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The Research of the Methods of Detecting Pesticide Residues in Vegetable Based on NIR Technology

Author: ZhengGuang
Tutor: LiuCuiLing
School: Beijing Technology and Business University
Course: Control Theory and Control Engineering
Keywords: Near Infrared Spectroscopy Pesticide residues Chlorpyrifos Correction model Partial Least Squares
CLC: TP274
Type: Master's thesis
Year: 2010
Downloads: 88
Quote: 0
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Abstract


With the continuous development of China's national economy, especially the integration of the global economy and the international food trade, food security has become an important problem. Excessive pesticide residues in vegetables is very prominent in China's food safety problems. Therefore developed a fast, efficient, economical, environmentally friendly pesticide residue detection technology has become a hot topic of current domestic and international research. The papers Beijing Natural Science Foundation project \to carry out an exploratory study of the method of detection of pesticide residues in vegetables. For chlorpyrifos concentration range of 0.05 ~ 4 mg / kg mixed solution samples paper by different sampling resolution, sampling frequency and sampling mode select, obtain a the the chlorpyrifos solution samples near infrared spectrum. Pretreatment methods, through 10 different pretreatment methods treated samples near infrared spectroscopy obtained the first order derivative of the 17-point smoothing standard normal variable transformation (SNV) combining preprocessing methods, and achieved good results. Wavelength optimization, determining the number of principal components, was established based on partial least squares near infrared calibration model, the predictive value of the calibration samples and the true value of the correlation coefficient of 0.9272, cross-validation mean square error the SECV as the 0.3, the predicted values ??of the prediction samples The coefficient of determination of the true value of 0.9747, projections variance the SEP 0.31, show that the model has good predictive effect and higher prediction accuracy. To further validate the practical application of the model, the paper carried the cabbage and spinach model comparative analysis, of pollution the spinach with market spinach model comparison analysis as well as spinach model stability studies verify. The results show that there is a good predictor of the effect of the model of Chinese cabbage and spinach, and spinach model predicted slightly better than the model of Chinese cabbage, pollution-free spinach model is superior to the model of the market spinach, but the versatility of the model needs to be further revised and improved. This study shows that, near infrared spectroscopy as a rapid, non-destructive analysis of multi-component analysis of the \Therefore, the study of the development of pesticide residue detection technology has certain referential significance, and played a positive role to build and improve our food safety regulatory system.

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