Rapid identification of marine microorganisms is critical in marine ecology, and Raman spectroscopy is a promising means to achieve this. Single cell Raman spectra contain the biochemical profile of a cell, which can be used to identify cell phenotype through classification models. 

However, traditional classification methods require a substantial reference database, which is highly challenging when sampling at difficult-to-access locations. In this scenario, only a few spectra are available to create a taxonomy model, making qualitative analysis difficult. And the accuracy of classification is reduced when the signal-to-noise ratio of a spectrum is low. 

In a study published in Talanta, a research group led by Prof. LI Bei and his Doctoral student LIU Bo from Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP) of the Chinese Academy of Sciences (CAS) proposed a novel method for categorizing microorganisms that combines optical tweezers Raman spectroscopy, Progressive Growing of Generative Adversarial Nets (PGGAN), and Residual network (ResNet) analysis.

Experimental validations show that the method enhances machine learning classification accuracy while also reducing the demand for a considerable amount of training data, both of which are advantageous for analyzing Raman spectra of low signal-to-noise ratios. 

This method is envisaged that when combined with microfluidics, it would allow for fast, accurate, and non-invasive cell sorting. Importantly, this approach doesn’t need complicated sample treatments and thus holds great potential for challenging in-situ investigations, such as microbial identification and sorting, benefitting a wide range of microbiology and healthcare areas.