Nondestructive microbial discrimination using single-cell Raman spectra and random forest machine learning algorithm

Raman microspectroscopy is a powerful tool for obtaining biomolecular information from single microbial cells in a nondestructive manner. Here, we detail steps to discriminate prokaryotic species using single-cell Raman spectra acquisitions followed by data preprocessing and random forest model tuning. In addition, we describe the steps required to evaluate the model. This protocol requires minimal preprocessing of Raman spectral data, making it accessible to non-spectroscopists, yet allows intuitive visualization of feature importance. For complete details on the use and execution of this protocol, please refer to Kanno et al. (2021).