@article{LeiterWohlschlaegerVersenetal.2022, author = {Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Versen, Martin and Laforsch, Christian}, title = {An algorithmic method for the identification of wood species and the classification of post-consumer wood using fluorescence lifetime imaging microscopy}, series = {Journal of Sensors and Sensor Systems}, volume = {11}, journal = {Journal of Sensors and Sensor Systems}, number = {1}, doi = {10.5194/jsss-11-129-2022}, pages = {129 -- 136}, year = {2022}, abstract = {In this contribution the frequency domain fluorescence lifetime imaging microscopy (FD-FLIM) technique is evaluated for post-consumer wood sorting. The fluorescence characteristics of several wood samples were determined, whereby two excitation wavelengths (405 and 488 nm) were used. The measured data were processed using algorithmic methods to identify the wood species and post-consumer wood category. With the excitation wavelength of 405 nm, 16 out of 19 samples could be correctly assigned to the corresponding post-consumer wood category by means of the fluorescence lifetimes. Thus, the experimental results revealed the high potential of the FD-FLIM technique for automated post-consumer wood sorting.}, language = {en} } @inproceedings{WohlschlaegerVersenLaforsch2022, author = {Wohlschl{\"a}ger, Maximilian and Versen, Martin and Laforsch, Christian}, title = {Investigation of the Impact of CaCO3 Concentrations on the Fluorescence Lifetime of Polypropylene}, series = {2022 Conference on Lasers and Electro-Optics (CLEO)}, booktitle = {2022 Conference on Lasers and Electro-Optics (CLEO)}, pages = {1 -- 2}, year = {2022}, abstract = {The impact of fillers on the fluorescence lifetime of polymers is still unknown. Investigating polypropylene containing different CaCO 3 concentrations using FD-FLIM show an exponential increase in fluorescence lifetime proportional to the CaCO 3 density in polypropylene.}, language = {en} } @inproceedings{LeiterWohlschlaegerVersen2022, author = {Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Versen, Martin}, title = {Frequency-domain fluorescence lifetime imaging as method to analyze wood structures}, series = {Conference on Lasers and Electro-Optics}, booktitle = {Conference on Lasers and Electro-Optics}, organization = {Technical Digest Series (Optica Publishing Group, 2022)}, doi = {10.1364/CLEO_AT.2022.JW3A.19}, pages = {JW3A.19}, year = {2022}, abstract = {The analysis of wood structures using FD-FLIM seems promising for the identification of wood at 488 nm, as lignin has a high impact on the fluorescence lifetime.}, language = {en} } @inproceedings{ManiyattuGeegyLeiteretal.2022, author = {Maniyattu, Georgekutty Jose and Geegy, Eldho and Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Versen, Martin and Laforsch, Christian}, title = {Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy}, series = {2022 IEEE Sensors Applications Symposium (SAS)}, booktitle = {2022 IEEE Sensors Applications Symposium (SAS)}, doi = {10.1109/SAS54819.2022.9881372}, pages = {1 -- 6}, year = {2022}, abstract = {Plastics have become a major part of human's daily life. An uncontrolled usage of plastic leads to an accumulation in the environment posing a threat to flora and fauna, if not recycled correctly. The correct sorting and recycling of the most commonly available plastic types and an identification of plastic in the environment are important. Fluorescence lifetime imaging microscopy shows a high potential in sorting and identifying plastic types. A data-based and an image-based classification are investigated using python programming language to demonstrate the potential of a neural network based on fluorescence lifetime images to identify plastic types. The results indicate that the data-based classification has a higher identification accuracy compared to the image-based classification.}, language = {en} } @inproceedings{BernoeckerLeiterWohlschlaegeretal.2022, author = {Bern{\"o}cker, Anton and Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Versen, Martin}, title = {Entwicklung eines neuronalen Netzwerks als Basis zur automatisierten Holzartenerkennung}, series = {18. AALE-Konferenz. Pforzheim, 09.03.-11.03.2022}, booktitle = {18. AALE-Konferenz. Pforzheim, 09.03.-11.03.2022}, organization = {Hochschule f{\"u}r Technik, Wirtschaft und Kultur Leipzig}, doi = {10.33968/2022.11}, year = {2022}, abstract = {Holz ist ein vielseitig einsetzbarer nachwachsender Rohstoff. Neben seinem wirtschaftlichen Nutzen ist er f{\"u}r den Erhalt des Klimas unersetzlich. Eine sortenreine Sortierung f{\"u}r die Weiterverarbeitung von Altholz spielt f{\"u}r einen ressourcenschonenden Umgang eine wichtige Rolle. Um das Potenzial eines neuronalen Netzwerks basierend auf Messdaten der bildgebenden Fluoreszenzabklingzeitmessung f{\"u}r die Altholzsortierung aufzuzeigen, wurden zwei unterschiedliche Klassifikationsans{\"a}tze auf Basis der Programmiersprache Python gew{\"a}hlt. Die Ergebnisse zeigen, dass die bildbasierte Klassifizierung der Holzart mit einer Genauigkeit von 47,36 \% noch ausbauf{\"a}hig ist. Eine datenbasierte Klassifizierung der Holzart mit einer Identifikationsgenauigkeit von 98,28 \% ist dagegen vielversprechend.}, language = {de} } @inproceedings{WohlschlaegerVersenLaforsch2022, author = {Wohlschl{\"a}ger, Maximilian and Versen, Martin and Laforsch, Christian}, title = {Investigation of the Impact of CaCO3 Concentrations on the Fluorescence Lifetime of Polypropylene}, series = {Conference on Lasers and Electro-Optics}, booktitle = {Conference on Lasers and Electro-Optics}, organization = {Technical Digest Series (Optica Publishing Group, 2022)}, doi = {10.1364/CLEO_AT.2022.AM2M.2}, pages = {AM2M.2}, year = {2022}, abstract = {The impact of fillers on the fluorescence lifetime of polymers is still unknown. Investigating polypropylene containing different CaCO3 concentrations using FD-FLIM show an exponential increase in fluorescence lifetime proportional to the CaCO3 density in polypropylene.}, language = {en} } @inproceedings{WohlschlaegerHolstVersenetal.2021, author = {Wohlschl{\"a}ger, Maximilian and Holst, Gerhard and Versen, Martin and Laforsch, Christian}, title = {An optical method to differentiate wood from polymers using fluorescence lifetime imaging microscopy}, series = {OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES) (2021), paper EW4G.6}, booktitle = {OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES) (2021), paper EW4G.6}, doi = {10.1364/ES.2021.EW4G.6}, pages = {EW4G.6}, year = {2021}, abstract = {In this contribution investigations with fluorescence lifetime imaging microscopy (FLIM) are made to optically distinguish polymers and wood. The results show that the distinction and identification is possible in a graphical and calculative way.}, language = {en} } @inproceedings{LeiterWohlschlaegerAueretal.2021, author = {Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Auer, Veronika and Versen, Martin and Laforsch, Christian}, title = {An algorithmic method to identify multiple wood species in a single image by their specific fluorescence lifetimes}, series = {OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP) (2021), paper CF2B.3}, booktitle = {OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP) (2021), paper CF2B.3}, doi = {10.1364/COSI.2021.CF2B.3}, pages = {CF2B.3}, year = {2021}, abstract = {Fluorescence lifetime imaging microscopy (FLIM) combined with an image processing algorithm is successfully used for the optical identification and areal separation of three wood species of maple, larch and nut in a single taken image.}, language = {en} } @article{LeiterWohlschlaegerVersen2023, author = {Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Versen, Martin}, title = {Analysis of the phase-dependent fluorescence decay time of treated wood using FD-FLIM}, series = {tm - Technisches Messen}, volume = {90}, journal = {tm - Technisches Messen}, number = {6}, doi = {10.1515/teme-2022-0114}, pages = {401 -- 406}, year = {2023}, abstract = {For sustainable handling of wood and high material utilization, post-consumer wood has to be sorted by post-consumer wood categories in Germany. The non-destructive FD-FLIM method could improve the wood recycling process by automatic classification of waste wood into post-consumer wood classes. Thus, the phase-dependent fluorescence decay time of treated wood is analysed in this study to obtain the impact of the treatment on the fluorescence decay time. It is shown that treated and untreated wood can be differentiated using the fluorescence decay time, although the state-of-the-art theoretical fluorescence decay time differs from the experimentally determined fluorescence decay time.}, language = {en} } @inproceedings{WohlschlaegerKhanLeiteretal.2023, author = {Wohlschl{\"a}ger, Maximilian and Khan, Yamna and Leiter, Nina and Versen, Martin and L{\"o}der, Martin and Laforsch, Christian}, title = {Combining BLOB-Detection and MLP to Detect and Identify Plastics in an Environmental Matrix}, series = {2023 IEEE Sensors Applications Symposium (SAS)}, booktitle = {2023 IEEE Sensors Applications Symposium (SAS)}, doi = {10.1109/SAS58821.2023.10254171}, pages = {1 -- 5}, year = {2023}, abstract = {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.}, language = {en} } @article{WohlschlaegerVersenLoederetal.2024, author = {Wohlschl{\"a}ger, Maximilian and Versen, Martin and L{\"o}der, Martin G. J. and Laforsch, Christian}, title = {A promising method for fast identification of microplastic particles in environmental samples: A pilot study using fluorescence lifetime imaging microscopy}, series = {Heliyon}, volume = {10}, journal = {Heliyon}, number = {3}, doi = {10.1016/j.heliyon.2024.e25133}, year = {2024}, abstract = {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.}, language = {en} } @article{WohlschlaegerVersenLoederetal.2024, author = {Wohlschl{\"a}ger, Maximilian and Versen, Martin and L{\"o}der, Martin G. J. and Laforsch, Christian}, title = {Identification of different plastic types and natural materials from terrestrial environments using fluorescence lifetime imaging microscopy.}, series = {Analytical and Bioanalytical Chemistry}, journal = {Analytical and Bioanalytical Chemistry}, doi = {10.1007/s00216-024-05305-w}, year = {2024}, abstract = {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.}, language = {en} } @article{ManiyattuGeegyWohlschlaegeretal.2023, author = {Maniyattu, Georgekutty Jose and Geegy, Eldho and Wohlschl{\"a}ger, Maximilian and Leiter, Nina and Versen, Martin and Laforsch, Christian}, title = {Multilayer Perceptron Development to Identify Plastics Using Fluorescence Lifetime Imaging Microscopy}, series = {EDFA Technical Articles}, volume = {25}, journal = {EDFA Technical Articles}, number = {3}, doi = {10.31399/asm.edfa.2023-3.p031}, pages = {31 -- 37}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{WohlschlaegerKhanLeiteretal.2023, author = {Wohlschl{\"a}ger, Maximilian and Khan, Yamna and Leiter, Nina and Versen, Martin and L{\"o}der, Martin and Laforsch, Christian}, title = {Development of a BLOB-detection algorithm based on DoG to detect Plastic in an environmental matrix using FD-FLIM}, series = {Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES)}, booktitle = {Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES)}, doi = {10.1364/ES.2023.EW4E.4}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{SchwarzWohlschlaegerLeiteretal.2023, author = {Schwarz, Jonas and Wohlschl{\"a}ger, Maximilian and Leiter, Nina and Auer, Veronika and Risse, Michael and Versen, Martin}, title = {Frequency Domain Fluorescence Lifetime Imaging Microscopy (FD-FLIM) analysis of Quercus robur samples for origin differentiation purposes}, series = {Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES)}, booktitle = {Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES)}, doi = {10.1364/AIS.2023.JTu4A.10}, year = {2023}, abstract = {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.}, language = {en} } @article{VersenWohlschlaegerLanghalsetal.2024, author = {Versen, Martin and Wohlschl{\"a}ger, Maximilian and Langhals, Heinz and Laforsch, Christian}, title = {The detection of organic polymers as contaminants in foodstuffs by means of the fluorescence decay of their auto fluorescence}, series = {Food and Humanity}, volume = {3}, journal = {Food and Humanity}, doi = {10.1016/j.foohum.2024.100363}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{HeitzmannWohlschlaegerLeiteretal.2024, author = {Heitzmann, Sebastian and Wohlschl{\"a}ger, Maximilian and Leiter, Nina and L{\"o}der, Martin G. J. and Versen, Martin and Laforsch, Christian}, title = {Classification of Foods and Plastics using FD-FLIM and Neural Networks}, series = {2024 IEEE Sensors Applications Symposium (SAS)}, booktitle = {2024 IEEE Sensors Applications Symposium (SAS)}, publisher = {IEEE}, doi = {10.1109/SAS60918.2024.10636453}, pages = {1 -- 6}, year = {2024}, abstract = {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.}, language = {en} } @misc{HeitzmannKallweitBrunnetal.2025, author = {Heitzmann, Sebastian and Kallweit, Stephan and Brunn, Andr{\´e} and Holst, Gerhard and Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Versen, Martin}, title = {Erweiterung eines FD-FLIM Messsystems durch MQTT Anbindung eines Roboters zur automatischen Sortierung von Altholzklassen}, series = {Tagungsband AALE 2025: Menschenzentrierte Automation im digitalen Zeitalter}, journal = {Tagungsband AALE 2025: Menschenzentrierte Automation im digitalen Zeitalter}, doi = {10.33968/2025.16}, pages = {147 -- 156}, year = {2025}, abstract = {Ziel des Projektes Fluoreszenz ID von Altholz (FrIDAH)5 ist die Entwicklung eines Demonstrators gewesen, welcher die automatisierte Sortierung von Altholzproben gem{\"a}ß der Altholzverordnung unter Verwendung der Messung von Fluoreszenzabklingzeiten erm{\"o}glicht. In diesem Beitrag werden der entwickelte Messaufbau, die Software, das Automatisierungssystem, sowie der Klassifikator vorgestellt. Die Ergebnisse zeigen, dass die verwendete Technologie zur zuverl{\"a}ssigen Klassifikation von Altholz geeignet ist und f{\"u}r die automatisierte Sortierung angewendet werden kann.}, language = {de} } @article{LeiterWohlschlaegerVersenetal.2025, author = {Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Versen, Martin and Harter, Sonja D. and Kießlich, Tina and Lederer, Franziska and Clauß, Stefanie and Schlosser, Dietmar and Armanu, Emanuel Gheorghita and Eberlein, Christian and Heipieper, Hermann J. and L{\"o}der, Martin G. J. and Laforsch, Christian}, title = {Effects of defined organic layers on the fluorescence lifetime of plastic materials}, series = {Analytical and Bioanalytical Chemistry}, volume = {417}, journal = {Analytical and Bioanalytical Chemistry}, number = {16}, doi = {10.1007/s00216-025-05888-y}, pages = {3651 -- 3663}, year = {2025}, abstract = {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.}, language = {en} } @misc{LeiterHeitzmannVersenetal.2025, author = {Leiter, Nina and Heitzmann, Sebastian and Versen, Martin and Wohlschl{\"a}ger, Maximilian and L{\"o}der, Martin G.J. and Laforsch, Christian}, title = {Identification of Microplastic Contamination in Food using FD-FLIM}, series = {2025 IEEE Sensors Applications Symposium (SAS)}, journal = {2025 IEEE Sensors Applications Symposium (SAS)}, publisher = {IEEE}, doi = {10.1109/SAS65169.2025.11105138}, pages = {1 -- 5}, year = {2025}, abstract = {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.}, language = {en} }