脐橙表皮两种混合农药残留的表面增强拉曼光谱定量检测

doi:10.3964/j.issn.1000-0593(2018)01-0123-05

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Abstract Pesticide residue in agricultural and sideline products has become a social problem. In order to solve the problem of pesticide residue in fruit production, the rapid detection of mixing pesticide residues with navel orange as carrier was explored by surface enhanced Raman spectroscopy (SERS). The organophosphorus pesticides phosmet and chlorpyrifos were the objects of study because they were used more in the field of pest control, and gold colloid was prepared to get the SERS of the organophosphorus pesticides. The SERS spectra of the mixing pesticide samples were collected, and the characteristic peaks of the pesticides could be used for qualitative analysis of the mixing pesticide. At the same time, the stoichiometric method could be used to establish the quantitative mathematical model of the mixing pesticide, and the combination of the optimal pretreatment method and the wavenumber could be selected by comparing the modeling results of the Raman spectra. In the Raman spectrum range of 200～2 300 cm^-1, the partial least square (PLS) was used to build the model of the spectral data after the first derivatives data preprocessing. The results of the combined regression model were better, and it showed that the correlation coefficient (Rp) was 0.912, Root mean square error (RMSEP) was 3.601 mg·L^-1. After spectral screening and comparison of the spectral results, it was found that the best model of the spectra was established by the PLS in the range of 200～620, 830～1 040 and 1 250～2 300 cm^-1. The regression model had better effect, in which R_p was 0.909 and RMSEP was 3.338 mg·L^-1. It had shown that the SERS technology could be used to qualitatively and quantitatively analyze the mixing pesticide residues on navel orange.

Key words：SERS；Pesticide residues；Blended pesticide；Quantitative analysis

Received: 2015-08-31 Accepted: 2016-05-08

ZTFLH:

O657.3

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)01-0123-05 OR https://www.gpxygpfx.com/EN/Y2018/V38/I01/123