Monitoring alcoholic fermentation: an untargeted approach.

This work describes the utility and efficiency of a metabolic profiling pipeline that relies on an unsupervised and untargeted approach applied to a HS-SPME/GC-MS data. This noninvasive and high throughput methodology enables "real time" monitoring of the metabolic changes inherent to the biochemical dynamics of a perturbed complex biological system and the extraction of molecular candidates that are latter validated on its biochemical context. To evaluate the efficiency of the pipeline five different fermentations, carried on a synthetic media and whose perturbation was the nitrogen source, were performed in 5 and 500 mL. The smaller volume fermentations were monitored online by HS-SPME/GC-MS, allowing to obtain metabolic profiles and molecular candidates time expression. Nontarget analysis was applied using MS data in two ways: (i) one dimension (1D), where the total ion chromatogram per sample was used, (ii) two dimensions (2D), where the integrity time vs m/z per sample was used. Results indicate that the 2D procedure captured the relevant information more efficiently than the 1D. It was also seen that although there were differences in the fermentation performance in different scales, the metabolic pathways responsible for production of metabolites that impact the quality of the volatile fraction was unaffected, so the proposed pipeline is suitable for the study of different fermentation systems that can undergo subsequent sensory validation on a larger scale.