Tailored SONAR-MSI: Converting SONAR-MS Data into Pseudoimages for Deep-Learning-Based Natural Products Analysis

species were accurately classified using a SONAR-MSI-based CNN model, which achieved 100% accuracy, surpassing the performance of feature-table-based models (91.4%). Furthermore, the pixel-wise structure of SONAR-MSI allows interpretable mapping of metabolites to image coordinates, supporting both visualization and annotation. These findings establish SONAR-MSI as a robust and scalable approach for DL-assisted metabolomics, enabling efficient and information-rich NP analysis.