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 - 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 - 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 - 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 - 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 - Versen, Martin A1 - Wohlschläger, Maximilian A1 - Langhals, Heinz A1 - Laforsch, Christian T1 - The detection of organic polymers as contaminants in foodstuffs by means of the fluorescence decay of their auto fluorescence JF - Food and Humanity N2 - Products such as food can become contaminated during their manufacture or afterwards. Depending on the type of substance causing the contamination, these contaminants can be harmful to health and difficult to detect by visible inspection. The suitability of fluorescence decay and FD-FLIM for the detection of plastics contamination in foodstuffs is demonstrated. Therefore, a procedure for the detection of contaminating organic polymers (plastics) in processed meat such as salami by means of the fluorescence decay time of auto fluorescence is described. The auto fluorescence of processed meat was found to decay according to first order with a typical time constant of about 2 ns, whereas the time constant of significant polymers for the processing of meat is generally appreciably higher (2.5 ns – 5.5 ns depending on the polymer). As a consequence, contaminating organic polymers can not only be globally detected by means of the fluorescence decay but also localised in two-dimensional imaging. The present study reports a high potential of FD-FLIM for rapidly identifying and differentiating different plastics on and in different foodstuffs. The method allows an improved quality control of foodstuffs. KW - Fluorescence KW - Decay constant KW - Foodstuff KW - Imaging KW - Quality control KW - Organic polymers Y1 - 2024 U6 - https://doi.org/10.1016/j.foohum.2024.100363 VL - 3 ER - TY - CHAP A1 - Heitzmann, Sebastian A1 - Wohlschläger, Maximilian A1 - Leiter, Nina A1 - Löder, Martin G. J. A1 - Versen, Martin A1 - Laforsch, Christian T1 - Classification of Foods and Plastics using FD-FLIM and Neural Networks T2 - 2024 IEEE Sensors Applications Symposium (SAS) N2 - Plastics and foods can be differentiated by their material characteristic fluorescence properties, especially their fluorescence lifetimes. An areal measurement of fluorescent lifetimes of these materials can be done using Frequency-Domain Fluorescence Lifetime Imaging Microscopy (FD-FLIM). Up until now, most plastic detection is done using NIR or X-ray, while most applications of FD-FLIM are in biomedicalfields. The application of FD-FLIM in a food safety setting presents a promising approach to the detection of plastic contaminants. A Multilayer Perceptron (MLP) based neural network is developed to reliably identify the presence of plastic in a food/plastic sample via FD-FLIM. Features like the mean, median, standard deviation, variance, range, and interquartile range are calculated from the intensity image, the phase shift and modulation index along with the according phase- and modulation-dependent fluorescence lifetimes from the FD-FLIM data. For training, test and validation, a total of 3520 FD-FLIM measurements have been taken at 445nm excitation of sixteen samples with the labels food and plastic. To rank the performance of the 3888 trained networks, Fl-score, accuracy, precision, and recall are used as metrics. The best performing network reaches a Fl-score of 98.86% proving that a differentiation of foods and plastics using a MLP classification based on FD- FLIM data is possible with a low error rate. KW - fluorescence KW - foods KW - FLIM KW - MLP KW - neural networks Y1 - 2024 U6 - https://doi.org/10.1109/SAS60918.2024.10636453 SP - 1 EP - 6 PB - IEEE ER - TY - GEN A1 - Heitzmann, Sebastian A1 - Kallweit, Stephan A1 - Brunn, André A1 - Holst, Gerhard A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin T1 - Erweiterung eines FD-FLIM Messsystems durch MQTT Anbindung eines Roboters zur automatischen Sortierung von Altholzklassen T2 - Tagungsband AALE 2025: Menschenzentrierte Automation im digitalen Zeitalter N2 - Ziel des Projektes Fluoreszenz ID von Altholz (FrIDAH)5 ist die Entwicklung eines Demonstrators gewesen, welcher die automatisierte Sortierung von Altholzproben gemäß der Altholzverordnung unter Verwendung der Messung von Fluoreszenzabklingzeiten ermöglicht. In diesem Beitrag werden der entwickelte Messaufbau, die Software, das Automatisierungssystem, sowie der Klassifikator vorgestellt. Die Ergebnisse zeigen, dass die verwendete Technologie zur zuverlässigen Klassifikation von Altholz geeignet ist und für die automatisierte Sortierung angewendet werden kann. Y1 - 2025 U6 - https://doi.org/10.33968/2025.16 SP - 147 EP - 156 ER - TY - JOUR A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin A1 - Harter, Sonja D. A1 - Kießlich, Tina A1 - Lederer, Franziska A1 - Clauß, Stefanie A1 - Schlosser, Dietmar A1 - Armanu, Emanuel Gheorghita A1 - Eberlein, Christian A1 - Heipieper, Hermann J. A1 - Löder, Martin G. J. A1 - Laforsch, Christian T1 - Effects of defined organic layers on the fluorescence lifetime of plastic materials JF - Analytical and Bioanalytical Chemistry N2 - Plastics have become an integral part of modern life, and linked to that fact, the demand for and global production of plastics are still increasing. However, the environmental pollution caused by plastics has reached unprecedented levels. The accumulation of small plastic fragments—microplastics and nanoplastics—potentially threatens organisms, ecosystems, and human health. Researchers commonly employ non-destructive analytical methods to assess the presence and characteristics of microplastic particles in environmental samples. However, these techniques require extensive sample preparation, which represents a significant limitation and hinders a direct on-site analysis. In this context, previous investigations showed the potential of fluorescence lifetime imaging microscopy (FLIM) for fast and reliable identification of microplastics in an environmental matrix. However, since microplastics receive an environmental coating after entering nature, a challenge arises from organic contamination on the surface of microplastic particles. How this influences the fluorescence signal and the possibility of microplastic detection are unknown. To address this research gap, we exposed acrylonitrile butadiene styrene (ABS) and polyethylene terephthalate (PET) plastic samples to peptides, proteins, bacteria, and a filamentous fungus to induce organic contamination and mimic environmental conditions. We analyzed the fluorescence spectra and lifetimes of the samples using fluorescence spectroscopy and frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM), respectively. Our results demonstrate that reliably identifying and differentiating ABS and PET was possible via FD-FLIM, even in the presence of these biological contaminations. These findings highlight the potential of this technique as a valuable tool for environmental monitoring and plastic characterization, offering a rapid and efficient alternative to currently used analytical methods. Y1 - 2025 U6 - https://doi.org/10.1007/s00216-025-05888-y VL - 417 IS - 16 SP - 3651 EP - 3663 ER - TY - GEN A1 - Leiter, Nina A1 - Heitzmann, Sebastian A1 - Versen, Martin A1 - Wohlschläger, Maximilian A1 - Löder, Martin G.J. A1 - Laforsch, Christian T1 - Identification of Microplastic Contamination in Food using FD-FLIM T2 - 2025 IEEE Sensors Applications Symposium (SAS) N2 - Microplastics have emerged as a significant environmental concern, particularly due to their potential impact on food safety and human health. This study uses frequency-domain fluorescence lifetime imaging microscopy to investigate the presence and effects of microplastics in four food types—ham, honey, fish, and lettuce. Samples were prepared with known quantities of high-density polyethylene particles, and their phase-dependent fluorescence lifetimes were analyzed to distinguish between contaminated and uncontaminated food. The results indicate that fluorescence lifetime analysis can effectively identify microplastic contamination, revealing distinct fluorescence characteristics for each food matrix. This research underscores the importance of innovative detection methods in ensuring food safety, highlighting the need for further studies on automated microplastic detection. KW - Microplastic KW - Food KW - FLIM KW - fluorescence Y1 - 2025 U6 - https://doi.org/10.1109/SAS65169.2025.11105138 SP - 1 EP - 5 PB - IEEE ER -