Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMS

Abstract

In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI- mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI-, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods. Graphical abstract Consensus workflow for combining fragmentation and retention prediction in LC-HRMS-based micropollutant identification.

An Integrated LC-MS-Based Strategy for the Quality Assessment and Discrimination of Three Panax Species

“…In Chinese folk medicine, PJ and PM are historically and generally used as a herbal medicine for similar indications. These two herbs possess combined medicinal effects, which are P. ginseng ’s “conserving vitality” activities and P. notoginseng ’s “replenishing blood” activities [ 10 ]. However, PJ and PM are considered to be two different herbal medicines, and are recorded as “zhujieshen” and “zhuzishen” in the Chinese Pharmacopoeia, respectively.…”

“…Combining UHPLC with Q-Exactive/HRMS has been increasingly used to screen and identify complex compounds in herbs and food products [ 8 , 9 ]. Furthermore, high-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS) is also a powerful tool for complex composition analysis for the quantitation and screening of specific chemicals in foods and plants [ 10 , 11 , 12 , 13 ]. The MS detector can provide information on molecular formula and fragmentation ions.…”

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

“…Another workflow included the use of multiple identification criteria such as accurate mass, retention time, MS/MS spectrum, and product/precursor ion intensity ratios to support reversed phase and HILIC based metabolic profiling [ 174 ]. Two in silico fragmenters and two retention prediction models were utilized to annotate hydrophobic compounds [ 175 ]. A tool for improved and automated adduct detection was discussed in [ 176 ], leading to 83% correct annotations of adduct ions.…”

High-resolution mass spectrometry to complement monitoring and track emerging chemicals and pollution trends in European water resources

“…In another study, a data set of 78 diverse known micropollutants analyzed by LC-HRMS was used to assess two different MS/MS fragmentation and two retention prediction approaches. To combine scores from these different candidate selection tools, consensus score values with optimal weights were calculated to show the contribution of each approach and whether the combination could improve candidate selection [40,41].…”