Unsupervised data driven approaches to Raman imaging through a multimode optical fiber

Raman spectroscopy is a label-free, chemically specific optical technique that provides detailed information about the chemical composition and structure of the excited analyte. Because of this, there is growing research interest in miniaturizing Raman probes to reach deep regions of the body. Typically, such probes utilize multiple optical fibers to act as separate excitation/collection channels with optical filters attached to the distal facet to separate the collected signal from the background optical signal from the probe itself. Although these probes have achieved impressive diagnostic performance, their use is limited by the overall size of the probe, which is typically several hundred micrometers to millimeters. Here, we show how a wavefront shaping technique can be used to measure Raman images through a single, hair-thin multimode fiber. The wavefront shaping technique transforms the tip of the fiber to a micrometer spatial resolution Raman microscope. The resultant Raman images were analyzed with a variety of state-of-the-art statistical techniques, including principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and k-means clustering. Our data-driven approach enables us to create Raman images of microclusters of pharmaceuticals using fingerprint region Raman spectra through a standard silica multimode optical fiber.