Evaluation of different cleanup sorbents for multiresidue pesticide analysis in fatty vegetable matrices by liquid chromatography tandem mass spectrometry

In this article we have evaluated the performance of different sorbents for the cleanup step in multiresidue pesticide analysis in fatty vegetable matrices using QuEChERS methodology. The three different matrices tested (olive oil, olives and avocado) were partitioned using acetonitrile prior to cleanup step. Afterwards, the supernatant was purified using different sorbents: C18+PSA (primary secondary amine), Z-Sep(+) (zirconium oxide and C18 dual bonded to silica), Z-Sep (zirconium oxide bonded to silica) and a novel sorbent Enhanced Matrix Removal-Lipid (EMR) whose composition has not been disclosed. The different cleanup strategies were compared for a group of 67 representative pesticides in terms of recovery rates, matrix effects, extract cleanliness and precision using ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). The best extraction efficiencies in olive oil matrix were obtained using EMR, while the results for olives and avocado were pretty similar amongst the different sorbents with an overall lower performance in terms of matrix effects and recovery rates compared to olive oil data, particularly in olives due to the higher complexity and concentration of coextracted species. On the other hand, the average reproducibility was clearly better when EMR sorbent was employed in all selected matrices for most pesticides (RSD < 10% for 45, 52, and 56 pesticides in avocado, olives and olive oil respectively). The best results in terms of matrix effects were also obtained with EMR; with signal suppression lower than 20% for 79%, 16% and 51% of pesticides tested in olive oil, olives and avocado respectively. Using EMR as cleanup sorbent, limits of quantitation using UHPLC-MS/MS, ranged from 0.10 to 90 mu g kg(-1), allowing their determination at the low concentration levels demanded by current olive oil regulations in most cases. (C) 2016 Elsevier B.V. All rights reserved.