Multisensor Sunflower Oil Quality Assessment System Based on “Electronic Nose”

The current paper demonstrates the application of artificial neural networks for accurately classifying sunflower oil based on gas sensor responses. Through a cost-effective, custom-designed electronic nose with a gas sensor module, experimental data is gathered. The trained classification neural network exhibits very good precision in recognizing different classes of sunflower oil. The developed device can be used as a basis for the creation of a mobile device for recognizing the quality of food products, using gas sensors.