Detection of Profenofos in Chinese Kale, Cabbage, and Chili Spur Pepper Using Fourier Transform Near-Infrared and Fourier Transform Mid-Infrared Spectroscopies

Different types of quantitative technology based on infrared spectroscopy to detect profenofos were compared based on Fourier transform near-infrared (FT-NIR; 12,500-4000 cm(-1)) and Fourier transform mid-infrared (FT-MIR; 4000-400 cm(-1)) spectroscopies. Standard solutions in the range of 0.1-100 mg/L combined with the dry-extract system for infrared (DESIR) technique were analyzed. Based on partial least-squares regression (PLSR) to develop a calibration equation, FT-NIR-PLSR produced the best prediction of profenofos residues based on the values for R-2 (0.87), standard error of prediction or SEP (11.68 mg/L), root-mean-square error of prediction or RMSEP (11.50 mg/L), bias (-0.81 mg/L), and ratio performance to deviation or RPD (2.81). In addition, FT-MIR-PLSR produced the best prediction of profenofos residues based on the values for R-2 (0.83), SEP (13.10 mg/L), RMSEP (13.00 mg/L), bias (1.46 mg/L), and RPD (2.49). Based on the ease of use and appropriate sample preparation, FT-NIR-PLSR combined with DESIR was chosen to detect profenofos in Chinese kale, cabbage, and chili spur pepper at concentrations of 0.53-106.28 mg/kg. The quick, easy, cheap, effective, rugged, and safe technique coupled with gas chromatography-mass spectrometry was used to obtain the actual values. The best FT-NIR-PLSR equation provided good profenofos detection in all vegetables based on values for R-2 (0.88-0.97), SEP (5.27-11.07 mg/kg), RMSEP (5.25-11.00 mg/kg), bias (-1.39 to 1.30 mg/kg), and RPD (2.91-5.22). These statistics revealed no significant differences between the FT-NIR predicted values and actual values at a confidence interval of 95%, with agreeable results presented at pesticide residue levels over 30 mg/kg. FT-NIR spectroscopy combined with DESIR and PLSR should be considered as a promising screening method for pesticide detection in vegetables.

Automated summary

Quantitative technologies based on infrared spectroscopy were compared for profenofos detection, with FT-NIR-PLSR and FT-MIR-PLSR showing the best prediction of profenofos residues. The ease of use and appropriate sample preparation make FT-NIR-PLSR combined with DESIR a promising screening method for pesticide detection in vegetables.