Fish freshness testing with Artificial Neural Networks

In this work, with the use of an electronic nose which has 8 metal oxide gas sensors and was set up at Karadeniz Technical University, a fish freshness system was designed. There are 7 classes (1, 3, 5, 7, 9, 11, 13 day for fish storage) for classification and to perform classification process, Artificial Neural Networks was used in this work. To increase the classification success, Artificial Neural Network architecture, activation functions and input data obtained from different feature extraction method was changed, the storage condition is very important factor for fish freshness and fishes used in this study were stored at fish market conditions. In this study to determine the classification success, 5-Fold Cross Validation method was used and the maximum success rate was obtained as 98.94 %.