| Currently, people are paying close attention to the problem of pesticide residues in vegetable. In order to ensure the food safety of consumers, the state has increased the monitoring of pesticide residues. How to rapidly detect pesticide residues in vegetables has imperative. Surface-enhanced Raman spectroscopy(SRES) has some advantages such as simple and rapid preparation, rapid testing speed, high detection sensitivity, etc. and has been widely used to rapidly detect the pesticide residue in food and agricultural products. In this paper, the rapid detection methods of pesticide resticides in several leaf vegetables were studied based on SERS combined with rapid solvent pretreatment methods. The main research contents are as follows:(1) The rapid detection method study of triazophos residues in cabbage and ipomoea aquatica forsk using SERS combined with rapid solvent pretreatment methods. Firstly, acetonitrile, magnesium sulfate anhydrous and sodium chloride were used to extract the vegetable juice solution containing triazophos pesticide residues from cabbage and ipomoea aquatica forsk. Then, magnesium sulfate anhydrous, PSA, C18 and graphitized carbon were used to remove the fluorescent substances such as chlorophyll, organic acid, etc. Six characteristic peaks of triazophos were identified, namely 611, 977, 1000, 1330, 1407 and 1595 cm-1 through density functional theory(DFT). Gold nanoparticles were used to enhance raman signal. SERS of triazophos standard solution were collected. The minimum detection concentration was 0.4mg/L for triazophos standard solution and minimum detection concentrations for triazophos in cabbage and ipomoea aquatica forsk were below 0.65mg/kg and 0.94mg/kg, respectively. The quantitative analysis equations were established using the peak intensity at 1407 cm-1, the rate of recovery were 85.55~97.78% and 92.88%~97.19%, respectively, the relative standard deviation(RSD) were 1.39~3.24% and 2.16~3.12%.(2) On the basis of above research, rapid solvent pretreatment method is further improved, and the rapid detection method for phosalone residue in pakchoi using SERS combined with the improved rapid solvent pretreatment method was studied. Sodium acetate anhydrous used to replace magnesium sulfate anhydrous, and the amounts of various fillers used to extract and purify the matrix were optimized. The rate of recovery of this pretreatment method was 93.13% ~ 98.40%, and the RSD was between 1.49%~2.09%. Five characteristic peaks of phosalone were identified, namely 596, 751, 1096, 1230 and 1282 cm-1 through DFT. Gold nanoparticles were used to enhance raman signal. The minimum detection concentration was 0.5mg/L for phosalone standard solution and minimum detection concentration for phosalone in pakchoi was below 0.960mg/kg. Partial least squares(PLS) method was used to develop the prediction model for predicting the phosalone residue in pakchoi. The correlation coefficient and root mean square error of prediction(RMSEP) were 0.9687 and 1.48mg/kg. Predict recoveries were 95.76%~102.78% and the absolute values of relative errors were below 5%. T-test showed that there were no significant differences between the true values and prediction values.(3) The SERS combined with improved rapid solvent pretreatment method was further used to detcect the difenoconazole residue in leaf lettuce. The rate of recovery of this pretreatment method for the detection of difenoconazole residue in leaf lettuce was 86.19%~95.50%, and RSD was between 2.92%~4.55%. Five characteristic peaks of difenoconazole were identified, namely 697, 808, 1088, 1159 and 1194cm-1 through DFT. Gold nanoparticles were used to enhance raman signal. The minimum detection concentration was 0.2mg/L for difenoconazole standard solution and minimum detection concentration for difenoconazole in leaf lettuce was below 0.252mg/kg. PLS method was used to develop the prediction model for predicting the difenoconazole residue in leaf lettuce. The correlation coefficient and RMSEP were 0.9826 and 1.03 mg/kg. Predict recoveries were 93.03%~104.47% and the absolute values of relative errors were below 7%. T-test showed that there were no significant differences between the true values and prediction values.The qualitative and quantitative analysis methods of pesticide residue in leafy vegetables using SERS combined with rapid solvent pretreatment methods were explored in this study. The pretreatment methods explored were simple and detection time for individual sample was less than 20 minutes. This study provides a basis for rapid screening equipment development of pesticide residues in vegetables. |
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