A Novel Electronic Nose as Adaptable Device to Judge Microbiological Quality and Safety in Foodstuff
Author(s) -
Veronica Sberveglieri,
Estefanía Núñez-Carmona,
Elisabetta Comini,
Andrea Ponzoni,
Dario Zappa,
Onofrio Pirrotta,
Andrea Pulvirenti
Publication year - 2014
Publication title -
biomed research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 126
eISSN - 2314-6141
pISSN - 2314-6133
DOI - 10.1155/2014/529519
Subject(s) - electronic nose , food spoilage , contamination , computer science , environmental science , chemical sensor , gas chromatography , principal component analysis , process engineering , materials science , biochemical engineering , nanotechnology , food science , chemistry , chromatography , artificial intelligence , bacteria , engineering , biology , ecology , genetics , electrode
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.
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