Mays Al-Dulaymi1, Xu Guo1, Sujata Rajan2, Sashank Pillai2 and Holly Lee1
1SCIEX, Canada; 2SCIEX, India
Published date: June 14, 2024
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.
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.
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.
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.
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
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.
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.
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.