# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 5 Discussion  
In this study, we proposed a general network framework for three-branch feature fusion based on spectral pair comparison. This framework realizes three-branch feature fusion using Raman spectral pair comparison technology. It fully considers the correlation between spectral pairs and captures the connection between local and global features of the Raman spectrum with a multi-head self-attention mechanism. Additionally, a mixed dataset was adopted to evaluate its performance. Experimental results show that, compared with U-Net, ResNext, DeepRaman, and RamanNet methods, this framework achieves higher accuracy in both qualitative identification and quantitative analysis. Therefore, the general network framework proposed in this study is a promising technique for component identification in Raman spectra. In comparison to previous works, such as DeepRaman, which primarily extracts features from individual spectra for pairwise comparison, our study places a stronger emphasis on identifying internal feature differences within the spectra to improve the accuracy of component identification in complex mixtures. Specifically, while DeepRaman processes two spectra simultaneously, it focuses on extracting features from each spectrum independently without fully leveraging the interactions between them. In contrast, our three-branch feature fusion network introduces a middle branch designed to integrate and fuse the features extracted from the other two branches. This additional branch enables the  
Figure 6: Heatmaps rendered by different models. The \(x\)-axis represents the Raman wavelength, while the \(y\)-axis represents the amplitude. Heatmaps are colored based on each model’s interpretation of feature importance, with red indicating areas of high importance and blue indicating areas of low importance. From top to bottom: (**a**) UNet, (**b**) ResNext, (**c**) DeepRaman, (**d**) RamanNet, (**e**) Our Method. Notice how our model highlights key spectral regions, providing better interpretability and focus on relevant features. network to better capture and combine the hidden information present between spectral pairs, enhancing the model's ability to discern subtle correlations that may be critical for accurate mixture analysis. The three-branch architecture, combined with a multi-head self-attention mechanism, allows our model to capture both local spectral features and global interactions more effectively than traditional methods. This approach not only improves the model's sensitivity to small but significant differences in spectral data but also enables a more comprehensive understanding of the relationships between the spectra. To our knowledge, such a framework for spectral pairwise comparison and feature fusion has not been previously applied in this context, making it a novel contribution to the field. Additionally, our use of data augmentation techniques--including Gaussian noise addition and DCGAN--addresses the challenge of limited data availability, which is a common issue in Raman spectroscopy research. By expanding the training dataset and introducing greater variability, we improve the model's robustness and generalization capabilities, allowing it to perform well on diverse and complex real-world samples. However, the algorithm does have some limitations. The multi-branch architecture and attention mechanisms significantly increase computational complexity [40], which may limit its practicality for real-time applications or deployment in resource-constrained environments. Additionally, although synthetic data generation via DCGAN improves the diversity of the training dataset, there is a risk that the model could overfit to these synthetic samples if they do not accurately reflect real-world conditions [41]. This reliance on synthetic data could potentially reduce the model's performance when applied to actual experimental data.