Hyperspectral imaging with Raman scattered photons: A new paradigm in Raman analysis

TL;DR

This paper introduces a novel hyperspectral imaging technique using Raman scattered photons on a patterned substrate, enabling spatially resolved chemical imaging with enhanced specificity and automation for analyte detection.

Contribution

It presents a new hyperspectral Raman imaging method leveraging a hierarchical substrate and machine learning for automated, spatially resolved chemical analysis.

Findings

Successful Raman imaging of dyes and glucose detection

Enhanced spatial resolution and specificity in chemical imaging

Automation improves detection speed and reduces human error

Abstract

Surface enhanced Raman spectroscopy, is a technique of fundamental importance to analytical science and technology where the amplified Raman spectrum of analytes is used for chemical fingerprinting. Here, we showcase an engineered hierarchical substrate in which the plasmonically active regions are restricted to a micron scale, 2D hexagonal pattern. The Raman signal enhancement of any analyte uniformly coating the substrate is consequently bears a high registry with the 2D pattern. This spatially segregated enhancement allows optical imaging of the 2D pattern solely using the Raman scattered photons from the analyte. While pattern brightness and contrast determine analyte identification and detection sensitivity, the spectrally selective contrast allows for tuning specificity. Conceptual proof of the technique is demonstrated via the acquisition of Raman images with rhodamine and fluorescein and finally applied to detect glucose in 40 mM concentration. The large area imaging and the inherent requirement of spatial uniformity for positive detection implemented using a machine learning based automated pattern recognition protocol increases the statistical confidence of analyte detection. This simultaneously multisite signal detection sacrifices continuous spectral information at the cost of speed, reproducibility and human error via automation of detection of the hyperspectral imaging technique presented here.