Diagnosis of adulteration in cow ghee is one of the key concerns of recent years. In this study, the aroma fingerprints of cow ghee were detected. For this purpose, an electronic nose system was developed and its ability for detection of different amounts of margarine mixed with pure cow ghee (10, 20, 30, 40, and 50% levels) was investigated. The system was equipped with eight sensors (MOS type), that each of them reacts to specific volatile compounds in the samples. The features of the signals were considered for data analysis. In this research, principal components analysis (PCA) and artificial neural networks (ANN) methods were used for data analyzing. According to the results, the PCA analysis explained 98% of the variance in the data set. Also, ANN analysis identified 85.6 and 97.2% of pure cow ghee from the adulteration ones when samples were classified in 7 and 2 classes, respectively.
