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

Abstract : 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.
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https://hal.univ-lorraine.fr/hal-01372482
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Submitted on : Tuesday, September 27, 2016 - 11:57:35 AM
Last modification on : Wednesday, November 22, 2017 - 10:56:27 AM

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Alireza Sanaeifar, Seyed Saeid Mohtasebi, Mahdi Ghasemi-Varnamkhasti, Maryam Siadat. Application of an Electronic Nose System Coupled with Artificial Neural Network for Classification of Banana Samples During Shelf-life Process. International Conference on Control, Decision and information technologies (CoDIT’14), Nov 2014, Metz, France. ⟨10.1109/CoDIT.2014.6996991⟩. ⟨hal-01372482⟩

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