利用表面增强拉曼光谱(SERS)技术, 建立了一种用于菠菜中毒死蜱农药残留的非破坏、 快速检测方法。 以碱性环境下盐酸羟胺还原法制备的银溶胶作为表面增强剂滴涂于菠菜样品表面后, 采用实验室自行搭建的拉曼系统直接采集样品的拉曼信息, 该方法无需对样品进行前处理, 可以实现菠菜中毒死蜱含量的实时在线定量分析。 采集24片不同毒死蜱含量的菠菜样品拉曼光谱, 每个样品采集20个点。 拉曼信号采集后, 用气相色谱法对24个菠菜样品中毒死蜱含量进行检测。 为了消除光谱噪音以及荧光背景对分析建模的影响, 分别采用Savitzky-Golay平滑和有效峰线性拟合法对原始拉曼光谱进行预处理。 该表面增强拉曼方法具有较好的重复性, 实验中对50个相同毒死蜱含量, 但不同状态的菠菜进行光谱采集, 其相对标准偏差为13.4%, 说明该方法具有一定的普适性。 光谱预处理后, 选取615.5~626.4 cm-1波段为感兴趣区域, 建立0.05~37.4 mg·kg-1浓度范围内毒死蜱含量的多元线性预测模型, 结果表明感兴趣区域的拉曼信号和毒死蜱浓度呈良好的线性关系, 其校正集和验证集相关系数RC和RP分别为0.961和0.954。 该方法的最低检出含量为0.05 mg·kg-1, 低于国家标准规定的农药残留最大限量。 该方法简单快速, 无需样品前处理, 可以实现果蔬的农药残留快速、 定量检测。

In this research, the surface enhanced Raman spectroscopy (SERS) technique is used to develop a nondestructive and fast detecting method for the detection of residual chlorpyrifos on spinach. Silver colloids used for SERS spectroscopy is prepared by the reduction of silver nitrate with hydroxylamine hydrochloride at alkaline pH. The prepared silver colloids are dropped onto spinach samples, then the SERS spectra are collected non-destructively with a self-developed Raman system. This method can be made without physical contact to samples, and rapidly completed without time-consuming sample pre-treatments, and suited to the development of real-time on-line detection methods for trace pesticide residues. SERS signals are collected from 20 points on each spinach sample with 450 mW laser power and 2.5 s exposure time. Chlorpyrifos concentrations in 24 samples are determined with gas chromatography after SERS spectra taken. Savitzky-Golay (SG) smoothing filter and effective peak linear fitting method are used to remove the random noise and the fluorescence background for improving the accuracy of SERS results. The SERS signals are collected from different parts of 50 spinach samples with the same concentration of chlorpyrifos but at different fresh degrees. The relative standard deviation (RSD) of chlorpyrifos' characteristic peak intensities is 13.4%. Although the differences of samples lead to differences in the curves of Raman spectrum, they have little influence on the characteristic peak intensities, which indicates the stability of the proposed detecting method. After the fluorescent background removed, the 20 curves of each sample are averaged. Correlation analysis is done between chlorpyrifos concentration and signal intensity at every Raman shift. Results show that correlation coefficients are higher than 0.85 in the range of 615.5~626.4 cm-1. Signals in this range are used to establish multiple linear regression (MLR) model for the prediction of residual chlorpyrifos. MLR model was developed for chlorpyrifos concentration versus Raman signal intensity at 615.5~626.4 cm-1 for predicting residual chlorpyrifos content in samples, the correlation coefficients of calibration (RC) and validation (RP) are 0.961 and 0.954, which indicate a good linear relationships between them. The minimum detectable threshold for this method is 0.05 mg·kg-1 which is close to the value limited by the national standard of China (0.1 mg·kg-1 for chlorpyrifos in spinach). The proposed practical method is sample, fast, without sample preparation, thus it shows great potential in safety detection of fruits and vegetables.