Optimized multimatrix calibration concept for liquid chromatography mass spectrometry-based bioanalysis methods
A Kaufmann1, P Butcher1, K Maden1
1Official Food Control Authority of the Canton of Zurich, Fehrenstrasse 15, 8032 Zürich, Switzerland.
Abstract:
In this paper, a calibration procedure for LC/MS-based bioanalysis methods, termed "A/B fortification", is proposed. The concept relies on the post-extraction fortification (B-spike) of an aliquot of the injection-ready sample extract for the determination and compensation of specific signal suppression or enhancement effects compared to matrix-free extract prepared in buffer or mobile phase. Conventional analyte recovery, observed due to the incomplete extraction of analytes from the sample or losses during a cleanup, is determined by the conventional pre-extraction fortification (A-spike) of a blank sample that belongs to the same type of matrix as the sample with the unknown analyte concentration. This approach permits a higher throughput than conventional sample fortification strategies. The results obtained by utilizing the A/B fortification concept were extensively compared against conventional methods (representative bank matrix fortification, sample fortification and internal standard). The proposed concept (based on the pre-fortification of a reference matrix and post-fortification of the sample) was found to be significantly less biased than internal standard-based techniques. The A/B fortification indicated a better accuracy than the sample fortification or representative blank matrix fortification approach and, most importantly, produced significantly fewer outliers. This was linked to the fact that in the case of the A/B fortification, the uncertainty of the subtraction of two peak areas (fortified minus unfortified sample) is reduced, because fortifications are not made prior to the extraction step but are made into the final injection-ready sample extract. Fortification into an injection-ready aliquot eliminates all sample processing-related differences (procedural errors), which can affect conventional sample fortification-based quantifications.
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