Olive Oil Classification and Fraud Detection Using E-Nose and Ultrasonic System

Abstract

The increasing interest of people for consumption of safe food has necessitated the research on fraud detection in foods. In this research, fraud samples of extra virgin olive oil were detected by fusion of E-nose and ultrasound systems. The data were analyzed with seven different classification algorithms and the samples were prepared in six levels of fraud. The principal component analysis was used for feature reduction and seven different classification models were also used for classification and fraud detection. Based on the results, the most effective features in classification were “losses in ultrasound wave amplitude percentage,” “ultrasound signal’s time of flight,” and “difference between maximum and minimum output of TGS2620 gas sensor,” respectively. As well, the results indicated that the Gradient Boosting Classifier (GBC) model represented the best classification accuracy (97.75%) which had the maximum calculation time, compared with the other methods. Furthermore, both NaiveBayes and linear SVM methods were found to be effective methods (95.51%) after GBC.