Optimization and evaluation of low-pressure gas chromatography–mass spectrometry for the fast analysis of multiple pesticide residues in a food commodity

A fast method of analysis for 20 representative pesticides was developed using low-pressure gas chromatography–mass spectrometry (LP-GC–MS). No special techniques for injection or detection with a common quadrupole GC–MS instrument were required to use this approach. The LP-GC–MS approach used an analytical column of 10 m×0.53 mm I.D., 1 μm film thickness coupled with a 3 m×0.15 mm I.D. restriction capillary at the inlet end. Thus, the conditions at the injector were similar to conventional GC methods, but sub-atmospheric pressure conditions occurred throughout the analytical column (MS provided the vacuum source). Optimal LP-GC–MS conditions were determined which achieved the fastest separation with the highest signal/noise ratio in MS detection (selected ion monitoring mode). Due to faster flow-rate, thicker film, and low pressure in the analytical column, this distinctive approach provided several benefits in the analysis of the representative pesticides versus a conventional GC–MS method, which included: (i) threefold gain in the speed of chromatographic analysis; (ii) substantially increased injection volume capacity in toluene; (iii) heightened peaks with 2 s peak widths for normal MS operation; (iv) reduced thermal degradation of thermally labile analytes, such as carbamates; and (v) due to larger sample loadability lower detection limits for compounds not limited by matrix interferences. The optimized LP-GC–MS conditions were evaluated in ruggedness testing experiments involving repetitive analyses of the 20 diverse pesticides fortified in a representative food extract (carrot), and the results were compared with the conventional GC–MS approach. The matrix interferences for the quantitation ions were worse for a few pesticides (acephate, methiocarb, dimethoate, and thiabendazole) in LP-GC–MS, but similar or better results were achieved for the 16 other analytes, and sample throughput was more than doubled with the approach.