为实现掺假羊肉的快速、客观检测，利用电子鼻定性和定量分析混入鸡肉的掺假羊肉糜。单因素试验表明顶空体积、载气流速、样品量和顶空生成时间对电子鼻传感器的响应影响极显著；主成分分析确定了电子鼻检测的较佳条件：样品量10 g、载气流速200 mL/min、顶空容积250 mL及顶空生成时间30 min。在此条件下检测混入鸡肉的掺假羊肉，结果发现采用主成分分析时，掺入鸡肉的比例随主成分一降低而增大，但相邻比例彼此重叠，难以有效区分；采用典则判别分析时，混入不同比例鸡肉的羊肉糜样品能较好地区分；采用主成分回归分析和偏最小二乘回归分析建立的定量预测模型（R2>0.95）能有效预测混入的鸡肉比例。电子鼻在混入鸡肉的掺假羊肉鉴别中具有可行性，论文可为羊肉掺假鉴别提供理论依据。

The adulteration of mutton has attracted increasing attention that requires reliable methods for the authentication. An electronic nose (Pen 2) was employed to analysis the adulteration of chicken in minced mutton. The effects of sample weight, headspace-generated time, headspace volume and flow rate of carrier gas on sensor responses were studied by single-factor experiment. Results of one-way analysis of variance found that the responses of electronic sensors were significantly affected by these factors. The optimum experimental parameters were 10 g sample with 30 min headspace-generated time in 250 mL beaker with a flow rate of 200 mL/min by using principle component analysis (PCA). The adulterated mutton was made by mixing mutton with chicken at different proportions. With the optimum experimental parameters, 144 samples of adulterated mutton were detected and the signals were analyzed by pattern recognition techniques to build models for classification of adulterated mutton with different proportions of chicken and prediction of the content of chicken in minced mutton. With PCA and CDA, the adulterated mutton samples were grouped according to their content of chicken with overlapping with each other, and better classification results were found with CDA. Principle component regression (PCR) and partial least square analysis (PLS) were employed to build the predictive model for the content of chicken adulterated into minced mutton. Both models could predict the adulteration with high determination coefficient (higher than 0.95). PCR was more effective for the prediction of chicken content. The E-nose has been proved to be a useful authentication method for meat adulteration detection for its efficiency and high accuracy.

国家科技支撑计划项目（2012BAD29B02-4）；国家自然科学基金项目（31071548）；高等学校博士点基金项目（20100101110133）