Journal of Tianjin Agricultural University ›› 2024, Vol. 31 ›› Issue (6): 79-84.doi: 10.19640/j.cnki.jtau.2024.06.013

• Researches and Scientific Notes • Previous Articles     Next Articles

Detection of carbaryl pesticide residues on vegetable surface based on fluorescence spectroscopy

Qin Yiyang, Li Shaocai, Zhang Han, Dong Guimei, Yang Fan, Yang RenjieCorresponding Author   

  1. College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China
  • Received:2023-09-27 Online:2024-12-31 Published:2024-12-31

Abstract: A qualitative and quantitative method for the detection of carbaryl pesticide residues on the surface of vegetable leaves was developed by combining fluorescence spectroscopy with chemometrics. Taking carbaryl pesticide on the surface of spinach leaves as the research object, spinach leaves of uncoated and coated with carbaryl pesticide at different concentrations(1.0×10-3 g/L~1.0×10-1 g/L)were prepared. The fluorescence spectra of all samples were collected by LS-55 fluorescence spectrophotometer produced by Perkin Elmer Company. All spectra were preprocessed using S-G smoothing and S-G smoothing + orthogonal signal correction(OSC)methods, respectively. On this basis, the mathematical models for qualitative and quantitative analysis of carbaryl pesticide residues on spinach leaf surface were established based on the partial least squares(PLS)method. Comparing the results of different models, it was found that the partial least squares discriminant(PLS-DA)model and the PLS quantitative model could provide the better results using S-G smoothing+OSC preprocessed spectra. For PLS-DA model, the discriminant accuracies were 100% for calibration and prediction sets. For PLS quantitative model, the correlation coefficients R2 were 0.98 for calibration and prediction sets, and the root-mean-square errors of calibration and prediction were 5.2×10-3 g/L and 6.1×10-3 g/L, respectively.

Key words: vegetable, carbaryl, fluorescence spectroscopy, pesticide residue, partial least squares method

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