Detection of adulteration in camellia seed oil and sesame oil using an electronic nose

Abstract An electronic nose was used for the detection of maize oil adulteration in camellia seed oil and sesame oil. The results of multivariate analysis of variance showed that the sensor signals of different kinds of oil are significantly different from each other. Principal component analysis (PCA) cannot be used to discriminate the adulteration of camellia seed oil, but can be used in the discrimination of adulteration in sesame oil. Linear discriminant analysis (LDA) is more effective than PCA and can be used in adulteration discrimination for both camellia seed oil and sesame oil. In order to check the discriminative power of LDA, canonical discriminant analysis was performed as well. Acceptable results were also obtained: The accuracy of prediction was 83.6% for camellia seed oil and 94.5% for sesame oil. The artificial neural network (ANN) model was used to detect the percentage of adulteration in camellia seed oil and sesame oil. The results showed that, based on ANN as its pattern recognition technique, the electronic nose cannot predict the percentage of adulteration in camellia seed oil, but can be used in the quantitative determination of adulteration in sesame oil.