Author(s): Julie Kowalski, Sharon Lupo, and Jack Cochran
Restek Corporation
Published By: Restek Corporation
Year of Publication: 2013
Abstract: Liquid chromatography tandem mass spectrometry (LC-MS/MS) is popular for monitoring pesticide residues in food. The increased selectivity and sensitivity of LC-MS/MS have impacted how multiresidue methods are performed, sometimes decreasing the need for rigorous sample preparation. However, this approach suffers from matrix effects causing poor data quality and difficult quantification. Matrix effects can be mitigated by sample preparation that reduces the concentration of coextracted matrix material or by experimental strategies like matrix-matched calibration that compensate for matrix effects. We considered these two aspects of multiresidue methods, sample preparation and calibration strategies, in order to determine recommendations that balanced data quality as well as time and financial investments.
We performed matrix effects studies investigating two approaches for reducing matrix interferences, QuEChERS sample preparation and dilution, in combination with the compensation strategy of matrix-matched calibration compared to solvent calibration. There are compromises with each method regarding time and financial resources. A variety of food types were tested including high water (celery), high pigment (kale), high fat (avocado), citrus (lime), and dry (brown rice flour) foods, with subsequent pesticide residue analysis by LC-MS/MS. Samples were fortified at high and low ppb levels with over 100 pesticides representing multiple classes.
We determined that with the easiest commodities, the dilution method and solvent-only calibration gave acceptable recovery values. However, for other commodities either a matrix-matched calibration curve and/or cleanup were needed to obtain good recovery values. The high carbohydrate and citrus commodities proved to be too difficult with the specific methods we tested here. In almost every case, use of matrix-matched calibration provided improvement.
