The key role of hydrophobicity in the determination of pharmaceuticals by liquid chromatography–electrospray ionization-mass spectrometry under the interference of natural organic matter

Environmental science and pollution research international(2022)

Cited 0|Views13
No score
Abstract
The determination of trace-level pharmaceuticals in water is generally performed using liquid chromatography combined with mass spectrometry, which is susceptible to interference from non-target substances, such as natural organic matter (NOM). In this study, the interference of NOM on the determination of 20 typical pharmaceuticals using solid-phase extraction followed by ultra-performance liquid chromatography–electrospray ionization-triple quadrupole mass spectrometry (UPLC–ESI-tqMS) was investigated with a combined consideration of recoveries, matrix effects, and process efficiencies. The results showed that the recoveries of most pharmaceuticals were not significantly affected by NOM concentrations of 1–50 mg/L. The matrix effects and process efficiencies decreased linearly with increasing logarithmic NOM concentrations, and the changes in matrix effects and process efficiencies both exhibited negative linear correlations with the pharmaceuticals’ hydrophobicity (log K ow ). This result indicated that the determination of hydrophilic pharmaceuticals suffered from more severe NOM interference, as NOM entered the ESI source together with hydrophilic pharmaceuticals after UPLC separation and subsequently weakened the ionization efficiency of these pharmaceuticals. According to the correlations between log K ow and the changes in matrix effects and process efficiencies, the pharmaceutical determination in positive/negative ESI modes with log K ow ≤ 3.80/4.27 is considered to be significantly affected by NOM, accompanied by > 20% changes in matrix effects and process efficiencies.
More
Translated text
Key words
Hydrophobicity,Liquid chromatography–electrospray ionization-mass spectrometry,Matrix effects,Natural organic matter,Pharmaceuticals
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined