One of the major health challenges that humans have been facing for the last few decades is antimicrobial resistance (AMR), where antibiotics stop responding to infections and, thereby, take a considerable amount of time to cure them while increasing mortality rates. There are various steps that have been taken by researchers and organizations to identify, cure, and prevent this urgent issue. Here, in this review, we have tried to illustrate how machine learning has been used with different spectroscopic analytical platforms—Raman spectroscopy (RS), Fourier-transform infrared spectroscopy (FTIR), and nuclear magnetic resonance (NMR)—to further accelerate the understanding and early detection of AMR. The combination of ML algorithms with advanced spectroscopic techniques delivers faster and deeper insights into the different mechanisms of bacterial resistance, delivering novel solutions.

Raman spectroscopy and machine learning can help to identify a novel spectral marker associated with phosphoethanolamine modification in the lipid A moiety of colistin-resistant Gram-negative Escherichia coli

Raman spectroscopy (RS) is rapidly becoming a key analytical tool for a wide range of microbiology applications. In combination with diverse machine learning methods, RS has demonstrated potentials to be translated in form of a culture-free, rapid, and objective tool for identifying antimicrobial resistance (AMR). Colistin is regarded as the final line of defense antibiotic for treating infections caused by gram-negative bacteria. In this study, we have employed a combinatorial approach of machine learning and RS to identify a novel spectral marker associated with phosphoethanolamine modification in lipid A moiety of colistin resistant gram-negative Escherichia coli. The visible spectral fingerprints of this marker have been validated by partial least square regression and discriminant analysis. The origin of the spectral feature has been confirmed by hyperspectral imaging and K-means clustering of a single bacterial cell. The chemical structure of the modified lipid A moiety has been verified by gold standard MALDI-TOF mass spectrometry. Our findings support futuristic applicability of this spectroscopic marker in objectively identifying colistin-sensitive and resistant strains.