An exometabolomics approach to monitoring microbial contamination in microalgal fermentation processes by using metabolic footprint analysis

The early detection of microbial contamination is crucial to avoid process failure and costly delays in fermentation industries. However, traditional detection methods such as plate counting and microscopy are labor-intensive, insensitive, and time-consuming. Modern techniques that can detect microbial contamination rapidly and cost-effectively are therefore sought. In the present study, we propose gas chromatography-mass spectrometry (GC-MS)-based metabolic footprint analysis as a rapid and reliable method for the detection of microbial contamination in fermentation processes. Our metabolic footprint analysis detected statistically significant differences in metabolite profiles of axenic and contaminated batch cultures of microalgae as early as 3 h after contamination was introduced, while classical detection methods could detect contamination only after 24 h. The data were analyzed by discriminant function analysis and were validated by leave-one-out cross-validation. We obtained a 97% success rate in correctly classifying samples coming from contaminated or axenic cultures. Therefore, metabolic footprint analysis combined with discriminant function analysis presents a rapid and cost-effective approach to monitor microbial contamination in industrial fermentation processes.