Chris Beecher, Felice A. de Jong, IROA Technologies
ABSTRACT: The lack of reproducible data due to matrix effects in untargeted metabolomics has been an on-going major concern. Ion suppression, interference from coeluted chemical components (sample matrix, solvent or LC-MS system components), is often observed as the loss of analyte ionization response and negatively affects precision, accuracy, and reproducibility (over- or underestimation of analyte levels). The optimization and validation of quantitative LC-MS methods relies on the assessment and correction of ion suppression. We conducted a simple experimental to understand the level of suppression of compounds in a common human plasma sample that had been serially diluted. Once the impact of ion suppression was assessed and quantified, we developed a workflow to minimize variability and to correct the ion suppressed data which then could be effectively normalized to achieve reproducible measurements. Why work up bad data?