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

Animal blood and semen contain diverse biochemical constituents that are of great importance in forensic science, veterinary diagnostics, and species traceability. Raman spectroscopy has emerged as a powerful tool for body fluid identification owing to its non-destructive and rapid acquisition of molecular vibrational fingerprints. However, achieving a balance between discriminative feature extraction and computational efficiency remains a challenge, particularly in imbalanced multiclass scenarios. To address this issue, we propose WTSynNet, a lightweight framework that integrates a one-dimensional wavelet convolution module (WTConv1d) with a star operation mechanism to enable efficient multiscale feature learning. Experiments on animal blood and semen Raman spectral datasets demonstrate that WTSynNet attains over 98% classification accuracy with fewer than 0.3 M parameters, while maintaining extremely low inference latency and memory usage. Moreover, the model achieves strong performance on a cross-domain marine pathogen Raman dataset, underscoring its robustness and adaptability. These results indicate that WTSynNet is a compact yet powerful model with strong generalization capability and holds broad potential for future applications in rapid on-site Raman spectral analysis.