The processing of the UHPLC-HRMS 2 metabolite profiling data, construction of the II-FBMN, and subsequent obtention of different putative annotations enabled the assessment of the occurrence of metabolites in the samples, as well as the generation of an overview of the chemical space within the data set. The application of IIMN regrouped the 16,139 nodes, and 13,672 nodes into 14,000 and 10,500 neutral molecules for PI and NI, respectively. While the different annotation workflows are done based on the feature number, the Sirius workflow considered the IIMN results to increase the annotation consistency . Overall, PI mode consistently yielded higher annotation rates compared to NI. Based on the GNPS library search results, the annotation coverage was approximately 11% for PI and 1.5% for NI. In relation to the in-silico metabolite annotation workflows, the ISDB pipeline achieved annotations of 49% for PI and 40% for NI. Meanwhile, when employing Sirius, the MF prediction coverage stood at 8% for PI and 5% for NI. For structural annotations, Sirius covered 62% in PI and 13% in NI. The numerical results are summarized in Table .