以表面增强试剂OTR202和OTR103作为表面增强拉曼光谱（SERS）的活性基底，探索建立一种鸭肉中强力霉素(DC)残留的SERS检测方法。首先对DC水溶液、鸭肉提取液和含DC的鸭肉提取液的SERS光谱特征进行了对比分析。然后研究了含DC的鸭肉提取液加入量和吸附时间分别对SERS信号强度的影响，确定了含DC的鸭肉提取液加入量为20 μL，最佳吸附时间为1 min。最后取1270与1242 cm-1 SERS信号强度比（I1270/I1242）来建立预测鸭肉提取液中DC残留的标准曲线。试验结果表明，鸭肉提取液中DC质量浓度范围为4~30 mg/L时，鸭肉提取液中DC质量浓度与I1270/I1242之间呈良好的线性关系。应用得到的标准曲线对含不同质量浓度DC的鸭肉提取液进行预测，预测集的相关系数(r)和预测均方根误差(RMSEP)分别为0.9762和3.0036，鸭肉提取液中DC的检出限可达1.346 mg/L，这说明本文研究方法用于鸭肉中的DC残留快速检测是可行的。

The surface strengthening agents OTR202 and OTR103 were used as active substrates for surface enhanced Raman spectroscopy (SERS), and a method for the detection of doxycycline residues in duck meat using SERS was established in this study. First, the SERS spectral characteristics of doxycycline aqueous solution, duck meat extract, and duck meat extract containing doxycycline were analyzed and compared. The effects of the amount of added duck meat extract containing doxycycline and the adsorption time on SERS intensity were then investigated, and the optimum amount of added duck meat extract containing doxycycline and optimum adsorption time were 20 μL and 1 min, respectively. A standard curve to predict the doxycycline residues in duck meat was established using the SERS intensity ratio at 1270 and 1242 cm-1 (I1270/I1242). The experimental results showed that a good linear relationship between the doxycycline concentration in duck meat extract and I1270/I1242 was obtained when the doxycycline concentration in duck meat extract was between 4 and 30 mg/L. The obtained standard curve was utilized to predict the doxycycline concentration in the duck meat extract, and the correlation coefficient (r) and the root mean squared error of prediction (RMSEP) were 0.9762 and 3.0036, respectively. The detection limit of doxycycline in duck meat extract reached 1.346 mg/L. The experimental results showed that it was feasible to predict doxycycline residue levels in duck meat using the method described in this paper.

国家自然科学基金项目（31660485）；国家自然科学基金项目（31101295）；江西省科技厅对外科技合作计划（20132BDH80005）；江西省科技厅科技支撑项目（20121BBG70058）