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The presence of hidden fungal disease inside pomegranate fruit has reduced the price in the trade of the pomegranate. Alternaria spp. is a widespread fungal disease threatening pomegranate quality. The present study aimed to examine the efficiency of the Electronic nose (E-nose) system as a fast, nondestructive, and low-cost method in diagnosis the amount of Alternaria fungi of the pomegranate. Sixty samples were classified to 0, 25, 50, 75, and 100% amount of Alternaria spp. Linear Discriminant Analysis (LDA), Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods were applied and compared as linear and non-linear analysis methods for detection. The results showed that the LDA method successfully detected healthy and infected samples with 100% accuracy, only by using two Metal Oxide Semiconductor (MOS) sensors. As a prediction method, BPNN showed higher accuracy of 100% in the detection of 0, 25, 50, 75, and 100% infected pomegranates. The results indicated that the E-nose technique is a reliable instrument to detect the quality of the pomegranate with high precision.