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Detection of Meat Adulteration Using Spectroscopy-Based Sensors
Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the p...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8071343/ https://www.ncbi.nlm.nih.gov/pubmed/33920872 http://dx.doi.org/10.3390/foods10040861 |
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author | Fengou, Lemonia-Christina Lianou, Alexandra Tsakanikas, Panagiοtis Mohareb, Fady Nychas, George-John E. |
author_facet | Fengou, Lemonia-Christina Lianou, Alexandra Tsakanikas, Panagiοtis Mohareb, Fady Nychas, George-John E. |
author_sort | Fengou, Lemonia-Christina |
collection | PubMed |
description | Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific. |
format | Online Article Text |
id | pubmed-8071343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80713432021-04-26 Detection of Meat Adulteration Using Spectroscopy-Based Sensors Fengou, Lemonia-Christina Lianou, Alexandra Tsakanikas, Panagiοtis Mohareb, Fady Nychas, George-John E. Foods Article Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific. MDPI 2021-04-15 /pmc/articles/PMC8071343/ /pubmed/33920872 http://dx.doi.org/10.3390/foods10040861 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fengou, Lemonia-Christina Lianou, Alexandra Tsakanikas, Panagiοtis Mohareb, Fady Nychas, George-John E. Detection of Meat Adulteration Using Spectroscopy-Based Sensors |
title | Detection of Meat Adulteration Using Spectroscopy-Based Sensors |
title_full | Detection of Meat Adulteration Using Spectroscopy-Based Sensors |
title_fullStr | Detection of Meat Adulteration Using Spectroscopy-Based Sensors |
title_full_unstemmed | Detection of Meat Adulteration Using Spectroscopy-Based Sensors |
title_short | Detection of Meat Adulteration Using Spectroscopy-Based Sensors |
title_sort | detection of meat adulteration using spectroscopy-based sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8071343/ https://www.ncbi.nlm.nih.gov/pubmed/33920872 http://dx.doi.org/10.3390/foods10040861 |
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