Nontargeted Volatile Metabolite Screening and Microbial Contamination Detection in Fermentation Processes by Headspace GC-IMS

Gas chromatography combined with ion mobility spectrometry (GC-IMS) is a powerful separation and detection technique for volatile organic compounds (VOC). This combination is characterized by exceptionally low detection limits in the low ppbv range, high 2-dimensional selectivity, and robust operation. These qualities make it an ideal tool for nontarget screening approaches. Fermentation broths contain a substantial number of VOC, either from the medium or produced by microbial metabolism, that are currently not regularly measured for process monitoring. In this study, Escherichia coli, Saccharomyces cerevisiae, Levilactobacillus brevis, and Pseudomonas fluorescens were exemplarily used as model organisms and cultivated, and the headspace was analyzed by GC-IMS. Additionally, mixed cultures for every combination of two of the microorganisms were also characterized. Multivariate data analysis of the GC-IMS data revealed that it is possible to differentiate between the microorganisms using PLS-DA with a prediction accuracy of 0.92. The mixed cultures could be separated from the pure cultures with accuracies between 0.87 and 1.00 depending on the organism. GC-IMS data correlate with the optical density and can be used to follow and model growth curves. The root mean squared errors ranged between 10 and 20% of the maximum value, depending on the organism. Peak identification with reference compounds did not reveal specific marker compounds, rather the pattern was found to be responsible for the model performance.