Detection of Profenofos in Chinese Kale, Cabbage, and Chili Spur Pepper Using Fourier Transform Near-Infrared and Fourier Transform Mid-Infrared Spectroscopies
Atchara Sankom, Warapa Mahakarnchanakul, Ronnarit Rittiron, Tanaboon Sajjaanantakul, Thammasak Thongket
Abstract
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 R2 (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 R2 (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 R2 (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.