MultilaboratoryStudy of a Nontarget Data Acquisitionfor Target Analysis (nDATA) Workflow Using Ultrahigh-Performance LiquidChromatography-High-Resolution Mass Spectrometry for the Screeningof 1087 Pesticides in Fresh Fruits and Vegetables

A nontarget Data Acquisition for Target Analysis (nDATA) workflow was developed to screen pesticides in fresh produce based on ultrahigh-performance liquid chromatography-high-resolution full scan mass spectrometry/variable data-independent tandem mass spectrometry acquisition (LC-FS MS/vDIA MSMS) and a pesticide database. The MSMS spectral library was generated to create a database consisting of 1087 pesticides based on authentic pesticide standards. The retention time (±0.5 min), precursor ion (≤± 5 ppm), and product ions (≤± 5 ppm) were extracted for each pesticide from LC-FS MS/data-dependent MSMS acquisition (LC-FS MS/DDA MSMS). Mass accuracy criteria, along with ±0.1 min retention time tolerance, were used for the identification of pesticides. Three laboratories evaluated and validated the nDATA workflow to screen and identify pesticides from produce extracts (apples, bananas, broccoli, carrots, grapes, lettuce, oranges, potatoes, strawberry, and tomatoes) prepared by the Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) procedure. Of the 1087 pesticides evaluated, false-positive rates were ≤5% for 977 pesticides in blank matrices and false-negative rates were ≤5% for 921 and 985 pesticides in fortified matrices at 10 and 100 μg/kg, respectively. False positives detected were misidentified pesticides, incurred residues, or contaminants possibly resulting from process or system contamination detected below the threshold level of 10 μg/kg. False negatives were attributed to pesticides that did not sufficiently ionize or fragment or had poor stabilities and QuEChERS extraction efficiencies. Incurred residues in archived produce samples (apple, Chinese broccoli, grape, kale, kohlrabi, orange, pepper, strawberry, tomato, and turnip green) were prepared using QuEChERS, evaluated by the nDATA workflow, and the results were compared and confirmed, if possible, to targeted GC-MS/MS, LC-MS/MS, and LC-FS MS/DDA MSMS methods. The three laboratories identified 25 parent pesticides at levels >10 μg/kg that were consistent with findings from targeted procedures and discovered 10 different metabolites that were not provided in the multiple reaction monitoring method or inclusion list of the targeted procedures. GC-MS/MS identified two pesticides, chlorothalonil and dacthal, and a possible chlorothalonil metabolite, pentachlorobenzonitrile, that were not amenable to LC-low or LC-high-resolution mass spectrometry analysis in produce samples. To improve the identification quality, the nDATA workflow further implemented quality control, operational, and processing measures to reduce the number of false detects, and the data evaluation workload. As demonstrated in this study, the validated nDATA workflow creates new opportunities for chemical residues analysis, offering a potential screening complement to targeted LC-MS/MS, GC-MS/MS, and nontargeted methods for pesticides and other contaminants of interest.