TY - CHAP A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Auer, Veronika A1 - Versen, Martin A1 - Laforsch, Christian T1 - A Novel Approach to Identify Wood Species Optically using Fluorescence Lifetime Imaging Microscopy T2 - SMSI 2021 Conference – Sensor and Measurement Science International N2 - 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. KW - fluorescence lifetime KW - wood species identification KW - fluorescence KW - wood KW - FLIM Y1 - 2021 UR - https://doi.org/10.5162/SMSI2021/B10.2 SP - 169 EP - 170 ER - TY - CHAP A1 - Bernöcker, Anton A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin T1 - Entwicklung eines neuronalen Netzwerks als Basis zur automatisierten Holzartenerkennung T2 - Tagungsband AALE 2022, 18. AALE-Konferenz. Pforzheim, 09.03.-11.03.2022 N2 - Holz ist ein vielseitig einsetzbarer nachwachsender Rohstoff. Neben seinem wirtschaftlichen Nutzen ist er für den Erhalt des Klimas unersetzlich. Eine sortenreine Sortierung für die Weiterverarbeitung von Altholz spielt für einen ressourcenschonenden Umgang eine wichtige Rolle. Um das Potenzial eines neuronalen Netzwerks basierend auf Messdaten der bildgebenden Fluoreszenzabklingzeitmessung für die Altholzsortierung aufzuzeigen, wurden zwei unterschiedliche Klassifikationsansätze auf Basis der Programmiersprache Python gewählt. Die Ergebnisse zeigen, dass die bildbasierte Klassifizierung der Holzart mit einer Genauigkeit von 47,36 % noch ausbaufähig ist. Eine datenbasierte Klassifizierung der Holzart mit einer Identifikationsgenauigkeit von 98,28 % ist dagegen vielversprechend. KW - FD-FLIM KW - Fluoreszenz KW - Fluoreszenzabklingzeit KW - Holzart KW - Neuronales Netz KW - Klassifizierung Y1 - 2022 U6 - https://doi.org/https://doi.org/10.33968/2022.11 SP - 115 EP - 124 ER - TY - CHAP A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Auer, Veronika A1 - Versen, Martin A1 - Laforsch, Christian T1 - An algorithmic method to identify multiple wood species in a single image by their specific fluorescence lifetimes T2 - OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP) N2 - 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. KW - Confocal laser scanning microscopy KW - Fluorescence lifetime imaging KW - Image processing KW - Image processing algorithms KW - Phase shift KW - Ultraviolet radiation Y1 - 2021 U6 - https://doi.org/10.1364/COSI.2021.CF2B.3 SP - CF2B.3 ER - TY - CHAP A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin T1 - Frequency-domain fluorescence lifetime imaging as method to analyze wood structures T2 - 2022 Conference on Lasers and Electro-Optics (CLEO) Y1 - 2022 SP - 1 EP - 2 ER - TY - CHAP A1 - Leiter, Nina A1 - Wohlschlaeger, Maximilian A1 - Versen, Martin A1 - Laforsch, Christian T1 - Non-destructive differentiation of treated and non-treated wood using fluorescence lifetime imaging microscopy T2 - Sensors and Measuring Systems; 21th ITG/GMA-Symposium Y1 - 2022 SP - 1 EP - 6 ER - TY - JOUR A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin A1 - Laforsch, Christian T1 - An algorithmic method for the identification of wood species and the classification of post-consumer wood using fluorescence lifetime imaging microscopy JF - Journal of Sensors and Sensor Systems N2 - 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. Y1 - 2022 U6 - https://doi.org/10.5194/jsss-11-129-2022 VL - 11 IS - 1 SP - 129 EP - 136 ER - TY - CHAP A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin T1 - Frequency-domain fluorescence lifetime imaging as method to analyze wood structures T2 - Conference on Lasers and Electro-Optics N2 - 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. KW - Diode lasers KW - Fluorescence KW - Fluorescence lifetime imaging KW - Microscopy Y1 - 2022 U6 - https://doi.org/10.1364/CLEO_AT.2022.JW3A.19 SP - JW3A.19 ER - TY - CHAP A1 - Maniyattu, Georgekutty Jose A1 - Geegy, Eldho A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin A1 - Laforsch, Christian T1 - Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy T2 - 2022 IEEE Sensors Applications Symposium (SAS) N2 - 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. KW - fluorescence lifetime imaging KW - FLIM KW - neural networks KW - plastic identification Y1 - 2022 U6 - https://doi.org/10.1109/SAS54819.2022.9881372 SP - 1 EP - 6 ER - TY - CHAP A1 - Bernöcker, Anton A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin T1 - Entwicklung eines neuronalen Netzwerks als Basis zur automatisierten Holzartenerkennung T2 - 18. AALE-Konferenz. Pforzheim, 09.03.-11.03.2022 N2 - Holz ist ein vielseitig einsetzbarer nachwachsender Rohstoff. Neben seinem wirtschaftlichen Nutzen ist er für den Erhalt des Klimas unersetzlich. Eine sortenreine Sortierung für die Weiterverarbeitung von Altholz spielt für einen ressourcenschonenden Umgang eine wichtige Rolle. Um das Potenzial eines neuronalen Netzwerks basierend auf Messdaten der bildgebenden Fluoreszenzabklingzeitmessung für die Altholzsortierung aufzuzeigen, wurden zwei unterschiedliche Klassifikationsansätze auf Basis der Programmiersprache Python gewählt. Die Ergebnisse zeigen, dass die bildbasierte Klassifizierung der Holzart mit einer Genauigkeit von 47,36 % noch ausbaufähig ist. Eine datenbasierte Klassifizierung der Holzart mit einer Identifikationsgenauigkeit von 98,28 % ist dagegen vielversprechend. KW - FD-FLIM KW - Fluoreszenz KW - Fluoreszenzabklingzeit KW - Holzart KW - Neuronales Netz KW - Klassifizierung Y1 - 2022 U6 - https://doi.org/10.33968/2022.11 ER - TY - CHAP A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Auer, Veronika A1 - Versen, Martin A1 - Laforsch, Christian T1 - An algorithmic method to identify multiple wood species in a single image by their specific fluorescence lifetimes T2 - OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP) (2021), paper CF2B.3 N2 - 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. Y1 - 2021 U6 - https://doi.org/10.1364/COSI.2021.CF2B.3 SP - CF2B.3 ER - TY - JOUR A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin T1 - Analysis of the phase-dependent fluorescence decay time of treated wood using FD-FLIM JF - tm - Technisches Messen N2 - 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. N2 - Für einen nachhaltigen Umgang mit Holz und eine hohe stoffliche Verwertung muss Altholz in Deutschland nach Altholzklassen sortiert werden. Die zerstörungsfreie FD-FLIM-Methode könnte den Holzrecyclingprozess durch eine automatische Klassifizierung von Altholz in Altholzklassen verbessern. Daher wird in dieser Studie die phasenabhängige Fluoreszenzabklingzeit von behandeltem Holz analysiert, um den Einfluss der Behandlung auf die Fluoreszenzabklingzeit zu ermitteln. Es wird gezeigt, dass behandeltes und unbehandeltes Holz anhand der Fluoreszenzabklingzeit unterschieden werden kann, auch wenn die nach dem Stand der Forschung theoretische Fluoreszenzabklingzeit von der experimentell ermittelten Fluoreszenzabklingzeit abweicht. KW - fluorescence KW - decay time KW - FD-FLIM KW - lifetime KW - wood Y1 - 2023 U6 - https://doi.org/10.1515/teme-2022-0114 VL - 90 IS - 6 SP - 401 EP - 406 ER - TY - CHAP A1 - Dietlmeier, Maximilian A1 - Rajan, Aromal Somarajan A1 - Leiter, Nina A1 - Wohlschläger, Maximilian A1 - Versen, Martin T1 - Klassifizierung von Altholz durch FD-FLIM Bilder mit neuronalen Netzen T2 - Tagungsband AALE 2023 N2 - Holz ist eine der wichtigsten erneuerbaren Ressourcen und kann recycelt werden, wobei derzeit nur unbehandeltes Holz in mehreren Lebenszyklen verwendet werden kann. Allerdings werden in Altholzsortieranlagen meist behandeltes und unbehandeltes Altholz gemischt angeliefert und es gibt keine automatische Klassifizierung zur sortenreinen Sortierung. Frühere Studien zeigen, dass behandelte und unbehandelte Holzproben mit Hilfe von bildgebender Fluoreszenzabklingzeit Mikroskopie unterschieden werden können. Um den Prozess der Klassifizierung von Altholz anhand von Fluoreszenzabklingzeitbildern zu automatisieren, werden zwei neuronale Netzwerke (NN) evaluiert. Das erste NN ist ein MLP (Multi Layer Perceptron), das das Altholz anhand von Erwartungswert und Standardabweichung eines Fluoreszenzabklingzeitbildes klassifiziert. Das zweite untersuchte NN ist ein CNN (Convolutional Neural Network), das das Holz direkt anhand der Fluoreszenzabklingzeitbilder klassifiziert. Beide Netzwerke sind in Python implementiert, um die spätere Verwendung in einem automatischen Klassifizierungs- und Sortierprozess zu erleichtern. Die Evaluation der NN zeigt vielversprechende Ergebnisse zur automatisierten Klassifikation von Altholz. Y1 - 2023 U6 - https://doi.org/10.33968/2023.24 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 - Thomas, Amal A1 - Saleem, Shaif A1 - Leiter, Nina A1 - Dietlmeier, Maximilian A1 - Wohlschläger, Maximilian A1 - Versen, Martin A1 - Laforsch, Christian T1 - Development of a Neural Network for an Automated Differentiation of Plastics using Rapid-FLIM T2 - Optica Sensing Congress 2023 N2 - The fast classification and identification of plastics presents a significant challenge. The study assesses the suitability of a Multilayer Perceptron to classify and identify commonly found plastic types using Rapid-FLIM, achieving an accuracy of 88.33%. KW - Fluorescence lifetime imaging KW - Phase shift KW - Laser sources KW - Neural networks KW - Positron emission tomography KW - Raman spectroscopy Y1 - 2023 U6 - https://doi.org/10.1364/ES.2023.EW4E.5 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 - 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 - Leiter, Nina A1 - Dietlmeier, Maximilian A1 - Wohlschläger, Maximilian A1 - Löder, Martin G.J. A1 - Versen, Martin A1 - Laforsch, Christian T1 - Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM T2 - 2023 IEEE Sensors Applications Symposium (SAS) N2 - 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. KW - fluorescence KW - Fluorescence KW - Microscopy KW - Neural networks KW - MLP KW - Current measurement KW - Feature extraction KW - Loss measurement KW - Post-consumer wood classification KW - Rapid-FLIM KW - Rapid-Fluorescence Lifetime Imaging Microscopy KW - Time measurement Y1 - 2023 U6 - https://doi.org/10.1109/SAS58821.2023.10254174 SP - 1 EP - 6 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 - TY - CHAP A1 - Leiter, Nina A1 - Schwarz, Jonas A1 - Versen, Martin A1 - Risse, Michael A1 - Löder, Martin G.J. A1 - Laforsch, Christian T1 - A non-destructive approach to wood origin differentiation using FD-FLIM T2 - 2024 IEEE Sensors Applications Symposium (SAS) N2 - 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. KW - wood KW - fluorescence KW - FLIM KW - origin KW - species Y1 - 2024 U6 - https://doi.org/10.1109/SAS60918.2024.10636550 SP - 1 EP - 6 PB - IEEE ER -