Aims: The efficacy of ambient mass spectrometry to identify and serotype Legionella pneumophila was assessed. To this aim, isolated waterborne colonies were submitted to a rapid extraction method and analysed by direct analysis in real-time mass spectrometry (DART-HRMS).
Methods and results: The DART-HRMS profiles, coupled with partial least squares discriminant analysis (PLS-DA), were first evaluated for their ability to differentiate Legionella spp. from other bacteria. The resultant classification model achieved an accuracy of 98.1% on validation. Capitalising on these encouraging results, DART-HRMS profiling was explored as an alternative approach for the identification of L. pneumophila sg. 1, L. pneumophila sg. 2-15 and L. non-pneumophila; therefore, a different PLS-DA classifier was built. When tested on a validation set, this second classifier reached an overall accuracy of 95.93%. It identified the harmful L. pneumophila sg. 1 with an impressive specificity (100%) and slightly lower sensitivity (91.7%), and similar performances were reached in the classification of L. pneumophila sg. 2-15 and L. non-pneumophila.
Conclusions: The results of this study show the DART-HMRS method has good accuracy, and it is an effective method for Legionella serogroup profiling.
Significance and impact of the study: These preliminary findings could open a new avenue for the rapid identification and quick epidemiologic tracing of L. pneumophila, with a consequent improvement to risk assessment.
Keywords: ambient mass spectrometry; classification model; machine learning; predictions; supervised statistical analysis.
© 2021 The Society for Applied Microbiology.