表面增强拉曼光谱法快速检测脐橙果肉中三唑磷农药残留

doi:10.3964/j.issn.1000-0593(2016)03-0736-07

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摘要：采用表面增强拉曼光谱技术结合快速溶剂前处理方法检测脐橙果肉中三唑磷农药残留，应用化学计量学方法建立脐橙果肉中三唑磷农药残留的快速检测模型。以脐橙果肉提取液为基质，采用N-丙基乙二胺、C18和石墨化碳去除果肉中有机酸、色素等荧光物质，配制不同浓度的三唑磷农药溶液，应用不同预处理方法对光谱信号进行预处理，建立偏最小二乘模型。结果表明，以脐橙果肉提取液为基质的三唑磷溶液最低检测浓度低于0.5mg·L^-1;归一化预处理后建立的模型预测性能最好，模型对预测集样本的均方根误差为1.38 mg·L^-1，相关系数为0.976 6，相对分析误差为(RPD)4.66。预测回收率为95.97%～103.18%，相对误差绝对值在5%以下，表明模型具有较好的预测效果。对4个未知浓度样本进行配对t检验，预测值与真实值无显著差异，说明所建立的方法准确可靠。

关键词：表面增强拉曼光谱;脐橙果肉;三唑磷;快速检测

Abstract：Surface enhanced Raman spectroscopy (SERS) and quick pre-treatment technology were used to detect triazophos residues in flesh of navel orange. Quantitative analysis model was developed by partial least squares (PLS) algorithm. SERS of different concentration (0.5 to 20 mg·L^-1) triazophos juice solution with flesh extract as the matrix were collected by laser Raman spectrometer. Three preprocessing methods such as normalization, MSC and SNV were used to optimize Raman signals and PLS models were set up. The results showed that minimum detection concentration for triazophos in navel orange below 0.5 mg·L^-1. The model built with normalization pre-processing gave the best result; the values of correlation (R_p) and Root mean square error of prediction set (RMSEP) were 1.38 and 0.976 6, respectively. The predict recoveries were 95.97%～103.18% and the absolute values of relative errors were below 5%. T-test (t=-0.018) showed that there was no significant difference between the true values and prediction values. This study demonstrates that this method is accurate and reliable.

Key words：Surface-enhanced Raman spectroscopy;Flesh of navel orange;Triazophos;Detection

收稿日期: 2014-10-16 修订日期: 2015-01-20

中图分类号:

O657.3

通讯作者: 陈金印 E-mail: jinyinchen@126.com

引用本文:

王晓彬¹，吴瑞梅¹，凌晶²，刘木华¹，张庐陵¹，蔺磊¹，陈金印^3* . 表面增强拉曼光谱法快速检测脐橙果肉中三唑磷农药残留 [J]. 光谱学与光谱分析, 2016, 36(03): 736-742.
WANG Xiao-bin¹, WU Rui-mei¹, LING Jing², LIU Mu-hua¹, ZHANG Lu-ling¹, LIN Lei¹, CHEN Jin-yin^3* . Study on the Rapid Detection of Triazophos Residues in Flesh of Navel Orange by Using Surface-Enhanced Raman Scattering. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(03): 736-742.

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