Application of an Electronic Nose System Coupled with Artificial Neural Network for Classification of Banana Samples During Shelf-life Process

In this research, an electronic nose (e-nose) system was used to discriminate the volatile odors produced by banana during shelf-life process. A measurement system, equipped with six metal oxide semiconductor (MOS) sensors, was used to generate a recognition pattern of the volatile compounds of the banana samples. For pattern classification on data obtained from the sensor array of the electronic nose system, back-propagation multilayer perceptron (BP-MLP) neural network was used. By using BP-MLP technique, 97.33 and 94.44% classification successes were achieved for ripening and senescence period of banana respectively. Sensor array ability in classification of shelf-life stages using support vector machines (SVM) analysis was investigated which leaded to develop the application of a specific e-nose system by using the most effective sensors or ignoring the redundant sensors. According to the results, it is concluded that the electronic nose could be a useful tool for discriminating between shelf-life stages of banana.