# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 1 Introduction  
Raman spectroscopy technology, as a powerful tool for revealing the internal structure of matter, relies on analyzing information on various vibration frequencies and vibration energy levels acquired through capturing the Raman scattering phenomenon generated by the sample [1]. This information serves as the \"code\" within the molecule, enabling scientists to identify its functional groups and obtain the molecular \"fingerprint\", characterized by distinct peaks [2]. The uniqueness of these characteristic peaks gives each substance its own unique \"identity\", which provides an important basis for substance identification and analysis [3]. One key advantage of Raman spectroscopy is its non-destructive analysis capability, allowing rapid in situ identification without altering the sample, making it particularly suitable for sensitive samples. Compared to force spectroscopy [4], which is typically used to probe the mechanical properties of biomolecules at the nanoscale, Raman spectroscopy offers a more direct method for molecular identification by providing unique vibrational \"fingerprints\" that correspond to the specific chemical bonds and functional groups present in a substance [5]. On the other hand, scanning near-field infrared microscopy (SNIM) can achieve high spatial resolution to investigate surface characteristics, but it lacks the specificity and convenience of Raman spectra for chemical identification in a wide range of materials [6]. Thus, Raman spectroscopy's combination of non-destructiveness, specificity, and convenience makes it a preferred choice in many applications, such as pharmaceuticals [7], environmental monitoring [8], and food safety [9]. However, when it comes to identifying components within complex mixtures, traditional spectral matching methodsoften fall short, particularly when the spectra overlap. This limitation poses significant challenges in critical applications, such as detecting contaminants in food products, ensuring the purity of pharmaceutical compounds, and monitoring environmental pollutants. During the development of Raman spectroscopy technology, spectral matching combined with databases has been the dominant method for identifying unknown samples [10]. There are several prevailing methods for this purpose, such as combining hit-quality-index-based spectral matching and voting [11], spectral linear kernel [12], and principal-component-based searching. Essentially, these algorithms determine the similarity between Raman spectra by comparing the measured spectrum with each spectrum in a database using techniques such as correlation, Euclidean distance, least-squares, etc. [13]. However, they are only suitable for analyzing the spectra of pure compounds and are ineffective for identifying mixtures [14]. Therefore, there is an urgent need to develop algorithms for identifying the Raman spectra of mixture components. With the rise of the deep learning, researchers began to incorporate deep learning into Raman spectroscopy technology [15]. For example, deep neural networks are used for spectrum reconstruction [16], acceleration of Raman imaging [17], component identification [18], etc. Deep learning realizes automatic feature extraction and learning of complex data through a multi-level neural network structure [19]. However, the existing Raman spectrum classification models often focus on the characteristics of each spectrum [12] and ignore the correlation between the spectrum pairs [20], or they fuse two spectra in the shallow layer of the network model [21], or simply ignore the relationship between the pairs from the very beginning [22]. These models have the potential to ignore the local characteristics of each spectrum to a certain extent. Given the growing demand for accurate and efficient mixture analysis [11], this study proposes a novel deep-learning-based approach designed to enhance the precision of Raman spectroscopy in identifying multiple components within a mixture.