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Detection of Chlorpyrifos Residues in Green Tea Using SERS and Rapid Pretreatment Method |
ZHU Xiao-yu1, AI Shi-rong2, XIONG Ai-hua2, DU Juan1, HUANG Jun-shi2, LIU Peng2, HU Xiao3, WU Rui-mei2* |
1. College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang 330045, China
2. School of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
3. School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China |
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Abstract Tea is one of the main economic-crops in China. During tea planting, the unreasonable use and abuse of pesticides lead to serious pesticide residues in tea. At present, the classical chemical laboratory methods are adopted to detect the pesticide residues in tea, but there are some shortcomings such as complex pretreatment, time-consumption and high cost in the laboratory methods. Therefore, it is urgent to study the rapid detection method of pesticide residues in tea to supervise the quality and safety of tea market. In this study, Nano Bamboo Charcoal (NBC) purifier was used to reduce the matrix-induced enhancement of tea substrate. Surface-enhanced Raman spectroscopy (SERS) was employed to detect Chlorpyrifos residues in green tea, and a rapid detection method for analyzing Chlorpyrifos residues in green tea was developed. Different doses of NBC (0, 15, 20, 25, 30 mg) were used to remove the matrix effects. The optimal dosage of NBC was obtained by comparing the purification effect and SERS of different NBC dosage. The recovery experiment was carried to verify the reliability of the optimized pretreatment method. The results showed that NBC had obvious purification effect and the green tea substrates such as pigment and so on matrix-induced enhancement were reducing when the dosage of NBC was 20 mg. It was proved that this optimized pretreatment method was suitable for decreasing the matrix-induced enhancement of tea substrate by recovery tests. Density functional theory was used to simulate the theory Raman spectrum of chlorpyrifos. The theoretical and experimental Raman spectrums of chlorpyrifos were compared and spectral peaks of their functional groups were assigned. Five characteristic peaks of Chlorpyrifos in green tea such as 526, 560, 674, 760 and 1 096 cm-1 were found. Within the scope of the concentration of 0.28~11.11 mg·kg-1, a line relation was developed between the peak intensity of 1 096 cm-1 and the concentration of Chlorpyrifos of tea extract, y=0.017 5x+0.909 2 and R2 was 0.986 2, indicating a good linear relationship. The average recovery rates of the method were 96.71%~105.24%, and the relative standard deviations (RSD) were between 2.36%~3.65%. The minimum detection concentration of Chlorpyrifos residues detected by this method was about 0.56 mg·kg-1 and the detection time of a single sample was performed within 15 minutes. The result demonstrated that SERS combined with rapid pretreatment method was feasible for rapidly detecting pesticide residue in tea.
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Received: 2018-12-17
Accepted: 2019-04-26
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Corresponding Authors:
WU Rui-mei
E-mail: wuruimei036@163.com
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[1] Thräne C, Isemer C, Engelhardt U H. European Food Research and Technology, 2015, 241(2): 227.
[2] Li J, Sun M, Chang Q, et al. Chromatographia, 2017, 80(9): 1447.
[3] Muehlwald S, Buchner N, Kroh L W. Journal of Chromatography A, 2018, 1542: 37.
[4] WANG Hui, DUAN Yu-yao, LI Xiao, et al(王 辉,段玉瑶,李 笑,等). Modern Food Science & Technology(现代食品科技), 2016, 32(2): 276.
[5] Liu B, Ge Y, Zhang Y, et al. Food and Agricultural Immunology, 2014, 23(2): 157.
[6] Luo Hairui, Huang Yiqun, Lai Keqiang, et al. Food Control, 2016, 68: 229.
[7] Tu Q, Hickey M E, Yang T, et al. Food Control, 2019, 96: 16.
[8] Wang K, Sun D, Pu H, et al. Talanta, 2019, 191: 449.
[9] Fan Y, Lai K, Rasco B A, et al. LWT-Food Science and Technology, 2015, 60(1): 352.
[10] ZHAI Chen, XU Tian-feng, PENG Yan-kun, et al(翟 晨,徐田锋,彭彦昆,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016,36(9): 2835.
[11] Liu D, Han Y, Zhu L, et al. Food Analytical Methods, 2017, 10(5): 1202.
[12] Luo H, Huang Y, Lai K, et al. Food Control, 2016, 68: 229.
[13] LIN Lei,WU Rui-mei,GUO Ping, et al(蔺 磊,吴瑞梅,郭 平,等). Modern Food Science & Technology(现代食品科技), 2015,(5): 291.
[14] ZHANG Lin, ZENG Yong-ming, ZHAO Jian-jun, et al(张 琳,曾勇明,赵建军,等). Scientia Sinica Chimica(中国科学:化学), 2017, 47(6): 794.
[15] WANG Hong-mei, LI Ling-ling, CHEN Hai-bing, et al(王红梅,李玲玲,陈海滨,等). Chemical Journal of Chinese Uiversities(高等学校化学学报), 2017, 38(6): 1040.
[16] Shende C, InscoreF , Sengupta A, et al. Sens. & Indtrumen. Food Qual., 2010, 4(3-4): 101.
[17] ZHAI Chen, PENG Yan-kun, LI Yong-yu, et al(翟 晨,彭彦昆,李永玉,等). Acta Chimica Sinica(化学学报), 2015,(73): 1167. |
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