Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous Abstract"Bioecology of pistachio twig borer moth Kermania pistaciella Amsel, in Rafsanjan and Isfahan-Iran"    Next AbstractExperimentally induced gestational androgen excess disrupts glucoregulation in rhesus monkey dams and their female offspring »

Biosensors (Basel)


Title:An Array of MOX Sensors and ANNs to Assess Grated Parmigiano Reggiano Cheese Packs' Compliance with CFPR Guidelines
Author(s):Abbatangelo M; Nunez-Carmona E; Sberveglieri V; Zappa D; Comini E; Sberveglieri G;
Address:"Department of Information Engineering, University of Brescia, Brescia, via Branze, 38, 25123 Brescia, BS, Italy. 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"
Journal Title:Biosensors (Basel)
Year:2020
Volume:20200502
Issue:5
Page Number: -
DOI: 10.3390/bios10050047
ISSN/ISBN:2079-6374 (Electronic) 2079-6374 (Linking)
Abstract:"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%"
Keywords:"Cheese/*analysis Copper/*chemistry *Electronic Nose *Neural Networks, Computer Semiconductors Tin Compounds/*chemistry Volatile Organic Compounds/*analysis MOX sensors Parmigiano Reggiano artificial neural network electronic nose food quality control nano;"
Notes:"MedlineAbbatangelo, Marco Nunez-Carmona, Estefania Sberveglieri, Veronica Zappa, Dario Comini, Elisabetta Sberveglieri, Giorgio eng Switzerland 2020/05/07 Biosensors (Basel). 2020 May 2; 10(5):47. doi: 10.3390/bios10050047"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 26-12-2024