Identification of olfactory characteristics of edible oil during storage period using metal oxide semiconductor sensor signals and ANN methods

Abstract

In this study, an electronic nose coupled with artificial neural network (ANN) was used to predict the shelf life of two oil with new production date and oil with old production date over a period of 150 days. According to the American Oil Chemists’ Society results, the oils were oxidized after 60 days. Principal component analysis results indicated that all the oil samples were correctly discriminated from each other during their storage times, and samples of oxidized and nonoxidized oils can be properly distinguished from each other. Two main components (PC1, PC2) managed to describe 97% of the data set variance concerning the shelf life of the oil. To develop the ANN models, the data were first divided into three groups: training (60%), validation, and test (40%). To determine the best model, two criteria (R2 and root mean square error) were used. The results revealed that the ANN model can be used as a powerful tool for pattern recognition and determination of the shelf life of oil and its oxidation degree at high precision. Scientific and feasible results can be obtained by matching ANN and the results obtained by metal oxide semiconductor sensors of E-nose. Practical applications One of the most important causes of food spoilage is lipid oxidation. The American Oil Chemists’ Society (AOCS) has developed a variety of methods for assessing the state of food oxidation. In this study, oil shelf life studied by a combination of artificial senses and chemometrics methods. The acidity, peroxide, anisidine, and Totox values of the edible oil samples were measured according to the AOCS standard. Principal component analysis and artificial neural neural methods succeeded in classifying the samples based on their storage time with high accuracy.