郭红青,刘木华,袁海超,赵进辉,彭义杰,李耀,陶进江.表面增强拉曼光谱技术快速检测鸭肉中的土霉素[J].食品安全质量检测学报,2017,8(1):169-176 |
表面增强拉曼光谱技术快速检测鸭肉中的土霉素 |
Rapid detection of oxytetracycline in duck meat by surface-enhanced Raman spectroscopy |
投稿时间:2016-10-23 修订日期:2017-01-05 |
DOI: |
中文关键词: 表面增强拉曼光谱 鸭肉 土霉素 纳米金胶 |
英文关键词:surface-enhanced Raman spectroscopy duck meat oxytetracycline Au nanoparticles |
基金项目:国家自然科学基金项目(31660485)、江西省科技厅对外科技合作计划项目(20132BDH80005)、江西省科技厅科技支撑计划项目(2012BBG70058)、江西省教育厅科技计划资助项目(GJJ12244) |
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Author | Institution |
GUO Hong-Qing | Optics-Electrics Application of Biomaterials Lab, College of Engineering, Jiangxi Agricultural University |
LIU Mu-Hua | Optics-Electrics Application of Biomaterials Lab, College of Engineering, Jiangxi Agricultural University |
YUAN Hai-Chao | Optics-Electrics Application of Biomaterials Lab, College of Engineering, Jiangxi Agricultural University |
ZHAO Jin-Hui | Optics-Electrics Application of Biomaterials Lab, College of Engineering, Jiangxi Agricultural University |
PENG Yi-Jie | Optics-Electrics Application of Biomaterials Lab, College of Engineering, Jiangxi Agricultural University |
LI Yao | Optics-Electrics Application of Biomaterials Lab, College of Engineering, Jiangxi Agricultural University |
TAO Jin-Jiang | Optics-Electrics Application of Biomaterials Lab, College of Engineering, Jiangxi Agricultural University |
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中文摘要: |
目的 采用纳米金胶和OTR103作为表面增强拉曼光谱(surface-enhanced Raman spectroscopy, SERS)的活性基底, 实现鸭肉中土霉素残留量的快速检测。方法 首先使用自适应迭代惩罚最小二乘法(adaptive iterative re-weighted penalized least squares, air-PLS)扣除SERS测定过程中的荧光等背景信号, 确定鸭肉中土霉素检测的特征峰。然后应用单变量分析法对纳米金胶、待测样品、OTR103的加入量和吸附时间进行优化, 确定最佳实验条件。结果 拉曼位移为1271 cm-1处的特征峰可以作为鸭肉中土霉素残留检测的拉曼特征峰, 纳米金胶、待测样品和OTR103的最适加入量分别为0.7 mL、70 μL和100 μL, 最佳吸附时间为5 min。鸭肉中的土霉素浓度范围为0.2~22.0 mg/L时, 土霉素浓度(X)与其在1271 cm-1处的SERS特征峰强度(Y)之间有良好的线性关系, 线性回归方程为Y=245.24X+647.29, 决定系数(RC2)为0.9891, 检测限为0.2 mg/L。预测集样本中土霉素含量的真实值与预测值之间的决定系数(RP2)为0.9941, 均方根误差(RMSEP)为1.1341 mg/L, 回收率为74%~102%。结论 该方法可用于鸭肉中土霉素残留的快速检测。 |
英文摘要: |
Objective To achieve the rapid detection of oxytetracycline (OTC) residues in duck meat using Au nanoparticles and OTR103 as active substrate of surface-enhanced Raman spectroscopy (SERS). Methods Firstly, the adaptive iterative re-weighted penalized least squares (air-PLS) was used to subtract the background signals such as fluorescence background, and the characteristic peaks of OTC in detection of duck meat were determined. Then the amounts of Au nanoparticles, the sample to be tested and OTR103 were optimized by single variable analysis in order to determine the optimal experimental conditions. Results The peak at 1271cm-1 (Raman shift) was considered as Raman characteristic peak for the detection of OTC residues in duck meat. The optimum adding amounts of Au nanoparticles, the sample to be tested and OTR103 were 0.7 mL, 70 μL and 100 μL, respectively, and the best adsorption time was 5 min. There was a good linear relationship between the concentration of OTC residues in duck meat (X) and the peak intensity of SERS at 1271cm-1 (Y), the linear equation was Y=245.24X+647.29, the coefficient of determination (RC2) was 0.9891 and the detection limit was 0.2 mg/L. The determination coefficient (RP2) between the actual value and predictive value of OTC content in the prediction samples was 0.9941, the root mean square error (RMSEP) was 1.1341 mg/L, and the recoveries were 74%~102%. Conclusion The established method can be used for the rapid detection of OTC residues in duck meat. |
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