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

摘要

In this study, an electronic nose coupled with artificial neural network (ANN) wasused to predict the shelf life of two oil with new production date and oil with oldproduction date over a period of 150 days. According to the American Oil Chemists’Society results, the oils were oxidized after 60 days. Principal component analysisresults indicated that all the oil samples were correctly discriminated from each otherduring their storage times, and samples of oxidized and nonoxidized oils can be properlydistinguished from each other. Two main components (PC1, PC2) managed to describe97% of the data set variance concerning the shelf life of the oil. To develop theANN 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 squareerror) were used. The results revealed that the ANN model can be used as a powerfultool for pattern recognition and determination of the shelf life of oil and its oxidationdegree at high precision. Scientific and feasible results can be obtained by matchingANN and the results obtained by metal oxide semiconductor sensors of E-nose.