In this tech tip, Ocean Insight offers suggestions for making sense of the sharp peaks and jagged shapes you’ll find in Raman spectra. Raman signatures can be used as is or matched to a known database for materials identification.
Once considered too advanced in theory and application for most users, Raman spectroscopy today encompasses instruments from simple handheld devices to more sophisticated benchtop systems, with Raman data analysis strategies ranging from careful application of a few key principles to more automated approaches using chemometrics and machine learning.
Raman Analysis at a Glance
Raman peak signatures offer highly repeatable fingerprints for each analyte that can be deconvoluted via any number of numerical methods. This allows detection of complex mixtures and real-world samples that often are quite “messy” versus an idealized laboratory sample.
Although Raman is advanced as a spectral technique, it’s not difficult or overly complicated. What makes Raman tricky is that the things you care about are mixed in with a bunch of other spectral features. For example, in traditional spectroscopy, you can analyze a transmissive absorbance plot (i.e., dye in a cuvette) or a material reflection plot, and easily pick out broad humps and intersecting isosbestic points and explain them. But in Raman spectra, there’s just a seemingly random topography of spectral features.
Indeed, most users have observed Raman spectra that have peaks and jagged shapes everywhere. Where do these features come from? Well, they’re from all sorts of things: residual sample, pixel-to-pixel response, and more. It’s best to not overthink it, honestly. These are the key questions to ask:
- What is changing to a statistically significant degree when I’m adding the sample I’m measuring versus some blank (reference)?
- How can I process the data to repeatably visualize and quantify this?
Fortunately, there is a limited set of tools to run your data through.