# A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra
## 3 Results and Discussion
### Validation of the Correction Models  
An experiment had been done to verify the reliability of the regression model. In the verification test of regression model, 200 deltamethrin and acetamiprid residue signals from 20 Fuji apples was set as validation set. The intensity of the characteristic peaks at 634, 735 and 1114 cm\({}^{-1}\) was extracted from the Raman spectrum with pretreatment. Then the intensity values would be applied to the pesticide residue quantitative model to get the estimated pesticide concentration. Correction coefficient would be acquired after substituting peak intensity into the regression model. And corrected peak intensity would be obtained through multiplying the peak intensity values and correction coefficient. After substituting corrected peak intensity into pesticide residue quantitative model, corrected pesticide concentration would be got. Through comparing the pesticide concentration and corrected pesticide concentration, a series of parameters would be obtained to judge the regression model.  
After above steps, Raman signals of validation were processed. The modeling effect was evaluated by T value, correlation coefficient (R\({}^{2}\)) and Root Mean Squared Error (RMSE). The results were shown in Figure 6. In Figure 6, \"\(\bullet\)\" and \"\(\blacksquare\)\" stood for the pesticide data before and after correction respectively.  
Figure 5: The fitting surface of characteristic peak change rule at 735 cm\({}^{-1}\) between pesticide concentration and correction coefficient.  
Figure 6: Results of regression model between pesticide concentration and T value, R\({}^{2}\) and Root Mean Square Error (RMSE). (**a**): T value of acetamiprid; (**b**): R\({}^{2}\) of acetamiprid; (**c**): RMSE of acetamiprid; (**d**): T value of deltamethrin; (**e**): R\({}^{2}\) of deltamethrin; (**f**): RMSE of deltamethrin.  
From the figure it could be seen that with the reduction of pesticide concentration, the parameters improved. It could be surmised that when the pesticide content increased, a plurality of pesticides molecules competed for the active sites provided by the silver sol. High concentration of pesticides resulted in a severe reduction in the number of active sites. And the other mixture molecules had fewer active sites and their characteristic peak signals were weaker correspondingly. When the pesticide concentration was relatively low, the number of silver sol active sites occupied by pesticide molecules was small. No obvious competitive relationship existed between pesticides. Therefore, one pesticide characteristic peak signal was not interfered by other pesticides. And all the parameters were improved obviously.  
It could be seen from Figure 6 that with the concentration of one pesticide decreasing, the correction coefficient of the other pesticide also decreased significantly. It could be deduced that the mutual influence of the functional groups of the two pesticide molecules which weakened each other caused this phenomenon. Concentration of pesticides functional groups reflected the characteristic peaks intensity, in turn reflecting the pesticides concentration. With the increase of one pesticide concentration, the influence of the other pesticide reduced gradually. This phenomenon may exist in other pesticide mixture and correction method would be useful in detecting them accurately.