The Use of FT-MIR Spectroscopy and Counter-Propagation Artificial Neural Networks for Tracing the Adulteration of Olive Oil
Neva Grošelj,1 Marjan Vračko,1 Juan Antonio Fernández Pierna,2 Vincent Baeten1 and Marjana Novič1,*
1 National Institute of Chemistry, Hajdrihova 19, Ljubljana,
2 Quality of Agricultural Products department, Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium
* Corresponding author: E-mail: firstname.lastname@example.org
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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 classified as pure olive oil (Class 1), pure hazelnut oil (Class 2), and two type of adulterated olive oil samples, one with more than (or equal to) 10% of hazelnut oil (Class 3), and the other with less than 10% of hazelnut oil (Class 4). In addition, 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 classification 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 classify correctly the pure olive oil and the hazelnut oil. Moreover a reasonable discrimination between both mixtures and pure oils was achieved.
Keywords: Counter-propagation artificial neural networks, hazelnut oil, MIR spectroscopy, olive oil adulteration.