Premium
The separation of several organophosphate pesticides on immobilized polysaccharide chiral stationary phases
Author(s) -
Champion William L.,
Watts William L.,
Umstead Weston J.
Publication year - 2022
Publication title -
chirality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.43
H-Index - 77
eISSN - 1520-636X
pISSN - 0899-0042
DOI - 10.1002/chir.23473
Subject(s) - chemistry , enantiomer , selectivity , chromatography , resolution (logic) , detection limit , chiral stationary phase , pesticide , high performance liquid chromatography , factorial experiment , organic chemistry , statistics , mathematics , artificial intelligence , computer science , agronomy , biology , catalysis
While not initially a focus or priority, in recent decades, an emphasis has been placed on the activity of individual enantiomers of widely used pesticides. Of particular note are organophosphorus‐based pesticides like fenamiphos and profenofos, as examples. This work explores the enantioselective high‐performance liquid chromatography (HPLC) separations of seven such organophosphorus pesticides (OP's) on the library of immobilized polysaccharide‐based chiral stationary phases (CSPs) with normal phase hexane/alcohol mixtures. Further exploration of the effect of mobile phase strength and temperature on several of the separations was performed using simple factorial design. Equivalent retention of the first eluting enantiomer of several combinations of temperature and mobile phase was compared for peak shape, selectivity, and resolution. Similarly, equivalent selectivity of several combinations of temperature and mobile phase was compared for peak shape, retention of the first eluting enantiomer, and resolution. The results of this study make available several new chiral separations of the OPs included in the work that were not previously documented, including separations on the three most recently commercialized phases, Chiralpak IH, IJ, and IK. Additionally, sufficient understanding was obtained to be able to predict the trade‐off of resolution, analysis time, peak sharpness (and thus improve limit‐of‐detection [LOD]/limit‐of‐quantification [LOQ]), robustness, and convenience of conditions for further application optimization.