Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network
AbstractElectronic 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Adak, M.F.; Yumusak, N. Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network. Sensors 2016, 16, 304.
Adak MF, Yumusak N. Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network. Sensors. 2016; 16(3):304.Chicago/Turabian Style
Adak, M. F.; Yumusak, Nejat. 2016. "Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network." Sensors 16, no. 3: 304.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.