I have data of varying concentrations of a particular substance in water. I recorded its Raman signals between 1 ppt to 1 ppb using identical parameters.
I would be using Savitzky-Golay filtering to smooth out my data for preprocessing.
How should I go about using these data to automatically identify the peaks in the spectra?
Can I just perform Principal Component Regression between the spectra and the concentration and look at the `loadings`? Wouldn't that tell me the wavenumbers that have the highest variations between data points?
yes, it should do. Use Partial Least squares regression, or you could even use a supervised classical least squares analysis, extracting the contribution of your analyte spectrum at each concentration.
I agree that Partial Least Squares regression should be used. You may also consider first or second derivative preprocessing of the Raman spectra. In some cases nomalization of the spectra are required especially if you are using single beam Raman spectra.
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