Additionally, during LC-MS ionization, a metabolite can generate multiple ion species (e.g. adducts like [M+H] + , [M+Na] + or [M+K] + ). Multiple HRMS 1 -MS 2 feature pairs are obtained for a metabolite. This can lead to unwanted separation of molecular families (subnetworks) and limits library annotations across MN. The development of Ion Identity Molecular Networking (IIMN) addressed this by merging features of identical molecules into groups (Ion Identity Networks, IIN) based on MS 1 feature peak shape correlation. This simplifies comparisons and enhances metabolite composition representation . The integration of machine learning-driven in-silico annotation tools has proven highly effective for characterizing metabolites across NPs exploration . Employing a combination of diverse annotation approaches and strategies like IIMN offers better coverage across the chemical space of the samples leading to a more comprehensive description.