Gro{elj et al.: The Use of FT-MIR Spectroscopy and... Scientific paper The Use of FT-MIR Spectroscopy and Counter-Propagation Artificial Neural Networks for Tracing the Adulteration of Olive Oil

Dedicated to the memory of Professor Ljubo Goli~ The aim of this work is to detect the presence of refined hazelnut oil in refined olive oil, using the Counter-propagation Artificial Neural Networks (CP-ANN) model. The oil samples were analyzed by FT-MIR spectroscopy. They were clas-sified as pure olive oil (Class 1), pure hazelnut oil (Class 2), and two type of adulterated olive oil samples, one with mo-re than (or equal to) 10 % of hazelnut oil (Class 3), and the other with less than 10 % of hazelnut oil (Class 4). In addi-tion, an external set of blind samples was also analyzed by FT-MIR. Five CP-ANN models with different number of selected infrared spectral regions were built up and tested for their clas-sification ability. On the basis of leave-one-out cross validation procedure the best models were selected and further used for blind samples prediction. The results obtained show that the models clearly separate different groups and clas-sify correctly the pure olive oil and the hazelnut oil. Moreover a reasonable discrimination between both mixtures and pure oils was achieved