Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level

Abstract

Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.