Thiabendazole (TBZ) is widely used in sclerotium blight, downy mildew as well as root rot disease prevention and treatment in plant. The indiscriminate use of TBZ causes the excess pesticide residues in soil, which leads to soil hardening and environmental pollution. Therefore, it is important to accurately monitor whether the TBZ residue in soil exceeds the standard. For this study, density functional theory (DFT) was used to theoretically analyze the molecular structure of TBZ, gold nanoparticles (AuNPs) were used to enhance the detection signal of surface-enhanced Raman spectroscopy (SERS) and the TBZ residue in red soil extracts was quantitatively determined by SERS. As a result, the theoretical Raman peaks of TBZ calculated by DFT were basically consistent with the measured results. Moreover, 784, 1008, 1270, 1328, 1406 and 1576 cm−1 could be determined as the TBZ characteristic peaks in soil and the limits of detection (LOD) could reach 0.1 mg/L. Also, there was a good linear correlation between the intensity of Raman peaks and TBZ concentration in soil (784 cm−1: y = 672.26x + 5748.4, R2 = 0.9948; 1008 cm−1: y = 1155.4x + 8740.2, R2 = 0.9938) and the limit of quantification (LOQ) of these two linear models can reach 1 mg/L. The relative standard deviation (RSD) ranged from 1.36% to 8.02% and the recovery was ranging from 95.90% to 116.65%. In addition, the 300–1700 cm−1 SERS of TBZ were analyzed by the partial least squares (PLS) and backward interval partial least squares (biPLS). Also, the prediction accuracy of TBZ in soil (Rp2 = 0.9769, RMSEP = 0.556 mg/L, RPD = 5.97) was the highest when the original spectra were pretreated by standard normal variation (SNV) and then modeled by PLS. In summary, the TBZ in red soil extracts could be quantitatively determined by SERS based on AuNPs, which was beneficial to provide a new, rapid and accurate scheme for the detection of pesticide residues in soil.
Chemical images of marine bio-active compounds by surface enhanced Raman spectroscopy and transposed orthogonal partial least squares (T-OPLS)
