# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 2 Dataset and Preprocessing
### Data Augmentation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Step** & **Description** & **Number of Data Entries** & **Comments** \\ \hline Initial Data Collection & Collection of Raman spectra using self-built spectrometer & 830 Raman spectra & Provides the foundation dataset; limited data availability due to equipment and resource constraints. \\ \hline Linear Superposition & Superposition of different pure substances’ Raman & 10,000 types & Increases data diversity significantly, but there is a risk of creating unrealistic spectral combinations, leading to potential artifacts. \\ \hline Preprocessing Transformation & Random shifting of Raman spectra & Expanded from 10 to 50 spectra per mixture & Shifting increases variability in the dataset, which improves model robustness; however, excessive shifting might deviate too far from real-world conditions. \\ \hline Gaussian Noise Addition & Addition of Gaussian noise with varying intensities & Expanded from 50 to 100 spectra per mixture & Introducing noise enhances model generalization and simulates practical conditions, but it may also introduce artifacts that could reduce prediction accuracy if not controlled properly. \\ \hline DCGAN Data Generation & Generation of synthetic Raman spectra using DCGAN & (\(38+45+10\),000) \(\times\) 10 entries & DCGAN-generated data increase both size and diversity of the dataset, aiding model training; a potential limitation is the generation of synthetic samples that may not accurately represent real-world scenarios, leading to overfitting. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data augmentation process and dataset expansion for Raman spectroscopy. of Gaussian noise is a common practice in generating augmented Raman data [32], as it helps simulate realistic experimental conditions. While this step is standard in data preprocessing for Raman spectroscopy, it is not the main focus of our study. Instead, it serves as a foundational step to improve the dataset quality and enhance the model's ability to handle real-world variability. This helps the model become more robust to slight variations and improves its ability to generalize beyond the training set, reducing the risk of overfitting. The amplitude of the Gaussian noise was chosen based on empirical testing to reflect realistic levels of noise that are typically present in Raman spectroscopy. We experimented with various noise levels and selected an amplitude that introduced sufficient variation without significantly distorting the underlying signal, ensuring that the augmented data remained representative of actual measurements. As shown in Figure 2, DCGAN is trained using data augmented by traditional augmentation method. After about 800 iterations, the simulated data generated by the generator not only retain the characteristics of the original data but also expand the diversity of data. In other words, after using traditional augmentation methods and then feeding it into the DCGAN network for enhancement, the resulting Raman spectrum data improve the diversity while retaining their original characteristics. Figure 2: _Cont._