Evidence map›Paper›PMID 29783673›Full record

ArticleSensors (Basel, Switzerland)2018

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

Marco Abbatangelo, Estefanía Núñez-Carmona, Veronica Sberveglieri, Dario Zappa, Elisabetta Comini, Giorgio Sberveglieri

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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  13. The Influence of Nb on the Synthesis of WOSensors (Basel, Switzerland) · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Marco AbbatangeloDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy. m.abbatangelo@unibs.it.ORCID 0000-0002-5550-3401
Estefanía Núñez-CarmonaDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy. e.nunezcarmona@unibs.it.ORCID 0000-0002-4621-0658
Veronica SberveglieriCNR-IBBR, Institute of Biosciences and Bioresources, Via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy. veronica.sberveglieri@ibbr.cnr.it.ORCID 0000-0002-5991-9391
Dario ZappaDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy. dario.zappa@unibs.it.
Elisabetta CominiDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy. elisabetta.comini@unibs.it.
Giorgio SberveglieriDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy. giorgio.sberveglieri@unibs.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial neural networkelectronic nosefood quality controlmultivariate data analysisnanowire gas sensorsParmigiano Reggiano

Identifiers

PMID29783673
PMCPMC5981319

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.