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BioMed Research International
Volume 2014 (2014), Article ID 529519, 6 pages
Research Article

A Novel Electronic Nose as Adaptable Device to Judge Microbiological Quality and Safety in Foodstuff

1Department of Life Sciences, University of Modena and Reggio Emilia, Via Amendola, 42122 Reggio Emilia, Italy
2CNR-INO Sensor Lab, Via Valotti 9, 25133 Brescia, Italy
3CNR IBF, Via Ugo La Malfa 153, 90146 Palermo, Italy
4Department of Information Engineering, University of Brescia, Via Valotti, 25133 Brescia, Italy
5University of Modena and Reggio Emilia, DISMI, Via Amendola, 42122 Reggio Emilia, Italy

Received 27 November 2013; Accepted 30 January 2014; Published 24 March 2014

Academic Editor: Moreno Bondi

Copyright © 2014 V. Sberveglieri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents different applications, in various foodstuffs, by a novel electronic nose (EN) based on a mixed metal oxide sensors array composed of thin films as well as nanowires. The electronic nose used for this work has been done, starting from the commercial model EOS835 produced by SACMI Scarl. The SENSOR Lab (CNR-INO, Brescia) has produced both typologies of sensors, classical MOX and the new technologies with nanowire. The aim of this work was to test and to illustrate the broad spectrum of potential uses of the EN technique in food quality control and microbial contamination diagnosis. The EN technique was coupled with classical microbiological and chemical techniques, like gas chromatography with mass spectroscopy (GC-MS) with SPME technique. Three different scenarios are presented: (a) detection of indigenous mould in green coffee beans, (b) selection of microbiological spoilage of Lactic Acid Bacteria (LAB), and (c) monitoring of potable water. In each case, the novel EN was able to identify the spoiled product by means of the alterations in the pattern of volatile organic compounds (VOCs), reconstructed by principal component analysis (PCA) of the sensor responses. The achieved results strongly encourage the use of EN in industrial laboratories. Finally, recent trends and future directions are illustrated.