Abstract：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.
[1] Li R, He L, Wei W, et al. Food Control, 2015, 51: 212. [2] Zainudin B H, Salleh S, Mohamed R, et al. Food Chem., 2015, 172: 585. [3] JIANG Xue-song, WANG Wei-qin, LU Li-qun, et al(蒋雪松，王维琴，卢利群，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(12): 278. [4] He S, Xie W, Zhang W, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2015, 137: 1092. [5] ZHAI Chen, PENG Yan-kun, LI Yong-yu, et al(翟 晨，彭彦昆，李永玉，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(8): 2180. [6] Lv M Y, Teng H Y, Chen Z Y, et al. Sensors and Actuators B: Chemical, 2015, 209: 820. [7] Strickland A D, Batt C A. Analytical Chemistry, 2009, 81(8): 2895. [8] Kubackova J, Fabriciova G, Miskovsky P, et al. Anal. Chem., 2015, 87(1): 663. [9] GAO Si-min, WANG Hong-yan, LIN Yue-xia, et al(高思敏，王红艳，林月霞，等). Acta Physico-Chimica Sinica(物理化学学报), 2014, 30(10): 1916. [10] CHEN Xu-cheng, ZHAO Ai-wu, GAO Qian, et al(陈旭成，赵爱武，高 倩，等). Acta Chimica Sinica(化学学报), 2014, (4): 467. [11] Leopold N, Lendl B. Journal of Physical Chemistry B, 2003, 107(24): 5723. [12] Fan Y X, Lai K Q, Rasco B A, et al. Food Control, 2014, 37: 153. [13] Wijaya W, Pang S, Labuza T P, et al. Journal of Food Science, 2014, 79(4): T743. [14] Xie Y, Mukamurezi G, Sun Y, et al. European Food Research and Technology, 2012, 234(6): 1091. [15] Dhakal S, Li Y, Peng Y, et al. Journal of Food Engineering, 2014, 123: 94. [16] XU Tian-feng, PENG Yan-kun, LI Yong-yu, et al(徐田锋，彭彦昆，李永玉，等). Journal of Food Safety & Quality(食品安全质量检测学报), 2014, (3): 707. [17] Shende C, Inscore F, Sengupta A, et al. Sensing and Instrumentation for Food Quality and Safety, 2010, 4(3-4): 101. [18] Zhang L, Wang B, Zhu G, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2014, 133: 411.
