The Research Of The Methods Of Detecting Pesticide Residues In Vegetable Based On Nir Technology

With the continuous development of our national economy, especially in the global economic integration and international trade in food, food safety has become a crucial issue. The pesticide residues in vegetables are very significant in China's food safety problems. Therefore the development for fast, efficient, economical and environmentally friendly pesticide residues testing technology has become the current hot topics at home and abroad.Supported by Beijing Municipal Natural Science Foundation project"Study on rapid intelligent detection of pesticide based on spectroscopy technology", selecting Chinese cabbage and spinach as the representative of vegetables, this thesis uses NIR spectroscopy technology to research Chlorpyrifos to carry out the exploration researches on detection methods of pesticide residues in vegetables.Contrary to the mixed solution samples with the Chlorpyrifos concentration range of 0.05 ~ 4 mg/kg, after the selections of sampling resolution, sampling frequency and sampling mode, the near-infrared spectroscopy of the mixed solution samples is collected. using 10 different spectral pretreatment methods on the near-infrared spectra of the samples, the results show that a 17-point first derivative smoothing with the standard normal variable transformation (SNV) method of combining spectral pretreatment is the best one. Through the wavelength optimized and the determination of the number of PCA, the near-infrared calibration model is established based on PLS regression analysis, the correlation coefficient of calibration set is 0.9272, the standard error of cross validation (SECV) is 0.3, the coefficient of determination of the prediction set is 0.9747, the standard error of prediction (SEP) is 0.31. It shows that the model has good predictive power and high prediction accuracy.In order to validate the practical application ability of this model, this thesis has designed three experimental programs which are compared analysis on Chinese cabbage and spinach, on pollution-free spinach and spinach in the market and the validation of the stability of spinach model. The results show that the model of the Chinese cabbage and the model of spinach have a good predictive power. Meanwhile, the predictive effect of spinach model is slightly better than the Chinese cabbage model, as the pollution-free spinach model is superior to the market model of spinach. However, the universality of the model still needs to revise and improve in the future. The research shows that near-infrared spectroscopy as a rapid, non-destructive, multi-component analysis of the"green"analysis of pesticide residues in vegetables has tremendous potential and value. Therefore, this research has some references on the development of the detection of pesticide residues, at the same time plays a positive role on building and improving our food safety regulatory system.