Quality detection of pomegranate fruit infected with fungal disease

Abstract

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.