Discrimination between different samples of olive oil using variable selection techniques and modified fuzzy artmap neural networks

An electronic nose for classification of olive oil samples is presented. Principal component analysis and a modified fuzzy artmap neural network where applied to data acquired from 12 sensors. A custom designed variable selection technique was also used to boost performance. Ten different samples of olive oils were classified with 78% accuracy, and confusion occurred mostly between similar olive oils. Defective samples were separated from defect-free olive oil with 97% accuracy. These results show that careful variable selection, coupled to a modified fuzzy artmap algorithm, can significantly improve electronic nose performance.