Matrix Effects in Multi-Residue Pesticide Analysis When Using Liquid Chromatography-Tandem Mass Spectrometry
Dr. Zhang is a Chemist in the Methods Development Branch of the US FDA Center for Food Safety and Applied Nutrition. His research interests focus on trace analysis of various contaminants, such as pesticides and mycotoxins, in foods using LC/MS and GC/MS.
Consumption of pesticide-contaminated food via daily diet is a major source of exposure to pesticides and poses a potential health threat to humans. It is necessary to monitor various pesticide residues in foods via multi-residue analysis procedures, because it would be impractical to develop individual analytical methods for every pesticide in suspected food commodities. The availability of liquid chromatography-tandem mass spectrometry (LC/MS/MS) has improved the selectivity and sensitivity of pesticide analysis, as well as workflow in the identification and quantification of various classes of pesticides in agricultural products. This leads to the development and use of LC/MS/MS multi-residue methods in laboratories worldwide to do consistent, targeted quantitative pesticides analysis from a single injection, providing increased sensitivity and the ability to screen a large number of target pesticides in one method.
The effect of the matrix is a phenomenon in electrospray ionization (ESI) LC/MS/MS analysis that impacts the data quality of the pesticide analysis. Matrix effects, caused by analyte and matrix component interactions, are unique to ESI-based LC/MS/MS instrumentation and present one of today’s most challenging analytical issues. Matrix effects can take the form of interference or signal suppression/enhancement (when compared to a pure analytical standard) and depend on the sample matrix, target analytes, and mode of ionization. Studies of matrix effects are essential to the application of LC/MS/MS with different food commodities. A thorough understanding of matrix effects would yield fundamental insights for different food matrices, corresponding sample preparation, and subsequent instrument performance, thus allowing major application needs (identification and quantitation) to be addressed.
Generally, there are two types of matrix effects—matrix interference and signal alteration. Matrix interference can be caused by those coeluting components in sample extracts that have similar ions in the MS/MS experiment. This type of matrix effect can lead to false positive/negative identification and can be resolved by using non-interfering MRM transitions, extensive sample cleanup, or improving the LC separation. Increased mass/charge selectivity, which can be acquired by using a high resolution accurate mass spectrometer, can help minimize matrix interference.
Matrix effects may also be caused by interactions (via van der Waals, dipolar-dipolar, or electrostatic forces) between pesticides and co-extractives in the prepared sample that could suppress or enhance the ionization of a pesticide in the ESI source. This can result in a lower or higher signal, which affects the accuracy of the quantitative results. Several approaches have been used to minimize the signal suppression or enhancement resulting from the matrix components. These include extensive sample cleanup, improvement of the LC separation to avoid coelutions with matrix components, or serial dilution of the final extract, such that fewer matrix components will be injected into the analytical system. Splitting of the LC eluent flow before entering the mass spectrometer may also help eliminate matrix suppression or enhancement. Unlike the above approaches, standard addition, internal standards, or matrix-matched calibration curves are commonly used to compensate for, but not to reduce, signal suppression or enhancement.
None of the above approaches will completely eliminate matrix effects. Increased selectivity (e.g. using specific transitions or improving mass resolution/accuracy) can minimize matrix interferences, but signal suppression or enhancement may still be observed because signal alteration happens in the ion source prior to detection. Using dilution or a smaller injection volume requires more sensitive instruments and introduces more error, in terms of accuracy and precision, for quantitative results. Additionally, optimal dilution factors depend on food matrices, instrument sensitivity, target pesticides, and LC conditions, so it is time-consuming to optimize the experimental conditions. Using internal standards might be too expensive to apply in multi-residue analysis. Matrix-matched calibration is commonly used for quantitation, but there are disadvantages associated with this approach. First, it is hard to collect blank matrix for each food commodity. Second, analytes in a matrix-matched environment are different from those in real samples, in which the analytes first interact with the matrix components and then are “modified” by sample preparation. Matrix-matched calibration standards would alleviate matrix effects on quantification only if sample matrices remained the same before and after the sample preparation, which is impossible to achieve. Therefore, this approach might only work well for simple matrices such as fresh produce, but not for more complex matrices, such as botanical samples. Third, it is laborious and time-consuming to prepare matrix-matched calibration standards for routine analysis, especially when samples of different commodities have to be analyzed on daily basis.
Obviously, the lack of well-suited approaches for circumventing matrix effects requires us to systematically investigate the problem so that, in theory, we will be able to describe and define the interactions between matrix components and analytes. In practice, we can quantitatively measure matrix effects and estimate the impact on quantitation and identification. At the present time, LC/MS/MS is known as the best instrument for target analysis and quantitation; however, it is limited by an incomplete understanding of matrix effects. This presents a significant challenge to researchers working to harness the sensitivity, selectivity, and specificity of LC/MS/MS to meet the growing need for better multi-residue analysis procedures.