基于增强拉曼光谱的苹果中啶虫脒农药残留的无损定量分析

为了快速准确检测苹果的农药残留,该研究基于表面增强拉曼光谱技术,以新烟碱类农药啶虫脒作为研究对象,建立了一种快速准确检测苹果农药残留含量的方法。为了改善表面增强剂对定量检测的检测精度和稳定性,在pH值为6.5的弱酸性条件下向银溶胶中加入稳定剂聚丙烯酸钠和团聚剂NaCl。采用了卡尔曼平滑(Rauch-Tung-Striebel,RTS)与非对称重加权惩罚最小二乘法(asymmetrically reweighted Penalized Least Squares,arPLS)结合扩展乘性散射校正(Extended Multiplicative Signal Correction,EMSC)来消除噪声和荧光信号对模型的影响。为了检测方法的重复性,对30个相同啶虫脒含量(20 mg/kg)的苹果进行了拉曼信号采集,并对627 cm~(-1),835 cm~(-1)和1 107 cm~(-1) 3个特征峰强度进行分析,其相对标准偏差(r Relative s Standard d Deviation,RSD)分别为6.14%,6.83%,6.99%,说明该方法具有较好的重复性。采集含有梯度浓度啶虫脒的苹果(0.012 mg/kg～10.830 mg/kg)的信号时,最低检测限为0.035 mg/kg,远低于国家规定的标准0.8 mg/kg。建立的苹果中啶虫脒农药残留偏最小二乘回归(Partial Least Squares Regression,PLSR)预测模型效果较好,检测范围在0.082～3.830 mg/kg,预测相关系数(Rrediction coefficient,R_p)为0.974,预测集均方根误差(Root Mean Square Errors of prediction,RMSEp)为0.044 1 mg/kg,校正相关系数(Correlation coefficient,R_c)为0.986,校正集均方根误差(Root Mean Square Errors of calibration,RMSEc)为0.036 9 mg/kg。研究表明,该方法可以对苹果中残留的啶虫脒农药进行准确的定量预测。

Nondestructive quantitative analysis of acetamiprid in apple based on enhanced raman spectra

Abstract: Determining the pesticide residues in fruits is of great significance to identify the edible safety of food for better sales volume. However, the non-destructive technology is still lacking for the detection of pesticide residues in apples, particularly for high efficiency, cost-saving, and easy operation. In this study, a new non-destructive analysis was developed to detect the pesticide residues using Raman spectroscopy. It also highly contributed to the rapid non-destructive detection and quantitative evaluation of pesticide residues on fruits. Surface Enhanced Raman Spectroscopy (SERS) was utilized to explore, where a new nicotinic pesticide with acetamiprid for pest control in apple production was taken as the research object, whereas, the rapid detection of apple pesticide residues using silver sol (AgNPs) as the enhanced substrate. The polymer sodium polyacrylate was added to the silver sol as a stabilizer, further to prevent the oxidation and deposition of silver sol during long-time storage. The pH value of silver sol was adjusted to change the adsorption state of pesticide molecules on the surface of silver colloidal particles. The silver sol presented the best enhancing performance on the Raman scattering of acetamiprid pesticidewhen the pH value was 6.5. Furthermore, 1 mol/L NaCl solution as a coagulant and mixed with silver sol in a ratio of 1:5 was greatly improved the SERS effect of acetamiprid, further promoting the adsorption between pesticide and silver colloidal particles. The SERS spectra of acetamiprid collected by the improved silver sol showed that there was a great enhancement in the effect of improved silver sol on Raman scattering. Specifically, the coefficient of variation of the SERS spectrum was reduced from 0.0625 to 0.0307, and the relative standard deviations of characteristic peak intensity at 627, 835, and 1107 cm-1 were 6.14%, 6.83%, and 6.99%, respectively. The minimum detection limit of acetamiprid was reduced from 0.683 to 0.035 mg·kg-1. The improved silver sol was used to collect the SERS spectrum of apple samples containing acetamiprid. Different pretreatment and modeling were used to establish the prediction model of acetamiprid residue concentration on the surface of apple samples. The results showed that: A prediction model of acetamiprid pesticide residue in the apple was successfully established using the Kalman smoothing (Rauch-Tung-Striebel, RTS), fluorescence background deduction (asymmetrically reweighted Penalized Least Squares, arPLS), and extended multiplicative scattering correction (Extended Multiplicative Signal Correction, EMSC) pretreatment combined with partial least squares in the Raman spectral range of 400-2300 cm-1. The predicted correlation coefficient (prediction coefficient, Rp) was 0.974, the root mean square error of prediction (Root Mean Square Error of predictions, RMSEp) was 0.0441 mg/kg, the corrected correlation coefficient (correlation coefficient, Rc) was 0.986, and the corrected root mean square error (Root Mean Square Errors of calibration, RMSEc) was 0.0369 mg/kg. When collecting signals from apples, in which the content of acetamiprid was in the range of 0.012 mg/kg and 10.830 mg/kg, the lowest detection limit of acetamiprid in the apple was 0.035 mg/kg, one order of magnitude lower than the detection limit of 0.8 mg/kg in the national standard. Consequently, the SERS technology can widely be expected to qualitatively and quantitatively analyze acetamiprid pesticide residues in apples.