An electronic nose to classify Iberian pig fats with different fatty acid composition

Abstract  Fatty acid analysis is frequently performed in fat and other raw materials to classify them according to their fatty acid composition, but the need to carry out online determinations has generated a growing interest in more rapid options. This research was done to evaluate the ability of a polymer-sensor based electronic nose to classify Iberian pig fat samples with different fatty acid compositions. Significant correlations were found between individual fatty acids and sensor responses, proving that sensor response data were not fortuitously sorted. Significant correlations also appeared between some sensors and water activity, which was considered during the sample classification. Two supervised pattern recognition techniques were attempted to process the sensor responses: 85.5% of the samples were correctly classified by discriminant analysis, but the percentage increased to 97.8% using a one-hidden layer back-propagation artificial neural network. The electronic nose (specifically, sensor responses analyzed by a neural network) achieved success similar to that obtained using the more usual fatty acid analysis by gas chromatography.