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.