined and time-efficient direct analysis of microplastic contamination. However, further investigations on size-, shape-, color-, and material-type detection limitations are necessary to evaluate if the direct identification of terrestrial environmental samples of relatively low complexity, such as a surface inspection soil, is possible. KW - FD-FLIM KW - Fluorescence lifetime KW - Microplastic contamination KW - Microplastic in soils KW - Plastic identification KW - Terrestrial pollution Y1 - 2024 U6 - https://doi.org/10.1007/s00216-024-05305-w ER - TY - JOUR A1 - Wohlschläger, Maximilian A1 - Versen, Martin A1 - Löder, Martin G. J. A1 - Laforsch, Christian T1 - A promising method for fast identification of microplastic particles in environmental samples: A pilot study using fluorescence lifetime imaging microscopy JF - Heliyon N2 - Microplastic pollution of the environment has been extensively studied, with recent studies focusing on the prevalence of microplastics in the environment and their effects on various organisms. Identification methods that simplify the extraction and analysis process to the point where the extraction can be omitted are being investigated, thus enabling the direct identification of microplastic particles. Currently, microplastic samples from environmental matrices can only be identified using time-consuming extraction, sample processing, and analytical methods. Various spectroscopic methods are currently employed, such as micro Fourier-transform infrared, attenuated total reflectance, and micro Raman spectroscopy. However, microplastics in environmental matrices cannot be directly identified using these spectroscopic methods. Investigations using frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) to identify and differentiate plastics from environmental materials have yielded promising results for directly identifying microplastics in an environmental matrix. Herein, two artificially prepared environmental matrices that included natural soil, grass, wood, and high-density polyethylene were investigated using FD-FLIM. Our first results showed that we successfully identified one plastic type in the two artificially prepared matrices using FD-FLIM. However, further research must be conducted to improve the FD-FLIM method and explore its limitations for directly identifying microplastics in environmental samples. KW - FD-FLIM KW - Fluorescence lifetime KW - Environmental science KW - Fluorescence microscopy KW - Material identification KW - Microplastics Y1 - 2024 U6 - https://doi.org/10.1016/j.heliyon.2024.e25133 VL - 10 IS - 3 ER - TY - CHAP A1 - Püschel, B. Eng M. A1 - Crämer, Dipl-Ing A1 - Kipfelsberger, S. A1 - Versen, Prof Dr-Ing M. T1 - Embedded Sensor System mit EtherCAT für Messungen des Raumklimas T2 - Tagungsband AALE 2024 Y1 - 2024 U6 - https://doi.org/10.33968/2024.59 SP - 291 EP - 296 ER - TY - CHAP 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 U6 - https://doi.org/10.1109/IJCNN54540.2023.10191054 SP - 1 EP - 9 ER - TY - CHAP A1 - Wohlschläger, Maximilian A1 - Khan, Yamna A1 - Leiter, Nina A1 - Versen, Martin A1 - Löder, Martin A1 - Laforsch, Christian T1 - Development of a BLOB-detection algorithm based on DoG to detect Plastic in an environmental matrix using FD-FLIM T2 - Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES) N2 - The direct identification of plastics in an environmental matrix is heavily researched. We successfully developed a BLOB-detection algorithm based on differences of Gaussians to identify HDPE particles in an artificial environmental matrix using FD-FLIM. KW - Diode lasers KW - Fluorescence lifetime imaging KW - Phase shift KW - Neural networks KW - Optical filters KW - Spatial resolution Y1 - 2023 U6 - https://doi.org/10.1364/ES.2023.EW4E.4 ER - TY - CHAP A1 - Wohlschläger, Maximilian A1 - Khan, Yamna A1 - Leiter, Nina A1 - Versen, Martin A1 - Löder, Martin A1 - Laforsch, Christian T1 - Combining BLOB-Detection and MLP to Detect and Identify Plastics in an Environmental Matrix T2 - 2023 IEEE Sensors Applications Symposium (SAS) N2 - Environmental pollution by plastics is an increasing problem. However, state-of-the-art methods have significant disadvantages in detecting and identifying plastics directly in an environmental matrix. In this study, we propose a blob detection algorithm in combination with a neural network for fast and automated identification of plastics and non-plastics in a single fluorescence lifetime image. Therefore an artificial environmental matrix is prepared that contains soil, grass, spruce and HDPE (high density polyethylene) particles. Several FD-FLIM (frequency domain fluorescence lifetime imaging microscopy) images are taken, and the detection algorithm and the neural network are applied. We successfully demonstrated the suitability of the thresholding algorithm and the binary classification of the HDPE particles directly in the environmental matrix. KW - Fluorescence KW - Plastics KW - FD-FLIM KW - Neural networks KW - MLP KW - blob detection KW - Classification algorithms KW - Dogs KW - fluorescence lifetime KW - plastics identification KW - Soil KW - Thresholding (Imaging) Y1 - 2023 U6 - https://doi.org/10.1109/SAS58821.2023.10254171 SP - 1 EP - 5 ER - TY - CHAP A1 - Schwarz, Jonas A1 - Wohlschläger, Maximilian A1 - Leiter, Nina A1 - Auer, Veronika A1 - Risse, Michael A1 - Versen, Martin T1 - Frequency Domain Fluorescence Lifetime Imaging Microscopy (FD-FLIM) analysis of Quercus robur samples for origin differentiation purposes T2 - Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES) N2 - Increasing demand for wood products requires methods to determine its harvest origin and ensure sustainable and legal sourcing. In 15 out of 21 cases, the origin of Quercus robur was differentiable in FD-FLIM studies. KW - Fluorescence lifetime imaging KW - Phase shift KW - Phase modulation KW - Laser sources KW - Bandpass filters KW - Frequency modulation Y1 - 2023 U6 - https://doi.org/10.1364/AIS.2023.JTu4A.10 ER - TY - JOUR A1 - Maniyattu, Georgekutty Jose A1 - Geegy, Eldho A1 - Wohlschläger, Maximilian A1 - Leiter, Nina A1 - Versen, Martin A1 - Laforsch, Christian T1 - Multilayer Perceptron Development to Identify Plastics Using Fluorescence Lifetime Imaging Microscopy JF - EDFA Technical Articles N2 - Existing plastic analysis techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy are problematic because samples must be anhydrous and identification can be hindered by additives. This article describes a new approach that has been successfully demonstrated in which plastics can be classified by neural networks that are trained, validated, and tested by frequency domain fluorescence lifetime imaging microscopy measurements. Y1 - 2023 U6 - https://doi.org/10.31399/asm.edfa.2023-3.p031 VL - 25 IS - 3 SP - 31 EP - 37 ER - TY - CHAP A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Dietlmeier, Maximilian A1 - Versen, Martin A1 - Löder, Martin A1 - Laforsch, Christian T1 - Comparative Analysis of Fluorescence Properties of Post-Consumer Wood Using FD-FLIM T2 - 2023 IEEE Sensors Applications Symposium (SAS) N2 - The FD-FLIM technique has a high potential for automated post-consumer wood sorting. A problem of analysing post-consumer wood fluorescence properties is the uncertainty of the post-consumer wood category as the origin of the samples are unknown. In this study, the fluorescence properties of actual post-consumer wood is compared with prepared wood samples. The post-consumer wood samples display slightly different fluorescence intensities and lifetimes due to environmental influences and a higher sample diversity. For improved training of evaluation algorithms for post-consumer wood sorting, the prepared sample set should be extended or the post-consumer wood should be additionally analysed in the laboratory. KW - fluorescence KW - Fluorescence KW - Neural networks KW - fluorescence properties KW - Moisture KW - post-consumer wood KW - Sensors KW - Training KW - Uncertainty KW - Visualization KW - waste wood Y1 - 2023 U6 - https://doi.org/10.1109/SAS58821.2023.10254052 SP - 1 EP - 6 ER -