Study On Rapid Determination Methods And Models Of Pesticide Residues On The Surface Of Fruits

With the progress of society and the improvement of people's life, the higher demand of fruits security is needed as well as its appearance and nutritional quality. At present, routine analytical methods used for the determination of pesticide residue on the surface of fruits are destructive, complex, time-consuming, high cost and not environmentally friendly. Hence, it will be very worthful for pratical applications to explore a determination technique, which is simple, rapid, green and environmentally friendly. This technique is very meaningful to expand China's exports of high-grade quality fruit, increase revenue of the fruit growers, and enhance the international competitiveness for the country's fruit industry.Raman spectroscopy technique is a kind of spectral analysis technique based on the development of the Raman scattering effects, which show informations of the molecule's vibration and rotating. With the merits of non-preparative sample, easy operation, short response time, green and environmentally friendly, Raman spectroscopy has been widely applied in petrol chemical, biomedicine, geoarchaeology, criminal justice and gem identification, etc. In the thesis, Gannan navel orange was chosen as the study objective, chlorpyrifos and malathion were chosen as analysis testing indexes. The rapid determination methods and quantitative models for pesiticide residues on the surface of fruits were studied using Raman spectroscopy technique. The main results of the thesis were involved:1. Raman spectra of chlorpyrifos and malathion standards and Gannan navel orange's pericarp samples were acquired. Then the Raman spectrum's ownerships and characteristic peaks of chlorpyrifos, malathion and Gannan navel orange's pericarp were obtained.2. Raman spectrum preprocessing methods were studied and ideal methods were obtained for chlorpyrifos and malathion standard solutions and residues on the surface of Gannan navel orange. Raman spectra of the chlorpyrifos and malathion solutions at different substrates (ophthalmic lens and silicon sheet) and on the surface of Gannan navel orange's pericarps were acquired. Then different spectral preprocessing methods were compared with and partial least square (PLS) regression models were established. The results showed that when the substrate was ophthalmic lens, the calibration model with linear baseline correcting preprocessing was the ideal model for chlorpyrifos solutions, and the calibration model with offset correcting preprocessing was the ideal model for malathion solutions. And when the substrate was silicon sheet, the calibration model with second derivative preprocessing was the ideal model for chlorpyrifos solutions, and the calibration model with first derivative preprocessing was the ideal model for malathion solutions. While the ideal preprocessing methods were first derivative and standard normal variate correction (SNV) for chlorpyrifos and malathion residues on the surface of Gannan navel orange, respectively.3. Rapid and quantitative mathematical models were established for the Rman spectra of the two kinds of pesticide standard solutions. Calibration models were eatablished using traditional peak-to-intensity ratio method and chemometric algorithms for Raman spectra data of chlorpyrifos and malathion solutions. Modeling effects between ophthalmic lens and silicon sheet for the acquisition of chlorpyrifos and malathion solutions were discussed. The results showed that when the substrate was ophthalmic lens, the model established by multiple linear regression (MLR) was the ideal model for chlorpyrifos solutions. The calibration and cross-validation results were: r_c=0.960, RMSEC=3.166, r_cv=0.877, RMSECV=5.421. And the PLS model was the ideal model for malathion solutions. The calibration and cross-validation results were: r_c=0.998, RMSEC=0.748, r_cv=0.985, RMSECV=2.236. However, when the substrate was silicon sheet, the PLS model was the ideal model for chlorpyrifos solutions. The calibration and cross-validation results were: r_c=0.986, RMSEC=1.628, r_cv=0.944, RMSECV=3.341. And the PLS model was the ideal model for malathion solutions. The calibration and cross-validation results were: r_c=0.996, RMSEC=1.175, r_cv=0.992, RMSECV=1.639. The results showed that it is better to acquire Raman spectrum of pesticide solutions by using silicon sheet than by using ophthalmic lens.4. Rapid and quantitative mathematical models were established for the Rman spectra of the two kinds of pesticide residues on the surface of fruit. Quantitative analysis mathematical models were established using traditional peak-to-intensity ratio method and chemometric algorithms for chlorpyrifos and malathion residues on the surface of Gannan navel orange. The feasibility of the rapid and quantitative determination of pesticide residue on the surface of fruit by Raman spectroscopy was discussed. The results showed that the least squares support vector regression (LS-SVR) model was the ideal model for the chlorpyrifos residue. The r_c, RMSEC, r_cv and RMSECV were 0.974, 2.592, 0.973 and 2.566. And the LS-SVR model was the ideal model for the malathion residue. The r_c, RMSEC, r_cv and RMSECV were 0.948, 4.101, 0.948 and 4.111. The results showed that pesticide residue on the surface of fruit could be determined by Raman spectroscopy, which supplied a kind of rapid, green and environmentally friendly method.