An Array of MOX Sensors and ANNs to Assess Grated Parmigiano Reggiano Cheese Packs' Compliance with CFPR Guidelines.

MOX sensors Parmigiano Reggiano artificial neural network electronic nose food quality control nanowire gas sensors rapid detection

Journal

Biosensors
ISSN: 2079-6374
Titre abrégé: Biosensors (Basel)
Pays: Switzerland
ID NLM: 101609191

Informations de publication

Date de publication:
02 May 2020
Historique:
received: 15 04 2020
revised: 28 04 2020
accepted: 29 04 2020
entrez: 7 5 2020
pubmed: 7 5 2020
medline: 9 2 2021
Statut: epublish

Résumé

Parmigiano Reggiano cheese is one of the most appreciated Italian foods on account of its high nutrient content and taste. Due to its high cost, these characteristics make this product subject to counterfeiting in different forms. In this study, an approach based on an array of gas sensors has been employed to assess if it was possible to distinguish different samples based on their aroma. Samples were characterized in terms of rind percentage, seasoning, and rind working process. From the responses of the sensors, five features were extracted and the capability of these parameters to recognize target classes was tested with statistical analysis. Hence, the performance of the sensors' array was quantified using artificial neural networks. To simplify the problem, a hierarchical approach has been used: three steps of classification were performed, and in each step one parameter of the grated cheese was identified (firstly, seasoning; secondly, rind working process; finally, rind percentage). The accuracies ranged from 88.24% to 100%.

Identifiants

pubmed: 32370241
pii: bios10050047
doi: 10.3390/bios10050047
pmc: PMC7277510
pii:
doi:

Substances chimiques

Tin Compounds 0
Volatile Organic Compounds 0
Copper 789U1901C5
stannic oxide KM7N50LOS6
cupric oxide V1XJQ704R4

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Déclaration de conflit d'intérêts

The authors declare no conflict of interest.

Références

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Auteurs

Marco Abbatangelo (M)

Department of Information Engineering, University of Brescia, Brescia, via Branze, 38, 25123 Brescia, BS, Italy.

Estefanía Núñez-Carmona (E)

CNR-IBBR, Institute of Bioscience and Bioresources, via Madonna del Piano, 10, 50019 Sesto Fiorentino, FI, Italy.

Veronica Sberveglieri (V)

CNR-IBBR, Institute of Bioscience and Bioresources, via Madonna del Piano, 10, 50019 Sesto Fiorentino, FI, Italy.
NANO SENSOR SYSTEMS, NASYS Spin-Off University of Brescia, Brescia, via Camillo Brozzoni, 9, 25125 Brescia, BS, Italy.

Dario Zappa (D)

Department of Information Engineering, University of Brescia, Brescia, via Branze, 38, 25123 Brescia, BS, Italy.

Elisabetta Comini (E)

Department of Information Engineering, University of Brescia, Brescia, via Branze, 38, 25123 Brescia, BS, Italy.
NANO SENSOR SYSTEMS, NASYS Spin-Off University of Brescia, Brescia, via Camillo Brozzoni, 9, 25125 Brescia, BS, Italy.

Giorgio Sberveglieri (G)

Department of Information Engineering, University of Brescia, Brescia, via Branze, 38, 25123 Brescia, BS, Italy.
NANO SENSOR SYSTEMS, NASYS Spin-Off University of Brescia, Brescia, via Camillo Brozzoni, 9, 25125 Brescia, BS, Italy.

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Classifications MeSH