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
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.
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