Matrix Effect Compensation in Small-Molecule Profiling for an LC鈥揟OF Platform Using Multicomponent Postcolumn Infusion

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• 作者：Oskar Gonz谩lez ; Michael van Vliet ; Carola W. N. Damen ; Frans M. van der Kloet ; Rob J. Vreeken ; Thomas Hankemeier
• 刊名：Analytical Chemistry
• 出版年：2015
• 出版时间：June 16, 2015
• 年：2015
• 卷：87
• 期：12
• 页码：5921-5929
• 全文大小：334K
• ISSN：1520-6882

文摘

The possible presence of matrix effect is one of the main concerns in liquid chromatography鈥搈ass spectrometry (LC鈥揗S)-driven bioanalysis due to its impact on the reliability of the obtained quantitative results. Here we propose an approach to correct for the matrix effect in LC鈥揗S with electrospray ionization using postcolumn infusion of eight internal standards (PCI-IS). We applied this approach to a generic ultraperformance liquid chromatography鈥搕ime-of-flight (UHPLC鈥揟OF) platform developed for small-molecule profiling with a main focus on drugs. Different urine samples were spiked with 19 drugs with different physicochemical properties and analyzed in order to study matrix effect (in absolute and relative terms). Furthermore, calibration curves for each analyte were constructed and quality control samples at different concentration levels were analyzed to check the applicability of this approach in quantitative analysis. The matrix effect profiles of the PCI-ISs were different: this confirms that the matrix effect is compound-dependent, and therefore the most suitable PCI-IS has to be chosen for each analyte. Chromatograms were reconstructed using analyte and PCI-IS responses, which were used to develop an optimized method which compensates for variation in ionization efficiency. The approach presented here improved the results in terms of matrix effect dramatically. Furthermore, calibration curves of higher quality are obtained, dynamic range is enhanced, and accuracy and precision of QC samples is increased. The use of PCI-ISs is a very promising step toward an analytical platform free of matrix effect, which can make LC鈥揗S analysis even more successful, adding a higher reliability in quantification to its intrinsic high sensitivity and selectivity.