Rapid Detection of Bacterial Infection Using Gas Phase Time Series Analysis

Quick identification of bacterial growth in culture is demonstrated using time series analysis of gas phase data collected by a commercial gas sensor. Clinically standard blood agar plates were inoculated with different concentrations of Escherichia coli and incubated at 37° C. BME688 gas sensors and read-out electronics were used to measure the gases emitted during bacterial growth. The time series show sigmoidal features, which appear at earlier time points with higher initial concentrations. A modified logistic model was found to fit the data well and confirms faster growth with higher concentrations. A data analysis algorithm was developed to identify the earliest time-point for bacterial growth detection; it revealed a logarithmic relationship between the initial seeding concentration and time for signal peaks. Concentration-dependent bacterial growth was identified at 2–6 hours, more than 90 % faster detection, compared to the current clinical protocols that take up to 72 hours.