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Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy

  • 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.

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Author:Georgekutty Jose Maniyattu, Eldho Geegy, Nina Leiter, Maximilian Wohlschläger, Martin Versen, Christian Laforsch
DOI:https://doi.org/10.1109/SAS54819.2022.9881372
Parent Title (English):2022 IEEE Sensors Applications Symposium (SAS)
Document Type:Conference Proceeding
Language:English
Publication Year:2022
Tag:FLIM; fluorescence lifetime imaging; neural networks; plastic identification
Page Number:6
First Page:1
Last Page:6