JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 38 Issue 6
December 2023
Article Contents
LI Pao, SU Guanglin, LI Caiting and et al. A non-targeted detection method of multi-pesticide residues in cowpea based on OMWTTFA[J]. Journal of Light Industry, 2023, 38(6): 46-51. doi: 10.12187/2023.06.006
Citation: LI Pao, SU Guanglin, LI Caiting and et al. A non-targeted detection method of multi-pesticide residues in cowpea based on OMWTTFA[J]. Journal of Light Industry, 2023, 38(6): 46-51. doi: 10.12187/2023.06.006 shu

A non-targeted detection method of multi-pesticide residues in cowpea based on OMWTTFA

  • Received Date: 2023-03-12
    Accepted Date: 2023-05-11
  • To the increasing difficulty of the detection of multi-pesticide residues in cowpea, optimized moving window target conversion factor analysis (OMWTTFA) algorithm was proposed, in which an adaptive iterative re-weighted penalized least squares calculation had been added to the moving window target transfer factor analysis (MWTTFA) algorithm. And a new method for non-targeted detection of multi-pesticide residues in cowpea was established by combining with gas chromatography-mass spectrometry (GC-MS). A mass spectrometry database of 500 common pesticides was established and high throughput analysis of cowpea GC-MS data was realized by using OMWTTFA algorithm. The method was verified with actual cowpea and spiked samples. The results showed that the method could realize non-targeted detection of all pesticide residues in the two groups of spiked cowpea samples within 30 min retention time, while no pesticide residues were detected in the actual cowpea samples. The matching values of all components were larger than 800‰, and this method was more accurate than MWTTFA algorithm. The above results showed that this method promised to become a new way for non-targeted and high-throughput detection of multi-pesticide residues in fruits and vegetables.
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