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  <doc>
    <id>2444</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>5</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
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    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Combining BLOB-Detection and MLP to Detect and Identify Plastics in an Environmental Matrix</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2023 IEEE Sensors Applications Symposium (SAS)</parentTitle>
    <identifier type="doi">10.1109/SAS58821.2023.10254171</identifier>
    <enrichment key="opus.import.data">@inproceedingswohlschlager_combining_2023, title = Combining BLOB-Detection and MLP to Detect and Identify Plastics in an Environmental Matrix, doi = 10.1109/SAS58821.2023.10254171, booktitle = 2023 IEEE Sensors Applications Symposium (SAS), author = Wohlschläger, Maximilian and Khan, Yamna and Leiter, Nina and Versen, Martin and Löder, Martin and Laforsch, Christian, year = 2023, keywords = Fluorescence, Plastics, FD-FLIM, Neural networks, MLP, blob detection, Classification algorithms, Dogs, fluorescence lifetime, plastics identification, Soil, Thresholding (Imaging), pages = 01–05,</enrichment>
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    <author>Maximilian Wohlschläger</author>
    <author>Yamna Khan</author>
    <author>Nina Leiter</author>
    <author>Martin Versen</author>
    <author>Martin Löder</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Plastics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FD-FLIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MLP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>blob detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Classification algorithms</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dogs</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence lifetime</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>plastics identification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Soil</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Thresholding (Imaging)</value>
    </subject>
  </doc>
  <doc>
    <id>2438</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
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    <issue/>
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    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Development of a BLOB-detection algorithm based on DoG to detect Plastic in an environmental matrix using FD-FLIM</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES)</parentTitle>
    <identifier type="doi">10.1364/ES.2023.EW4E.4</identifier>
    <enrichment key="opus.import.data">@inproceedingswohlschlager_development_2023, title = Development of a BLOB-detection algorithm based on DoG to detect Plastic in an environmental matrix using FD-FLIM, url = https://opg.optica.org/abstract.cfm?URI=ES-2023-EW4E.4, doi = 10.1364/ES.2023.EW4E.4, 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., booktitle = Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES), publisher = Optica Publishing Group, author = Wohlschläger, Maximilian and Khan, Yamna and Leiter, Nina and Versen, Martin and Löder, Martin and Laforsch, Christian, year = 2023, note = Journal Abbreviation: Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES), keywords = Diode lasers, Fluorescence lifetime imaging, Phase shift, Neural networks, Optical filters, Spatial resolution, pages = EW4E.4,</enrichment>
    <enrichment key="opus.import.dataHash">md5:ed8ebf3f812ae7e83aa23a628a356eee</enrichment>
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    <author>Maximilian Wohlschläger</author>
    <author>Yamna Khan</author>
    <author>Nina Leiter</author>
    <author>Martin Versen</author>
    <author>Martin Löder</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Diode lasers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence lifetime imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Phase shift</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optical filters</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Spatial resolution</value>
    </subject>
  </doc>
  <doc>
    <id>2441</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>9</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2023 International Joint Conference on Neural Networks (IJCNN)</parentTitle>
    <identifier type="doi">10.1109/IJCNN54540.2023.10191054</identifier>
    <enrichment key="opus.import.data">@inproceedingswohlschlager_comparison_2023, title = Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials, doi = 10.1109/IJCNN54540.2023.10191054, booktitle = 2023 International Joint Conference on Neural Networks (IJCNN), author = Wohlschläger, Maximilian and Leiter, Nina and Dietlmeier, Maximilian and Löder, Martin G.J. and Versen, Martin and Laforsch, Christian, year = 2023, keywords = Fluorescence, Microscopy, Plastics, FD-FLIM, Neural networks, Environment, Frequency-domain analysis, Gaussian analysis, MLP, Multilayer perceptrons, Random forests, RFC, pages = 1–9,</enrichment>
    <enrichment key="opus.import.dataHash">md5:e9b53ffef54730e6c949ed39d9e70201</enrichment>
    <enrichment key="opus.import.date">2024-05-27T12:38:19+00:00</enrichment>
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    <enrichment key="opus.import.format">bibtex</enrichment>
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    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <author>Maximilian Wohlschläger</author>
    <author>Nina Leiter</author>
    <author>Maximilian Dietlmeier</author>
    <author>Martin G.J. Löder</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Plastics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FD-FLIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Frequency-domain analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gaussian analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MLP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multilayer perceptrons</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Random forests</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RFC</value>
    </subject>
  </doc>
  <doc>
    <id>2442</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2023 IEEE Sensors Applications Symposium (SAS)</parentTitle>
    <identifier type="doi">10.1109/SAS58821.2023.10254174</identifier>
    <enrichment key="opus.import.data">@inproceedingsleiter_development_2023, title = Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM, doi = 10.1109/SAS58821.2023.10254174, booktitle = 2023 IEEE Sensors Applications Symposium (SAS), author = Leiter, Nina and Dietlmeier, Maximilian and Wohlschläger, Maximilian and Löder, Martin G.J. and Versen, Martin and Laforsch, Christian, year = 2023, keywords = fluorescence, Fluorescence, Microscopy, Neural networks, MLP, Current measurement, Feature extraction, Loss measurement, Post-consumer wood classification, Rapid-FLIM, Rapid-Fluorescence Lifetime Imaging Microscopy, Time measurement, pages = 01–06,</enrichment>
    <enrichment key="opus.import.dataHash">md5:32f666ffd4ce419dce9463823190d772</enrichment>
    <enrichment key="opus.import.date">2024-05-27T12:38:19+00:00</enrichment>
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    <author>Nina Leiter</author>
    <author>Maximilian Dietlmeier</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin G.J. Löder</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MLP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Current measurement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Feature extraction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Loss measurement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Post-consumer wood classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Rapid-FLIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Rapid-Fluorescence Lifetime Imaging Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Time measurement</value>
    </subject>
  </doc>
  <doc>
    <id>2443</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparative Analysis of Fluorescence Properties of Post-Consumer Wood Using FD-FLIM</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2023 IEEE Sensors Applications Symposium (SAS)</parentTitle>
    <identifier type="doi">10.1109/SAS58821.2023.10254052</identifier>
    <enrichment key="opus.import.data">@inproceedingsleiter_comparative_2023, title = Comparative Analysis of Fluorescence Properties of Post-Consumer Wood Using FD-FLIM, doi = 10.1109/SAS58821.2023.10254052, booktitle = 2023 IEEE Sensors Applications Symposium (SAS), author = Leiter, Nina and Wohlschläger, Maximilian and Dietlmeier, Maximilian and Versen, Martin and Löder, Martin and Laforsch, Christian, year = 2023, keywords = fluorescence, Fluorescence, Neural networks, fluorescence properties, Moisture, post-consumer wood, Sensors, Training, Uncertainty, Visualization, waste wood, pages = 1–6,</enrichment>
    <enrichment key="opus.import.dataHash">md5:72865d2f2abde5cc0635ac4f1d2848f3</enrichment>
    <enrichment key="opus.import.date">2024-05-27T12:38:19+00:00</enrichment>
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    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
    <author>Maximilian Dietlmeier</author>
    <author>Martin Versen</author>
    <author>Martin Löder</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence properties</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Moisture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>post-consumer wood</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Training</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Uncertainty</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Visualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>waste wood</value>
    </subject>
  </doc>
  <doc>
    <id>2833</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A non-destructive approach to wood origin differentiation using FD-FLIM</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2024 IEEE Sensors Applications Symposium (SAS)</parentTitle>
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    <title language="eng">Classification of Foods and Plastics using FD-FLIM and Neural Networks</title>
    <abstract language="eng">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.</abstract>
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    <title language="eng">Effects of defined organic layers on the fluorescence lifetime of plastic materials</title>
    <abstract language="eng">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.</abstract>
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    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
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    <title language="eng">Identification of Microplastic Contamination in Food using FD-FLIM</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2025 IEEE Sensors Applications Symposium (SAS)</parentTitle>
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    <author>Nina Leiter</author>
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    <title language="eng">A Novel Approach to Identify Wood Species Optically using Fluorescence Lifetime Imaging Microscopy</title>
    <abstract language="eng">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.</abstract>
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    <title language="deu">Entwicklung eines neuronalen Netzwerks als Basis zur automatisierten Holzartenerkennung</title>
    <abstract language="deu">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.</abstract>
    <parentTitle language="deu">Tagungsband AALE 2022, 18. AALE-Konferenz. Pforzheim, 09.03.-11.03.2022</parentTitle>
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    <author>Anton Bernöcker</author>
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    <title language="eng">An algorithmic method to identify multiple wood species in a single image by their specific fluorescence lifetimes</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)</parentTitle>
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    <title language="eng">Frequency-domain fluorescence lifetime imaging as method to analyze wood structures</title>
    <parentTitle language="eng">2022 Conference on Lasers and Electro-Optics (CLEO)</parentTitle>
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    <title language="eng">Non-destructive differentiation of treated and non-treated wood using fluorescence lifetime imaging microscopy</title>
    <parentTitle language="eng">Sensors and Measuring Systems; 21th ITG/GMA-Symposium</parentTitle>
    <enrichment key="opus.import.data">@INPROCEEDINGS9861927, author=Leiter, Nina and Wohlschlaeger, Maximilian and Versen, Martin and Laforsch, Christian, booktitle=Sensors and Measuring Systems; 21th ITG/GMA-Symposium, title=Non-destructive differentiation of treated and non-treated wood using fluorescence lifetime imaging microscopy, year=2022, volume=, number=, pages=1-6, doi=</enrichment>
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    <author>Nina Leiter</author>
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    <title language="eng">An algorithmic method for the identification of wood species and the classification of post-consumer wood using fluorescence lifetime imaging microscopy</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Journal of Sensors and Sensor Systems</parentTitle>
    <identifier type="doi">10.5194/jsss-11-129-2022</identifier>
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    <title language="eng">Frequency-domain fluorescence lifetime imaging as method to analyze wood structures</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Conference on Lasers and Electro-Optics</parentTitle>
    <identifier type="doi">10.1364/CLEO_AT.2022.JW3A.19</identifier>
    <enrichment key="opus.import.data">@inproceedingsLeiter:22, author = Nina Leiter and Maximilian Wohlschläger and Martin Versen, booktitle = Conference on Lasers and Electro-Optics, journal = Conference on Lasers and Electro-Optics, keywords = Diode lasers; Fluorescence; Fluorescence lifetime imaging; Microscopy, pages = JW3A.19, publisher = Optica Publishing Group, title = Frequency-domain fluorescence lifetime imaging as method to analyze wood structures, year = 2022, url = https://opg.optica.org/abstract.cfm?URI=CLEO_AT-2022-JW3A.19, doi = 10.1364/CLEO_AT.2022.JW3A.19, 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.,</enrichment>
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      <type>uncontrolled</type>
      <value>Fluorescence lifetime imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microscopy</value>
    </subject>
  </doc>
  <doc>
    <id>2042</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber>6</pageNumber>
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    <title language="eng">Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2022 IEEE Sensors Applications Symposium (SAS)</parentTitle>
    <identifier type="doi">10.1109/SAS54819.2022.9881372</identifier>
    <enrichment key="opus.import.data">@INPROCEEDINGS9881372, author=Maniyattu, Georgekutty Jose and Geegy, Eldho and Leiter, Nina and Wohlschläger, Maximilian and Versen, Martin and Laforsch, Christian, booktitle=2022 IEEE Sensors Applications Symposium (SAS), title=Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy, year=2022, volume=, number=, pages=1-6, doi=10.1109/SAS54819.2022.9881372</enrichment>
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    <author>Georgekutty Jose Maniyattu</author>
    <author>Eldho Geegy</author>
    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence lifetime imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FLIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>plastic identification</value>
    </subject>
  </doc>
  <doc>
    <id>2039</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
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    <publisherName/>
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    <creatingCorporation>Hochschule für Technik, Wirtschaft und Kultur Leipzig</creatingCorporation>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="deu">Entwicklung eines neuronalen Netzwerks als Basis zur automatisierten Holzartenerkennung</title>
    <abstract language="deu">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.</abstract>
    <parentTitle language="deu">18. AALE-Konferenz. Pforzheim, 09.03.-11.03.2022</parentTitle>
    <identifier type="doi">10.33968/2022.11</identifier>
    <enrichment key="opus.import.data">@inproceedingsBernocker.09.03.11.03.2022, author = Bernöcker, Anton and Leiter, Nina and Wohlschläger, Maximilian and Versen, Martin, title = Entwicklung eines neuronalen Netzwerks als Basis zur automatisierten Holzartenerkennung, publisher = Hochschule für Technik, Wirtschaft und Kultur Leipzig, isbn = 9783910103009, booktitle = Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel, year = 09.03.-11.03.2022, doi = 10.33968/2022.11</enrichment>
    <enrichment key="opus.import.dataHash">md5:23892c0a8bc3f41097af5d15e64bf3b9</enrichment>
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    <author>Anton Bernöcker</author>
    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>FD-FLIM</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Fluoreszenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Fluoreszenzabklingzeit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Holzart</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Neuronales Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Klassifizierung</value>
    </subject>
  </doc>
  <doc>
    <id>2265</id>
    <completedYear>2021</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>CF2B.3</pageFirst>
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    <title language="eng">An algorithmic method to identify multiple wood species in a single image by their specific fluorescence lifetimes</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP) (2021), paper CF2B.3</parentTitle>
    <identifier type="doi">10.1364/COSI.2021.CF2B.3</identifier>
    <enrichment key="opus.import.data">@inproceedingsleiter_algorithmic_2021, title = An algorithmic method to identify multiple wood species in a single image by their specific fluorescence lifetimes, copyright = &amp;#169; 2021 The Author(s), url = https://opg.optica.org/abstract.cfm?uri=COSI-2021-CF2B.3, doi = 10.1364/COSI.2021.CF2B.3, 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, urldate = 2023-06-14, booktitle = OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP) (2021), paper CF2B.3, publisher = Optica Publishing Group, author = Leiter, Nina and Wohlschläger, Maximilian and Auer, Veronika and Versen, Martin and Laforsch, Christian, month = jul, year = 2021, pages = CF2B.3,</enrichment>
    <enrichment key="opus.import.dataHash">md5:1446fef6f81f39597733555dda2657fe</enrichment>
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    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
    <author>Veronika Auer</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <collection role="institutes" number="">Fakultät für Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>2266</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>401</pageFirst>
    <pageLast>406</pageLast>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue>6</issue>
    <volume>90</volume>
    <type>contributiontoperiodical</type>
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    <title language="eng">Analysis of the phase-dependent fluorescence decay time of treated wood using FD-FLIM</title>
    <abstract language="eng">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.</abstract>
    <abstract language="deu">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.</abstract>
    <parentTitle language="eng">tm - Technisches Messen</parentTitle>
    <identifier type="doi">10.1515/teme-2022-0114</identifier>
    <enrichment key="opus.import.data">@articleleiter_analysis_2023, title = Analysis of the phase-dependent fluorescence decay time of treated wood using FD-FLIM, volume = 90, issn = 2196-7113, url = https://www.degruyter.com/document/doi/10.1515/teme-2022-0114/html, doi = 10.1515/teme-2022-0114, 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, number = 6, urldate = 2023-06-14, journal = tm - Technisches Messen, author = Leiter, Nina and Wohlschläger, Maximilian and Versen, Martin, month = jun, year = 2023, note = Publisher: Oldenbourg Wissenschaftsverlag, keywords = fluorescence, decay time, FD-FLIM, lifetime, wood, pages = 401–406, file = Full Text PDF:CUsersjach208ZoterostorageFLSP5B55Leiter et al. - 2023 - Analysis of the phase-dependent fluorescence decay.pdf:application/pdf,</enrichment>
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    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>decay time</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FD-FLIM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lifetime</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>wood</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>2267</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
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    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Klassifizierung von Altholz durch FD-FLIM Bilder mit neuronalen Netzen</title>
    <abstract language="deu">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.</abstract>
    <parentTitle language="deu">Tagungsband AALE 2023</parentTitle>
    <identifier type="doi">10.33968/2023.24</identifier>
    <enrichment key="opus.import.data">@inproceedingsdietlmeier_klassifizierung_2023, title = Klassifizierung von Altholz durch FD-FLIM Bilder mit neuronalen Netzen, doi = 10.33968/2023.24, abstract = 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., language = ger, booktitle = Tagungsband AALE 2023, author = Dietlmeier, Maximilian and Rajan, Aromal Somarajan and Leiter, Nina and Wohlschläger, Maximilian and Versen, Martin, month = mar, year = 2023, note = ISBN: 9783910103016 Place: Luxemburg,</enrichment>
    <enrichment key="opus.import.dataHash">md5:1cd4b03d601770e66c7693df27327cf0</enrichment>
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    <author>Maximilian Dietlmeier</author>
    <author>Aromal Somarajan Rajan</author>
    <author>Nina Leiter</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <collection role="institutes" number="">Fakultät für Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>2434</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Development of a Neural Network for an Automated Differentiation of Plastics using Rapid-FLIM</title>
    <abstract language="eng">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%.</abstract>
    <parentTitle language="eng">Optica Sensing Congress 2023</parentTitle>
    <identifier type="doi">10.1364/ES.2023.EW4E.5</identifier>
    <enrichment key="opus.import.data">@inproceedingsamal_thomas_development_2023, title = Development of a Neural Network for an Automated Differentiation of Plastics using Rapid-FLIM, abstract = 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%., booktitle = Optica Sensing Congress 2023, author = Amal Thomas and Shaif Saleem and Nina Leiter and Maximilian Dietlmeier and Maximilian Wohlschläger and Martin Versen and Christian Laforsch, year = 2023, keywords = Fluorescence lifetime imaging, Phase shift, Laser sources, Neural networks, Positron emission tomography, Raman spectroscopy, pages = EW4E.5,</enrichment>
    <enrichment key="opus.import.dataHash">md5:9ebd3a369a146f94ee63f7840f2ee5a8</enrichment>
    <enrichment key="opus.import.date">2024-05-27T12:38:19+00:00</enrichment>
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    <author>Amal Thomas</author>
    <author>Shaif Saleem</author>
    <author>Nina Leiter</author>
    <author>Maximilian Dietlmeier</author>
    <author>Maximilian Wohlschläger</author>
    <author>Martin Versen</author>
    <author>Christian Laforsch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fluorescence lifetime imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Phase shift</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser sources</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Positron emission tomography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Raman spectroscopy</value>
    </subject>
  </doc>
  <doc>
    <id>2437</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>31</pageFirst>
    <pageLast>37</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>25</volume>
    <type>article</type>
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    <title language="eng">Multilayer Perceptron Development to Identify Plastics Using Fluorescence Lifetime Imaging Microscopy</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">EDFA Technical Articles</parentTitle>
    <identifier type="doi">10.31399/asm.edfa.2023-3.p031</identifier>
    <enrichment key="opus.import.data">@articlemaniyattu_multilayer_2023, title = Multilayer Perceptron Development to Identify Plastics Using Fluorescence Lifetime Imaging Microscopy, volume = 25, issn = 1537-0755, url = https://doi.org/10.31399/asm.edfa.2023-3.p031, doi = 10.31399/asm.edfa.2023-3.p031, 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., number = 3, journal = EDFA Technical Articles, author = Maniyattu, Georgekutty Jose and Geegy, Eldho and Wohlschläger, Maximilian and Leiter, Nina and Versen, Martin and Laforsch, Christian, month = aug, year = 2023, note = _eprint: https://dl.asminternational.org/edfa-tech/article-pdf/25/3/31/655168/edfa.2023-3.p031.pdf, pages = 31–37,</enrichment>
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    <author>Georgekutty Jose Maniyattu</author>
    <author>Eldho Geegy</author>
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    <title language="eng">Frequency Domain Fluorescence Lifetime Imaging Microscopy (FD-FLIM) analysis of Quercus robur samples for origin differentiation purposes</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES)</parentTitle>
    <identifier type="doi">10.1364/AIS.2023.JTu4A.10</identifier>
    <enrichment key="opus.import.data">@inproceedingsschwarz_frequency_2023, title = Frequency Domain Fluorescence Lifetime Imaging Microscopy (FD-FLIM) analysis of Quercus robur samples for origin differentiation purposes, url = https://opg.optica.org/abstract.cfm?URI=ES-2023-JTu4A.10, 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., booktitle = Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES), publisher = Optica Publishing Group, author = Schwarz, Jonas and Wohlschläger, Maximilian and Leiter, Nina and Auer, Veronika and Risse, Michael and Versen, Martin, year = 2023, note = Journal Abbreviation: Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES), keywords = Fluorescence lifetime imaging, Phase shift, Phase modulation, Laser sources, Bandpass filters, Frequency modulation, pages = JTu4A.10,</enrichment>
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    <author>Jonas Schwarz</author>
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    <title language="deu">Erweiterung eines FD-FLIM Messsystems durch MQTT Anbindung eines Roboters zur automatischen Sortierung von Altholzklassen</title>
    <abstract language="deu">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.</abstract>
    <parentTitle language="deu">Tagungsband AALE 2025: Menschenzentrierte Automation im digitalen Zeitalter</parentTitle>
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