Abstract
We present a real-time terahertz (THz) plastic classification system based on rotating frequency-selective surfaces (FSSs) and supervised machine learning. The system achieves an average accuracy of 95% across seven polymer types, with prediction times under one second. The analysis confirms robust feature learning and reliable discrimination of chemically similar polymers, demonstrating strong potential for practical recycling and sorting applications.
| Original language | English |
|---|---|
| Journal | Photonics North, PN |
| Issue number | 2026 |
| DOIs | |
| Publication status | Published - 2026 |
| Event | 2026 Photonics North, PN 2026 - Quebec City, Canada Duration: 2 Jun 2026 → 5 Jun 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 12 Responsible Consumption and Production
!!!Keywords
- Terahertz
- multispectral spectroscopy
- plastic recycling
- real-time sensing
- supervised learning
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