Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, 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 a study published in Talanta, a research group led by LI Bei from the Changchun Institute of Optics and Precision Mechanics and Physics (CIOMP) of the Chinese Academy of Sciences(CAS) proposed multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level.

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.

The results showed that the recall rate for all bacteria in the five-point scanning spectral data was higher than that of the single-point spectral data. The overall recognition accuracies of 94.1±2.6% and 97.6±1.7% for the single-point spectral data and the five-point scanning spectral data were improved by approximately 3.7% for the five-point scanning spectral data.

Multi-point scanning confocal Raman spectroscopy can fully characterise the full range of biochemical component information within a cell, making Raman spectroscopy a true 'fingerprint spectrum' of individual cells. Combined with a wide range of statistical analysis methods, this enables the creation of a more reliable database of microbial Raman spectra.