An Effective Deep Learning Model for Surface-Enhanced Raman Spectroscopy Detection Using Artificial Neural Network

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for molecular sensing and has gained significant attention due to its high sensitivity and selectivity. SERS based on deep learning technology have been used in this study of materials, biological recognition, food safety, and intelligence. Deep learning techniques have shown tremendous potential in various scientific fields, including spectroscopy-based detection methodologies. In this study, we propose an effective deep learning approach for SERS detection using an artificial neural network (ANN). Then, the results are compared using two datasets batch1 and batch2. This study used Rhumamine 6G (R6G) as an aim molecule in this study. The experimental develops show the effectiveness of proposed ANN