Data-driven exploration of electronic nose technology to differentiate bacteria in blood cultures under biofilm-promoting conditions.

Biofilms are a major cause of delayed wound healing, yet current biofilm identification methods are limited by invasiveness, processing times, or specificity. This study investigates the potential of metal-oxide electronic noses for identifying bacterial cultures of Staphylococcus aureus, Pseudomonas aeruginosa, Enterococcus faecium, and Staphylococcus epidermidis grown in blood-based growth medium. We conducted in-vitro experiments to capture volatilome signatures from cultures grown under biofilm-promoting conditions and analyzed data using an interpretable machine learning workflow to disentangle algorithmic limitations from biological variability. This workflow incorporated feature extraction and selection, correlation-based clustering, and Shapley analysis. Six classification models were evaluated using cross-validation. Considering all five classes, classification accuracy reached at most 55.6%, which Shapley-based interpretation attributed mainly to biological factors: E. faecium and S. aureus exhibited high signal similarity to control samples and strong inter-day variability. Accuracy increased to 100.0% for species with distinct volatile signatures, and dimensionality reduction resulted in a model using two constructed features. These findings demonstrate that classification performance in biological sensing cannot be explained solely by algorithmic factors. Interpretable machine learning workflows help for distinguishing biological sources of complexity from algorithmic ones. We provide a proof-of-principle for electronic nose-based identification of bacterial growth under biofilm-promoting conditions.