Li, Y.; Peng, Y.; Qin, J.; Chao, K. A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra. Appl. Sci.2019, 9, 1699.
Li, Y.; Peng, Y.; Qin, J.; Chao, K. A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra. Appl. Sci. 2019, 9, 1699.
Li, Y.; Peng, Y.; Qin, J.; Chao, K. A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra. Appl. Sci.2019, 9, 1699.
Li, Y.; Peng, Y.; Qin, J.; Chao, K. A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra. Appl. Sci. 2019, 9, 1699.
Abstract
In the study, a new correction method was applied to reduce error during detection on mixed pesticide residue in apples by using Raman spectra. Combined with self-built pesticide residues detection system by Raman spectroscopy and the application of surface enhancement technology, rapid real-time qualitative and quantitative analysis of deltamethrin and acetamiprid residues in apples can be applied effectively. In quantitative analysis, compared with the intensity value of characteristic peaks of single pesticide with same concentration, the intensity value of characteristic peaks of the two pesticides decreased after mixing the pesticides, which interferes the results severely. By comparing the difference in the intensity of characteristic peaks of single and mixed pesticides, a correction method is proposed to eliminate the influence of pesticides mixture. Characteristic peak intensity values of gradient concentration pesticide from 10-1 g•kg-1 to 10-6 g•kg-1 and Lagrangian interpolation are applied in the correction method. And a smooth surface is applied to describe the correction ratio of characteristic peak intensity. Through detecting the characteristic peak intensity values of the mixed pesticide, correction ratio will be obtained. Then real values of the peak intensity of pesticides and the content of each component of the mixed pesticide will be acquired by the correction method. Correlation coefficient of model validation exceeds 0.88 generally and Root Mean Square Error also decreases obviously after correction, which proved the reliability of the method.
Chemistry and Materials Science, Applied Chemistry
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