PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Chimenti M., De Rossi D., Di Francesco F., Domenici C., Pieri G., Pioggia G., Salvetti O. A neural approach for improving the measurement capability of an electronic nose. In: Measurement Science & Technology, vol. 14 pp. 815 - 821. IOP, 2003.
 
 
Abstract
(English)
Electronic noses, instruments for automatic recognition of odours, are typically composed of an array of partially selective sensors, a sampling system, a data acquisition device and a data processing system. For the purpose of evaluating the quality of olive oil, an electronic nose based on an array of conducting polymer sensor capable of discriminating olive oil aroma was developed. The selection of suitable pattern recognition techniques for a particular application can enhance the performance of electronic noses. Therefore, an advanced neural recognition algorithm for improving the measurement capability of the device was designed and implemented. This method combines multivariate statistical analysis and a hierarchical neural-network architecture based on self-organizing maps and error back-propagation. The complete system was tested using samples composed of characteristic olive oil aromatic components in refined olive oil. The results obtained have shown that this approach is effective in grouping aromas into different categories representative of their chemical structure.
Subject electronic nose
I.5.1 PATTERN RECOGNITION. Models
B.7 INTEGRATED CIRCUITS
J.7 COMPUTERS IN OTHER SYSTEMS


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