Photonic Data Science

1D data, such as time series, and spectroscopy contain rich information but pose challenges for machine learning, due to limited large, labeled datasets and absence of specialized pretrained neural networks. Existing 1D analysis methods often rely on traditional chemometric approaches and rarely exploit the full potential of online data augmentation, novel architectures, and explainability methods common in image analysis. To address these gaps, a novel approach is proposed that transforms 1D signals into 2D spider plot visualizations, enabling utilization of pretrained deep learning models originally developed for image datasets. The approach also allows transformation of model interpretation maps back to the original variable space, making them more intuitive. The general applicability of this method is demonstrated across multiple data types: Raman spectra, mid-infrared spectra, electrocardiograms, and mass spectrometry data(MALDI-IMS). The method achieves competitive performance, reaching a balanced accuracy of 99% in Raman-based oil identification tasks, surpassing principal component analysis combined with linear discriminant analysis (94%). Performance across datasets reflects variability due to data complexity, highlighting the method’s versatility and potential across diverse signal types. This visualization-based strategy presents an innovative solution to overcome dataset-size and model-related limitations while enhancing interpretability in complex 1D data analysis.