Abstract: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.
