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Development of a Neural Network for an Automated Differentiation of Plastics using Rapid-FLIM

  • 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%.

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Author:Amal Thomas, Shaif Saleem, Nina Leiter, Maximilian Dietlmeier, Maximilian Wohlschläger, Martin Versen, Christian Laforsch
DOI:https://doi.org/10.1364/ES.2023.EW4E.5
Parent Title (English):Optica Sensing Congress 2023
Document Type:Conference Proceeding
Language:English
Publication Year:2023
Tag:Fluorescence lifetime imaging; Laser sources; Neural networks; Phase shift; Positron emission tomography; Raman spectroscopy
Article Number:EW4E.5