Fourier transform infrared spectroscopy enables rapid strain typing and cluster analysis of Listeria monocytogenes under diverse growth conditions

using 118 strains representing diverse serotypes. FTIR showed high reproducibility, with tight clustering of spectral profiles from technical and biological replicates across multiple days, supporting its utility as a dereplication tool. Both hierarchical clustering and an artificial neural network classifier achieved 100% accuracy at the serogroup level, with an adjusted Rand index of 1.00 compared to conventional serotyping. However, FTIR lacked the resolution of whole-genome sequencing (WGS), likely due to spectral overlaps in polysaccharide-associated regions among clonal complexes and some serotypes (e.g., 1/2a, 1/2b, 1/2c). Despite this, FTIR could differentiate related and unrelated isolates in outbreak or persistence strain cluster analyses. Spectral profiles were significantly influenced by the bacteria growth conditions, with the strongest clustering coherence observed among isolates cultured on the same media. ALOA, Blood, and Oxford agar yielded the best classifier performance, whereas RAPID'L.mono produced the lowest scores. Incubation temperature and duration also influenced spectral quality and classification confidence. Lower temperatures (25 °C vs. 37 °C) enhancing serotype-level discrimination within serogroups, highlighting the importance of optimized culture protocols in improving the discriminatory power of FTIR-based strain typing. Overall, our data supports the use of FTIR as a rapid initial screening method for pathogen typing and persistence or outbreak-related cluster detection, helping prioritize isolates for confirmatory WGS. Expanding classifier databases and integrating FTIR with molecular methods could further improve its resolution and applicability for real-time pathogen typing.