Abstract: Abstract: Cheaper animal protein, such as Chicken as an example, has been fraudulently used as a substitute for more expensive animal proteins, like mutton and beef. The adulteration of mutton has attracted increasing attention. It requires reliable methods for the authentication of mutton adulteration. An electronic tongue with chemically modified field-effect-transistor sensors was employed to analysis the adulteration of chicken in minced mutton. The effects of sample weight on the sensor responses of electronic tongue were studied at three different extraction solutions. Analysis of variance found that the sample weight affected the responses of the sensor significantly. With the help of Principle component analysis (PCA), the optimum experimental parameters were acquired: 15 g sample extracted by 100 mL KCl solution.The adulterated mutton was made by mixing mutton with chicken at levels of 0, 20%, 40%, 60%, 80%, and 100% by weight, respectively. With the optimum experimental parameters, 168 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 content of chicken, and prediction of the content of chicken in minced mutton. With PCA, the adulterated mutton samples were grouped according to their content of chicken with good classification results, except that samples containing 80% and 100% chicken partially overlapped with each other. Better classification results were found when canonical discriminant analysis (CDA) was employed, as samples containing 80% and 100% chicken were clearly grouped and separated. Multiple linear regression (MLR) 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 a high determination coefficient as high as 0.9925 and 0.9923, respectively. MLR was more effective for the prediction of chicken content. The E-nose proved to be a useful authentication method for meat adulteration detection for its efficiency and high accuracy.