Beyond High-Accuracy Mass Spectrometry: Why Chromatographic Retention Time Must Reclaim Its Role in Analyte Identification

Author(s)Olga Begou, Helen Gika, Christina Virgiliou

Μetabolomics enables the comprehensive profiling of small molecules in medicine, plant science, and systems biology. Its true value depends not on the number of detected features but on the reliability of metabolite identification and pathway analysis. Despite well-established guidelines, annotation and definitive identification are often conflated in practice. Simple matches in mass databases are frequently reported as identities, without comparison to standards or chromatographic evidence. This overstatement of confidence compromises validity and risks propagating errors into databases, pathway analyses, and AI-driven workflows. Mass spectrometry (MS) alone is rarely sufficient for identification and orthogonal evidence is essential. Chromatographic retention time is an underused but powerful descriptor reflecting molecular properties. When combined with MS it can provide plausibility checks and form the basis of Level 1 identification. Regulatory frameworks already require such combined criteria in targeted analysis. Systematic use of retention order, retention indices, and prediction models can filter implausible candidates and strengthen identification.

Metabolomics (metabolic phenotyping, or metabotyping) aims to catalogue and interpret the small molecule content of a biological system and study metabolite trends in concentrations that reflect the biochemical state of the studied cells, organs, or organisms. In practice, this means discovering small molecule metabolites, including lipids, and their patterns that discriminate between samples or groups of samples, to reveal differences between, for example, physiological and pathological states. Ultimately, these findings help scientists to generate new knowledge and understand the underlying or perturbed biochemical mechanisms of the topic of their investigation. If the topic of study is disease, this can lead to improved diagnosis or prognosis; if the study is on sports biochemistry, we can obtain a new understanding of the mechanism of energy depletion during physical exercise and the body’s mechanisms for replenishment, the mechanism of oxidative stress, and so on. For plants, we are interested in (among others), plant protection mechanisms, and the processes of plant and product growth. The true value of any untargeted metabolomics experiment depends not on the number of features detected during sample analysis, but on how the detected features can be translated into reliable metabolite identities and, further, how many of the metabolites are related to biochemical phenomena. Obviously, without trustworthy identification, any mechanistic interpretation becomes speculative, and the claimed biomarkers become little more than statistical artefacts.