Low resolution Raman: the impact of spectral resolution on limit of detection and imaging speed in hyperspectral imaging

Abstract

The majority of problems in analytical Raman spectroscopy are mathematically over-determined, where many more spectral variables are measured than analytic outputs (such as chemical concentrations) are calculated. Thus, to improve spectral throughput and simplify system design, some researchers have explored the use of low resolution Raman systems for cell or tissue classification, achieving accuracy independent of spectral resolution. However, the tradeoffs inherent in this approach have not been systematically studied. Here, we theoretically and experimentally explore the relationship between spectral resolution and analytical error. We show that decreased spectral resolution leads to spectral signal-to-noise ratio and therefore more reliable results and lower limits of detection for equivalent integration times in blind unmixing of hyperspectral images. Our theoretical analysis demonstrates that the primary benefit of low resolution Raman spectroscopy is in overcoming detector noise (such as thermal or electronic noise). Therefore, the benefits are most pronounced when utilizing lower-grade, uncooled detectors. Therefore, using a low-cost CMOS camera we experimentally demonstrate the ability of low resolution Raman spectroscopy to achieve substantially improved imaging performance compared to fully-resolved Raman spectral imaging, paving the way for cost-effective, pervasive Raman spectroscopy.