abstract

Abstract

This technical note describes a MS/MS spectral library searching workflow for confident identification of >200 pesticides in orange juice on the SCIEX 5500+ system. The QTRAP technology of the SCIEX 5500+ system enabled data-dependent acquisition (DDA) of enhanced product ion (EPI) scans to produce MS/MS spectra that were used to compare against mass spectral libraries for improved compound identification confidence (Figure 1). The automated library searching and confirmation of candidate hits supplemented retention time (RTs) and ion ratio, which minimized the risk of false positive and negative results during compound identification. In this work, >85% of the targeted pesticides were correctly identified by MS/MS library matching in spiked orange juice.

Figure 1. Extracted ion chromatogram (XIC) of imazalil spiked at 100 ng/mL in orange juice showing >95% library hit scores when acquired by sMRM-DDA-EPI. The SCIEX OS software automatically selects the triggered EPI scan (circle on XIC) with the highest scoring MS/MS spectrum from library matching. An access button provides easy navigation between the Analytics and Data Explorer modules in SCIEX OS software to view the IDA Explorer showing number of triggered EPI scans.

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Keybenefits

Key benefits of QTRAP-based MS/MS spectra for library searching on the SCIEX 5500+ system

• Increased compound identification confidence:  Optimized triggering of EPI scans resulted in fragment-rich MS/MS spectra for comparison against reference libraries.
• Optimized MRM duty cycle by intelligent EPI triggering.  Dynamic exclusion of previously triggered EPIs preserved cycle time to ensure adequate acquisition of data points across a chromatographic peak for reliable quantitation.
• Excellent qualitative data. >85% of the targeted pesticides were correctly identified in the orange juice spikes.
• Easy method development in SCIEX OS software: The SCIEX OS software provides a step-by-step workflow for easy method creation and seamless navigation between data acquisition and processing workflows.

Introduction

Introduction

Pesticides protect crops from infestation and diseases, but their residues can remain on food products. Pesticide residues in food have been linked to various health problems, including acute toxicity, carcinogenicity, and endocrine disruption.¹ The regulatory requirements for pesticides in the European Union (EU) are comprehensive and aim to ensure the safe use of pesticides while protecting human health. EU regulation (EC) No 396/2005 sets the maximum residue levels (MRLs) of pesticides in products of animal or vegetable origin intended for consumption. In the absence of a specific MRL, such as in the case of citrus fruits like oranges, a generic MRL of 0.01 mg/kg is applied.² Monitoring these residues ensures that the food supply is safe for consumption. LC-MS/MS can detect and quantify these residues accurately, even at very low levels, ensuring that food products meet safety standards. Here, compound identification is improved by MS/MS spectral matching against reference or published libraries.

Methods

Methods

Standard stock preparation:  The iDQuant Standards Kit (204 pesticides) was used. A 1 µg/mL stock solution was prepared by diluting 10 μL of the individual mixed standards in the kit with 900 μL of acetonitrile.³

Sample preparation:  The orange juice was diluted 10-fold with water and filtered through a 0.45 µm Phenex syringe filter. The filtered orange juice was spiked in triplicate at 1, 10 and 100 ng/mL using the iDQuant stock solution.

Chromatography:  A Shimadzu Nexera Prominence LC system was used with a Kinetex Biphenyl column (50 x 2.1 mm, 2.6 µm, Phenomenex P/N 00B-4622-AN). The gradient conditions used are shown in  Table 1. The injection volume was 10 μL and the column oven temperature was 30°C.

Table 1: Chromatographic gradient for the analysis of pesticides in orange juice.

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Mass spectrometry:  Analysis was performed in DDA mode. Scheduled multiple reaction monitoring (sMRM) survey scans were used to trigger EPI scans on the SCIEX 5500+ system with polarity switching between positive and negative electrospray ionization. Only the quantifier transition was monitored for each compound in the sMRM survey scan. MS/MS spectra were acquired with dynamic fill time enabled at a scan speed of 10,000 Da/sec and with a collision energy (CE) of 35 V and collision energy spread (CES) of ±15 V.  Tables 2  and  3 present the source and gas parameters and the criteria for triggering the data-dependent EPI scans. Optimized MRM transitions and compound-specific parameters were used for the target analytes.

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Data processing:  Data acquisition and processing were performed using the SCIEX OS software, version 3.3.0. MS/MS spectral matching was compared against the SCIEX Pesticide LCMS/MS Library.  Figure 2A  shows the library search parameters that were customized to achieve high-quality library hits. The confirmation search algorithm was selected to screen for library hits with the same names as the target analytes. Library hits were ranked by purity scores that measure the similarity between the library and unknown MS/MS spectra, with higher values representing a lower frequency of interfering peaks from additional compounds in the unknown spectrum. Optimization of the algorithm parameters can accelerate processing time and filter the library results to the user’s preference. For example, each library may contain MS/MS spectra acquired with different experimental parameters, such as collision energy (CE) and collision energy spread (CES). Unchecking CE and CES broadens the search to include MS/MS spectra acquired with different methods and/or on different instruments, which can minimize the risk of false negatives.  Figure 2B shows how qualitative criteria such as library scores can be flagged in the results table to expedite data review

Figure 2. Screenshots of the Analytics processing method editor in SCIEX OS software. A) Configuration of the library search parameters optimizes the processing speed and returns library hits based on user-defined search criteria. B) Configuration of flagging rules expedites data review by highlighting results that are filtered and/or sorted based on user preference.

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Developing

Step-by-step creation of sMRM-DDA-EPI method

The MS Method Editor in the SCIEX OS software provides a step-by-step workflow to easily create sMRM-DDA-EPI acquisition methods (Figure 3). Selecting DDA as the experiment type automatically creates a method containing a survey scan, a section for specifying DDA triggering criteria and the resulting triggered dependent scan for acquiring MS/MS spectra. Here, typical MRM parameters are defined for all target transitions in the sMRM survey scan. The DDA criteria section enables the user to customize the conditions for EPI triggering, such as the number of candidate ions to monitor in the survey scan, the intensity threshold, dynamic background subtraction (DBS) and dynamic exclusion. DBS prioritizes EPI triggering of candidate ions that change in intensity above a user-specified threshold during the elution of an LC peak. Enabling this option allows for more consistent trigger thresholds despite varying analyte concentrations and matrices. Additionally, dynamic exclusion optimized the cadence of EPI triggering across each LC peak based on a user-specified interval, ensuring that cycle time is preserved for MRM acquisition and not wasted on unnecessary, redundant triggering. Furthermore, a second DDA experiment with negative polarity was added, resulting in a polarity-switching method sequentially triggering EPI scans in positive and negative modes for over 250 sMRM transitions in the same injection. The SCIEX OS software also specifies different intensity thresholds in each DDA experiment, enabling users to optimize triggering based on different sensitivity needs in each polarity. The Method Overview on the left panel provides a visual layout of the 2 experiments in the DDA method, while the right panel features collapsible sections that enable users to hide or reveal content for easy navigation.

Figure 3: Screenshot of the MS Method workspace in the SCIEX OS software showing the step-by-step creation of the DDA method. In this work, the DDA method structure consisted of a sMRM survey scan comprised of >250 MRM transitions, the criteria for EPI triggering and the dependent EPI scan for acquiring MS/MS spectra. The Method Overview panel (left) provides a visual layout of the overall DDA method structure, while the MS Method Editor on the right provides collapsible panels of different sections to allow easy viewing and navigation of method parameters.

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Leveraging

Automated compound identification using MS/MS library searching

MS/MS comparison of the acquired experimental spectra against the library spectra resulted in >90% purity scores for the majority of the compounds in negative (Figure 4) and positive polarity (Figure 5) without impacting quantitative data quality. Dynamic exclusion instructed the software to exclude any candidate ions that triggered a specific number of EPI occurrences for a user-defined period of time. This helped preserve cycle time to capture more MRM data points across the LC peak. The impact of dynamic exclusion can be seen in  Figures 4  and  5.  Figure 4 compares the MS/MS spectra of novaluron, a negative compound spiked at 100 ng/mL in orange juice acquired without (bottom panel) and with (top panel) dynamic exclusion enabled. Both experiments yielded similar library scores of >90%, but less excessive triggering was observed when dynamic exclusion was enabled. This was demonstrated by the lower number of EPI scans triggered across each LC peak (circles) in the XIC pane in Analytics.

Figure 4. Screenshots of the Data Explorer and Analytics workspaces showing different ways of interrogating MRM-DDA-EPI data acquired for novaluron, a negative compound, spiked at 100 ng/mL in orange juice with dynamic exclusion disabled (top) and enabled (bottom). The SCIEX OS software provides an access point in Analytics to easily navigate to Data Explorer to view and interrogate the same datafile in a different way. Library results in the Analytics workspace are supplemented with useful information like the chemical structure, CAS number and formula. The benefit of dynamic exclusion is demonstrated by less excessive EPI triggering without impacting data quality, as observed in the bottom row. Chromatograms are shown from the quantifier transition for all XICs.

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By clicking on the access point in the Analytics workspace (Figure 4), a similar comparison of the triggered EPI scans is available in the IDA Explorer map in the Data Explorer workspace. Similarly,  Figure 5 demonstrates the benefit of dynamic exclusion for omethoate, a positive compound, spiked at 100 ng/mL in orange juice. While similar library scores of >90% were achieved in both cases, dynamic exclusion resulted in less EPI triggering, as shown by the 3 EPI scans on the right compared to the 7 EPI scans on the left in both the Analytics and Data Explorer workspaces. The cycle time saved from fewer EPI triggering resulted in more time spent on MRM acquisition, as shown by the more frequent sampling of data points across the LC peak on the right. This improved duty cycle is important for less sensitive compounds or those at lower concentrations where quantitative performance may suffer from poorly sampled XIC peaks. Overall, the targeted MS/MS screening correctly identified 93% and 86% of the pesticides in the 100 ng/mL and 10 ng/mL orange juice spikes, respectively. These results were calculated by comparing the number of library hits with purity scores exceeding the acceptance criteria of 70% against the total number of the target pesticides known to be in the library.

Figure 5. Screenshots of the Data Explorer and Analytics workspaces showing different ways of interrogating MRM-DDA-EPI data acquired for omethoate, a positive compound, spiked at 100 ng/mL in orange juice with dynamic exclusion disabled (left) and enabled (right). The top row shows the results table for 2 representative samples acquired without (top) and with (bottom) dynamic exclusion. The middle section compares the quantifier XIC, the number of EPI scans triggered (circles) across the LC peak and the corresponding MS/MS spectra in the Analytics workspace between the use of dynamic exclusion. A similar comparison is shown for the Data Explorer workspace in the bottom section, which shows the number of data points collected each LC peak and the number of triggered EPIs in the IDA Explorer map.

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Conclusion

Conclusion

• Using the QTRAP technology on the SCIEX 5500+ system, a MRM-DDA-EPI method was developed that enabled the simultaneous acquisition of MRM data and high-quality EPI-triggered MS/MS spectra
• MS/MS library searching provided orthogonal information in addition to retention time and ion ratio, to increase confidence in compound identification
• Coupled with the fast polarity switching of the SCIEX 5500+ system, intelligent software features such as dynamic exclusion minimize the time spent on unnecessary acquisition of EPI scans, preserving cycle time and maintaining the MRM duty cycle.

References

References

1. Asghar, U.; Malik, M.F.; Javed, A. Pesticide Exposure and Human Health: A Review. J. Ecosys Ecograph.   2016, S5:005.
2. European Commission. EU Pesticides Database.
   https://food.ec.europa.eu/plants/pesticides/eu-pesticidesdatabase_en
3. Schreiber, A. Using the iDQuant™ Standards Kit for Pesticide Analysis to Analyze Residues in Fruits and Vegetable Samples. 2011. Publication No: 3370211-01
4. Regulation (EC) No 396/2005 on Maximum Residue Levels of Pesticides in or on Food and Feed of Plant and Animal Origin. Directive 91/414/EEC.