Feasibility of using a cylindrical resonator sensor for adulteration detection in sesame oil

One of the important concerns regarding the food safety is widespread adulteration in food production which necessitates the development of precise and rapid diagnostic instruments more than ever. In this study, a cylindrical resonator sensor, developed in previous studies and examined for quality detection of some food materials, was applied for detection of adulteration in sesame oil mixed with corn, canola and sunflower oils. To measure the dielectric response of oil in 0-150 MHz frequency range, a new sensor with 400 mm long cylinder was fabricated. Adulteration samples were prepared with mixing pure sesame oil with the above mentioned oils at 15, 30 and 45% by weight and each sample was measured with three replications using the dielectric sensor. Statistical methods of principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machine (SVM) were evaluated for detection of adulteration type and level from the dielectric spectral data. The biplot of the first two principal components showed excellent discrimination of adulteration type and level with 96% of variation explanation. As the best result, LDA classifier showed 96.7% accuracy for adulteration detection.