为快速、准确鉴别市面上羊肉中掺入鸭肉的商品，本研究应用电子鼻结合可见/近红外光谱技术，实现了羊肉中掺入不同比例鸭肉样品的有效鉴别。试验制备了174个羊肉中掺入不同比例鸭肉样品，分别采集了样品电子鼻数据和200~1 100 nm、900~1 700 nm波长范围内的反射光谱数据，利用2分类定性判别和6分类定量检测法分别构建了支持向量机（Support Vector Machine，SVM）和偏最小二乘法（Partial Least Squares，PLS）定性定量判别模型，并用6分类最优模型进行预测。结果表明：电子鼻可以利用不同比例羊肉鸭肉样品间的气味差异对不同组进行判别，羊肉中含有的挥发性香气成分如萜烯类、芳香类、有机硫化物等物质的含量高于鸭肉。基于两个波段数据、两种分类方法构建的PLS模型判别效果优于SVM模型，总的判别正确率均达到96%以上，光谱数据经过多元散射校正处理的效果最佳，且最优模型预测效果良好。电子鼻结合可见/近红外光谱分析技术可有效鉴别羊肉中掺入不同比例鸭肉样品，为羊肉真实性的快速无损鉴别提供技术支撑。

In order to identify the minced mutton products adulterated with duck rapidly and accurately, an electronic nose combined with visible/near-infrared spectroscopy technology was used to realize effective identification. A total of 174 samples of minced mutton adulterated with duck in different proportions were prepared, and electronic nose data and reflection spectra in the wavelength ranges of 200~1 100 nm and 900~1 700 nm were acquired, respectively. Two-classification qualitative discrimination and six-classification quantitative detection methods were used to establish the support vector machine (SVM) and partial least squares (PLS) qualitative and quantitative discrimination models, respectively. Subsequently, the six-classification optimal models were used for prediction based on two spectral bands. The electronic nose detected and identified the six groups through the odor difference. The contents of volatile aroma components such as terpenes, aromatic compounds, and organic sulfides in mutton were higher than those in duck. The PLS models based on two-classification methods and spectral data of two bands were superior to the SVM models, and the total discriminant accuracy was more than 96%. The best spectral pretreatment method was multiplicative scatter correction, and the final optimal model predicted well. In summary, an electronic nose combined with visible/near infrared spectroscopy can effectively identify the mutton samples adulterated with duck, thus providing technical support for rapid and nondestructive identification of mutton adulteration.

中央级公益性科研院所基本科研业务费专项（S2021JBKY-14）；国家农业科技创新工程项目（CAAS-ASTIP-2020-IFST-03）