I'm working on classification of two classes of Raman spectra. And while I was working on finding the optimal steps for pre-processing, I started to wonder if it is really necessary.
I have a lot of Raman spectra data and and most of them differ between both classes even to the naked eye (characteristic peaks or the lack of). While I do have many datasets I know were not recorded under exact same conditions, I also have a few sets for which exact same conditions apply for both classes. They still look obviously different from each other. Since my primary goal is to develop a robust algorithm with an unsupervised classificator, I now start to wonder:
Assuming the setup and thus noise and any sort of distortion is the same in both classes, even if dominant, do I need to bother with pre-processing when various classifiation models already return satisfying results with the raw data?
Most (if not all) papers I read about Raman spectra pre-processing (there's a lot on that topic) gave me the impression, the authors are always interested in separating the Raman spectrum of interest from all the unwanted other signals (fluorescent background, white noise, cosmic spikes etc). While this certainly would be interesting, I actually only want to sort them.
I'm asking this question from a rather hypothetical standpoint. I know that in reality you can hardly guarantee exact same conditions (especially with random occurrences like cosmic spikes).