- Detection of pesticide residues on intact tomatoes by carbon fiber ionization mass spectrometry.
Detection of pesticide residues on intact tomatoes by carbon fiber ionization mass spectrometry.
Trace and toxic pesticide residues may still remain on crops after harvest. Thus, maximum residual levels (MRLs) of pesticides on crops have been regulated. To determine whether the remaining pesticide residue level is below MRL, time-consuming sample pretreatment is needed prior to analysis of crop samples by suitable analytical tools. By elimination of sample pretreatment steps, a high-throughput method can be developed to determine the presence of pesticide residues directly on intact crops. Carbon fiber ionization mass spectrometry (CFI-MS) is effective in determining analytes with different polarities in solid, liquid, and vapor phases in open air. Moreover, the vapor derived from solid or liquid samples possessing high vapor pressure can be readily detected by CFI-MS. The setup of CFI-MS is straightforward. A carbon fiber (diameter of ~ 10 μm and length of ~ 1 cm) is placed close (~ 1 mm) to the inlet of the mass spectrometer applied with a high voltage (- 4.5 kV). No direct electrical contact applied on the carbon fiber is required. When placing the sample with certain vapor pressure underneath the carbon fiber, analyte ions derived from the sample can be readily detected by the mass spectrometer. Given that most pesticides possess a certain vapor pressure (~ 1.33 × 10-5-~ 1.33 × 10-4 Pa), we herein develop a qualitative and quantitative analysis method to determine pesticide residues on intact fruits such as tomato based on CFI-MS without requiring any sample pretreatment. Atrazine, ametryn, carbofuran, chlorpyrifos, isoprocarb, and methomyl were selected as model samples. Low limits of detection (at nM range) were achieved for the model pesticides using the current approach. Moreover, we demonstrated that the precision and accuracy of quantitative analysis of ~ 5% and ~ 2%, respectively, could be achieved using this approach. Graphical Abstract ᅟ.