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WTSynNet: a lightweight cooperative network for multi-species Raman spectral classification.

Zhishun Huang1,2, Ri-Gui Zhou1,2, Pengju Ren1,2

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. rgzhou@shmtu.edu.cn.

Analytical Methods : Advancing Methods and Applications
|October 1, 2025
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Summary

A new lightweight framework, WTSynNet, efficiently identifies animal biological fluids using Raman spectroscopy. This model achieves high accuracy with minimal parameters, offering rapid analysis for forensic and veterinary applications.

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Area of Science:

  • Biochemical analysis
  • Spectroscopy
  • Machine learning

Background:

  • Animal blood and semen analysis is crucial for forensics, diagnostics, and traceability.
  • Raman spectroscopy offers non-destructive molecular fingerprinting for body fluid identification.
  • Challenges exist in efficient feature extraction for imbalanced, multiclass spectral data.

Purpose of the Study:

  • To develop a computationally efficient and accurate framework for analyzing animal biological fluid Raman spectra.
  • To address the challenge of feature extraction in imbalanced multiclass classification scenarios.
  • To create a lightweight model with strong generalization capabilities.

Main Methods:

  • Proposed WTSynNet, a lightweight framework integrating one-dimensional wavelet convolution (WTConv1d) and a star operation mechanism.
  • Employed efficient multiscale feature learning for Raman spectral data.
  • Validated on animal blood and semen Raman spectral datasets and a cross-domain marine pathogen dataset.

Main Results:

  • WTSynNet achieved over 98% classification accuracy on animal fluid datasets.
  • The model utilizes fewer than 0.3 million parameters, demonstrating high efficiency.
  • Demonstrated strong performance and robustness on a cross-domain marine pathogen dataset, indicating adaptability.

Conclusions:

  • WTSynNet is a compact and powerful model for rapid on-site Raman spectral analysis.
  • The framework shows excellent generalization capability across different spectral datasets.
  • Offers a promising solution for efficient and accurate identification of biological constituents.