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Sensors 2018, 18(5), 1617; https://doi.org/10.3390/s18051617

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano

1
Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy
2
CNR-IBBR, Institute of Biosciences and Bioresources, Via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy
3
NANO SENSOR SYSTEMS S.r.l., Via Branze 38, 25123 Brescia, Italy
*
Author to whom correspondence should be addressed.
Received: 11 April 2018 / Revised: 11 May 2018 / Accepted: 15 May 2018 / Published: 18 May 2018
(This article belongs to the Special Issue Artificial Olfaction and Taste)
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Abstract

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent. View Full-Text
Keywords: electronic nose; nanowire gas sensors; food quality control; Parmigiano Reggiano; multivariate data analysis; artificial neural network electronic nose; nanowire gas sensors; food quality control; Parmigiano Reggiano; multivariate data analysis; artificial neural network
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Abbatangelo, M.; Núñez-Carmona, E.; Sberveglieri, V.; Zappa, D.; Comini, E.; Sberveglieri, G. Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano. Sensors 2018, 18, 1617.

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