Electronic nose based on metal oxide semiconductor sensors and pattern recognition techniques: characterisation of vegetable oils

Different supervised pattern recognition treatments were applied to the signals generated by an electronic nose for the classification of vegetable oils. The system, comprising six metal oxide semiconductor sensors, was used to generate a pattern of the volatile compounds present in the samples. Feature selection techniques were employed to choose a set of optimally discriminant variables. The K-nearest neighbours (KNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), soft independent modelling of class analogy (SIMCA) and artificial neural networks (ANN) were applied to model the different classes. The results obtained indicated good classification and prediction capabilities, the neural networks being those that afforded the best results.