@inproceedings{LeiterWohlschlaegerAueretal.2021, author = {Leiter, Nina and Wohlschl{\"a}ger, Maximilian and Auer, Veronika and Versen, Martin and Laforsch, Christian}, title = {A Novel Approach to Identify Wood Species Optically using Fluorescence Lifetime Imaging Microscopy}, series = {SMSI 2021 Conference - Sensor and Measurement Science International}, booktitle = {SMSI 2021 Conference - Sensor and Measurement Science International}, pages = {169 -- 170}, year = {2021}, abstract = {This contribution presents the results obtained with fluorescence lifetime imaging microscopy (FLIM)within the optical identification and differentiation of the four wood species walnut, beech, spruce, and maple. The experimental setup as well as the evaluation algorithm, with which the experiments were carried out, is explained briefly.}, 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{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{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} }