@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} } @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{WohlschlaegerLeiterDietlmeieretal.2023, author = {Wohlschl{\"a}ger, Maximilian and Leiter, Nina and Dietlmeier, Maximilian and L{\"o}der, Martin G.J. and Versen, Martin and Laforsch, Christian}, title = {Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials}, series = {2023 International Joint Conference on Neural Networks (IJCNN)}, booktitle = {2023 International Joint Conference on Neural Networks (IJCNN)}, doi = {10.1109/IJCNN54540.2023.10191054}, pages = {1 -- 9}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{LeiterDietlmeierWohlschlaegeretal.2023, author = {Leiter, Nina and Dietlmeier, Maximilian and Wohlschl{\"a}ger, Maximilian and L{\"o}der, Martin G.J. and Versen, Martin and Laforsch, Christian}, title = {Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM}, series = {2023 IEEE Sensors Applications Symposium (SAS)}, booktitle = {2023 IEEE Sensors Applications Symposium (SAS)}, doi = {10.1109/SAS58821.2023.10254174}, pages = {1 -- 6}, year = {2023}, abstract = {The economic use of wood is a growing sector, not only because of the significant advantage of wood to retain CO 2 . It is crucial to increase the material recycling of wood in several lifecycles, but currently, there is no reliable post-consumer wood sorting technique in line. This research measures 365 post-consumer wood samples of classes A1-4 four times with the frequency domain fluorescence lifetime imaging microscopy (FD-FLIM) subset method rapid fluorescence lifetime imaging microscopy (Rapid-FLIM). The data is analyzed on their statistical features. Four neural networks based on Multilayer perceptron are then trained and tested with twelve statistical features extracted from the Rapid-FLIM images. The best model for this application contains the optimizer RMSprop, the activation function SELU and the loss function binary crossentropy. The best model of this structure could achieve a false positive ratio of 4.79 \% over the ten folds.}, language = {en} } @inproceedings{LeiterWohlschlaegerDietlmeieretal.2023, author = {Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Dietlmeier, Maximilian and Versen, Martin and L{\"o}der, Martin and Laforsch, Christian}, title = {Comparative Analysis of Fluorescence Properties of Post-Consumer Wood Using FD-FLIM}, series = {2023 IEEE Sensors Applications Symposium (SAS)}, booktitle = {2023 IEEE Sensors Applications Symposium (SAS)}, doi = {10.1109/SAS58821.2023.10254052}, pages = {1 -- 6}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{LeiterSchwarzVersenetal.2024, author = {Leiter, Nina and Schwarz, Jonas and Versen, Martin and Risse, Michael and L{\"o}der, Martin G.J. and Laforsch, Christian}, title = {A non-destructive approach to wood origin differentiation using FD-FLIM}, series = {2024 IEEE Sensors Applications Symposium (SAS)}, booktitle = {2024 IEEE Sensors Applications Symposium (SAS)}, publisher = {IEEE}, doi = {10.1109/SAS60918.2024.10636550}, pages = {1 -- 6}, year = {2024}, abstract = {Wood auto-fluorescence, primarily attributed to lignin, presents a distinctive feature. Different wood species exhibit variations in lignin distribution. Frequency-Domain Fluorescence Lifetime Imaging Microscopy is effective in distinguishing wood species based on their fluorescence characteristics. This study investigates the potential to differentiate the origins of beech, spruce, and larch through phase-dependent fluorescence decay times. Therefore the zero hypothesis H0 is tested: The phase dependent fluorescence lifetimes of samples from the same species but varying in origin are equal. To determine the fluorescence characteristics of woods of different origins, wood samples of the species Fagus sylvatica L. (beech), Larix decidua Mill. (larch), and Picea abies (L.) H. Karst. (spruce) from Germany, Austria, the Netherlands, Spain, Sweden, New Zealand and Romania were analyzed. The wood samples were analyzed with a FD-FLIM camera setup, including a laser source emitting at an excitation wavelength of 445 nm. Employing Analysis-of-Variance hypothesis testing on fluorescence lifetime data for each wood species, the results indicate that 23 out of 35 origin pairs could be distinguished at a 5\% significance level. While acknowledging the challenges of origin-based differentiation, the findings emphasize the promising potential of fluorescence lifetime imaging microscope as a valuable tool in this context. Moving forward, a more intricate approach to sample differentiation should involve acquiring detailed information about the samples, including associated temperature and precipitation profiles, and soil composition.}, 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} } @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} }