Multi-Scale Feature Attention Network for Polymer Classification using THz Dual-Comb Spectroscopy
New research proposes MSFAN deep learning architecture, combined with terahertz dual-comb spectroscopy, to classify 12 types of polymers with 85.2% accuracy, promising to improve plastic recycling sorting efficiency.
[2606.06554] Multi-Scale Feature Attention Network for Polymer Classification using THz Dual-Comb Spectroscopy
[Submitted on 4 Jun 2026]
Title:Multi-Scale Feature Attention Network for Polymer Classification using THz Dual-Comb Spectroscopy
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Abstract:Reliable polymer identification is essential for ensuring the quality and safety of recycled plastics, yet conventional sorting and spectroscopic techniques often struggle to deliver robust discrimination. Terahertz Dual-Comb Spectroscopy (THz-DCS) offers a promising alternative, providing rapid, high-resolution, and non-destructive measurements. In this work, we leverage THz-DCS to classify 12 types of polymers, including pure polymers, multilayer films, commercial blends, and biopolymers. To handle the complexity of these spectral signals, we propose the Multi-Scale Feature Attention Network (MSFAN), a novel deep learning architecture tailored for THz-DCS data. The framework integrates feature gating for signal recalibration and multi-scale parallel convolutions to capture diverse frequency patterns. These features are further refined through cross-feature attention and attention pooling, enabling the model to intrinsically highlight the most informative THz regions. MSFAN consistently outperforms state-of-the-art models, reaching a classification accuracy of 85.2%. This study demonstrates the potential of combining THz-DCS with deep learning techniques for effective, scalable, and interpretable polymer classification.
Comments: Accepted in EUSIPCO'26
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.06554 [cs.LG]
(or arXiv:2606.06554v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.06554
arXiv-issued DOI via DataCite
Submission history
From: Ilán Carretero [view email] [v1] Thu, 4 Jun 2026 09:42:23 UTC (599 KB)
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