Target Compound Analysis: A Step Ahead Using LC–ESI-TOF Mass Spectrometry

LC–MS–MS methods for the unambiguous identification and quantification of pesticides in complex matrix samples are well known and widely used. Triple quadrupole systems have proven useful for this task because of their high specificity in MS–MS mode and their low detection limits. However, working in targetted MS–MS mode prevents the detection of other compounds.

Therefore, it is difficult to develop methods for simultaneous analysis of high numbers of pesticides. The high mass accuracy and mass resolution of an LC–ESI-TOF system is another way of achieving high specificity without limiting the number of observed target compounds. This specific approach to pesticide detection is known as multi-targeting.^1,2

Molecular Formula Generation: The Isotopic Pattern Filter

With high mass accuracy alone, confident molecular formulae cannot usually be generated. An isotopic abundance pattern filter needs to be applied to reduce the number of molecular formula candidates if for example the presence of a putative compound has to be confirmed.³

Using an actual ESI-TOF MS and a sophisticated software solution, ESI-TOF MS is a key to both, simultaneous screening for multiple targets and sum formula confirmation:

1. As a result of mass accuracy apparently independent of peak intensity, it is possible to generate extracted ion traces with a window down to a few mDa, allowing for extreme selectivity and simple and fast identification.

2. Because of the conserved correct isotopic pattern, it is possible to reduce the number of possible hits within a given mass interval by at least an order of magnitude. The derived SigmaFit strongly helps to find the correct elemental composition (Figure 1). For a similar confidence with mass accuracy alone, 50 ppb (parts per billion; 10^–9 ) would be required — based on the unambiguous formula generation from reserpine; a 609 Da molecule.

Figure 1

Experimental:

Different matrix samples, spiked at various levels with a commercial pesticide standard (Ehrenstorfer, Pesticide Mix 34) were analysed using an Agilent 1100 LC system (Agilent Technologies, Waldbronn, Germany ) interfaced with a micrOTOF ESI-TOF MS and an RP-HPLC column (3 μm particles, 2.1 × 125 mm Hypersil ODS C18 material, 0.2 mL/min flow-rate) with an acetonitrile/water (1 mM NH₄OAc) — gradient (5–55% ACN in 45 min) applied for separation. The micrOTOF was equipped with an orthogonal ESI source and operated in positive mode. Calibration was performed externally prior to a sample series with a sodium formate solution, and additionally internally for each chromatogram by injecting the calibrant at the beginning and at the end of each run via a six-port divert valve equipped with a 100 μL loop.

Experiment 1: Selective Recovery of Three Pesticide Isoforms from a Plant Extract

The selectivity of the method based on accurate mass traces is demonstrated for three azine isoforms spiked in chamomile (Figure 2). A window of 10 mDa gives a dozen peaks, whereas a selectivity of a mass trace defined to a window of 3 mDa is sufficient for unequivocal identification. Retention time is required for the attribution of the individual isomers.

Figure 2

Figure 3

Detection limits well below 2 ppb have been determined, both for standards (Figure 3) and for spiking this amount in various matrices (Figure 4 for lettuce extract).

A basic requirement for reliable detection of compounds with such narrow mass traces is the mass stability for a wide dynamic range over the entire peak. As figure 3 shows, this mass stability is given for at least 4 orders of magnitude.

Figure 4

Experiment 2: Multi Target Compound Analysis: Automated Detection of Multiple Pesticides with Database Search

Automated target detection for the pesticide standard mix spiked to a lettuce extract can be achieved by automated peak detection on the EICs expected for the [M+H]⁺ ions of each compound in a database. This database contains the names, sum formulas, exact masses and retention times for about 230 pesticides. As shown above, a mass window of 3 mDa allows for selective detection of the compounds — if present. Based on accurate mass and known retention times the compounds present in the sample are identified. Additionally, for each identification candidate the theoretical isotope abundance pattern is compared to the experimental one. The derived SigmaFit values serve either for confirmation of the putative compounds or prevention of false positive identifications. Figure 4 shows the EICs and database search results for the spiked lettuce extract.

Conclusion: Exponentiate the Confidence of ESI-TOF MS Measurements

Screening of several hundreds of possible pesticides is easily feasible with one single ESI-TOF MS run. Based on accurate mass, a high selectivity is achieved. Even over a dynamic range of 4 orders of magnitude, the mass traces could be defined within 0.003 Da. With detection limits in the sub-ppm-range, pesticides can be readily characterized at the level following regulatory requirements in a variety of matrices.

Efficient multi target screening using this system was also reported elsewhere in the literature. Toxicological drug screening in urine was based on accurate mass, SigmaFit isotopic pattern analysis and automated database search. In automatic runs, correct SigmaFit values and accurate masses were achieved over a wide dynamic range, while a mean mass error was only 2.51 ppm.⁴

Compared to classical approaches by triple quadrupole instruments, an ESI-TOF MS solution allows the screening of a high number of targets within one LC-run and without the loss of sensitivity; it allows identification of unknown peaks based on accurate mass and isotopic pattern information (SigmaFit) while data can be archived and reprocessed later for additional compounds as well as data can be profiled for further statistical evaluation.

Petra Decker, Matthias Pelzing, Christian Neusüβ and Ralf Ketterlinus, Bruker Daltonik GmbH, Bremen, Germany.

References

1. H. Sasaki, J. Yonekubo and K. Hayakawa, Anal Sci. Jun, 22(6), 835–40 (2006).

2. S. Lacorte and A.R. Fernandez-Alba., Mass Spectrom Rev., 25(6), 866–880 (2006).

3. T. Kind and O. Fiehn, BMC Bioinformatics, 7, 234 (2006).

4. S. Ojanperä et al., RCM, 20, 1161–1167 (2006).

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