Integration of Raman Spectroscopy and Python-based Data Analysis for Advancing Neurobiological Research

The field of Raman spectroscopy continues to expand into biological applications due to its usefulness as a non-invasive technique that can be utilized qualitatively and quantitatively. However, the inherent weakness of Raman scattering leads to the need for each collected spectra to undergo a preprocessing step to remove noise, background drift, and cosmic rays. Biological research in particular needs large datasets due to the increased variability in samples. As datasets grow, the need to perform preprocessing on each individual spectra becomes daunting. Often, these steps are done by hand with the help of specialized software programs. Preprocessing can be accelerated by using computer algorithms to automatically correct all spectra at once, but they are constrained by their accuracy while corrections done by hand introduce a new variable into the dataset time-consuming. Many algorithms exist to assist in spectral preprocessing, but these are mainly designed around ‘ideal’ samples with strong Raman signal and limited complexity. Therefore there is a need to design more open-source programs to accurately and efficiently preprocess large Raman datasets in order to advance biological Raman research. This research utilizes a Python-based approach to biological Raman data analysis for a variety of biological studies, including live cell analysis and neurological development.