Identification of plastics in foodstuff using Rapid-FLIM and neural networks
- 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.
| Author: | S. Heitzmann, A. S. Rajan, M. Versen, N. Leiter |
|---|---|
| DOI: | https://doi.org/10.5162/SMSI2025/P30 |
| Parent Title (English): | SMSI 2025 Conference – Sensor and Measurement Science International |
| Document Type: | conferencepaper |
| Language: | English |
| Publication Year: | 2025 |
| Conference: | SMSI 2025 (Nürnberg) |
| Release Date: | 2025/08/06 |
| Page Number: | 2 |
| First Page: | 290 |
| Last Page: | 291 |
