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

• Published in: Biosensors (Basel) Vol. 10; no. 5; p. 47
• Main Authors: Abbatangelo, Marco, Núñez-Carmona, Estefanía, Sberveglieri, Veronica, Zappa, Dario, Comini, Elisabetta, Sberveglieri, Giorgio
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 02.05.2020; MDPI AG
• Subjects: Cheese - analysis; Copper - chemistry; Electronic Nose; food quality control; MOX sensors; nanowire gas sensors; Neural Networks, Computer; Parmigiano Reggiano; rapid detection; Semiconductors; Tin Compounds - chemistry; Volatile Organic Compounds - analysis; nanowire gas sensors; artificial neural network; food quality control; Parmigiano Reggiano; MOX sensors; electronic nose; rapid detection
• ISSN: 2079-6374; 2079-6374
• DOI: 10.3390/bios10050047

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%.