TY  - CPAPER
U1  - Konferenzpaper
A1  - Heitzmann, S.
A1  - Rajan, A. S.
A1  - Versen, M.
A1  - Leiter, N.
T1  - Identification of plastics in foodstuff using Rapid-FLIM and neural networks
T2  - SMSI 2025 Conference – Sensor and Measurement Science International
N2  - The analysis of fluorescence lifetimes has the potential to reliably differentiate plastics from foodstuffs. A setup for use in an industrial process is required to work in real time. The combination of Rapid fluorescence lifetime imaging microscopy (Rapid-FLIM) and neural networks shows significant improvements in capture time compared to other FLIM technologies, while achieving a reliable differentiation of foodstuffs and plastics.
AB  - The analysis of fluorescence lifetimes has the potential to reliably differentiate plastics from foodstuffs. A setup for use in an industrial process is required to work in real time. The combination of Rapid fluorescence lifetime imaging microscopy (Rapid-FLIM) and neural networks shows significant improvements in capture time compared to other FLIM technologies, while achieving a reliable differentiation of foodstuffs and plastics.
Y1  - 2025
U6  - https://doi.org/10.5162/SMSI2025/P30
DO  - https://doi.org/10.5162/SMSI2025/P30
SP  - 290
EP  - 291
S1  - 2
ER  -