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Sensors 2016, 16(3), 304; doi:10.3390/s16030304

Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network

Computer Engineering Department, Computer and Information Sciences Faculty, Sakarya University, 2nd Ring Street, Esentepe Campus, Serdivan, Sakarya 54187, Turkey
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Academic Editor: M. Carmen Horrillo Güemes
Received: 21 December 2015 / Revised: 7 February 2016 / Accepted: 23 February 2016 / Published: 27 February 2016
(This article belongs to the Special Issue E-noses: Sensors and Applications)
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Abstract

Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data. View Full-Text
Keywords: aroma data; e-nose; ABC; neural networks; sensors aroma data; e-nose; ABC; neural networks; sensors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Adak, M.F.; Yumusak, N. Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network. Sensors 2016, 16, 304.

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