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Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning

Chicken liver is a highly perishable meat product with a relatively short shelf-life and that can get easily contaminated with pathogenic microorganisms. This study was conducted to evaluate the behavior of spoilage microbiota and of inoculated Salmonella enterica on chicken liver. The feasibility o...

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Autores principales: Dourou, Dimitra, Grounta, Athena, Argyri, Anthoula A., Froutis, George, Tsakanikas, Panagiotis, Nychas, George-John E., Doulgeraki, Agapi I., Chorianopoulos, Nikos G., Tassou, Chrysoula C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901899/
https://www.ncbi.nlm.nih.gov/pubmed/33633698
http://dx.doi.org/10.3389/fmicb.2020.623788
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author Dourou, Dimitra
Grounta, Athena
Argyri, Anthoula A.
Froutis, George
Tsakanikas, Panagiotis
Nychas, George-John E.
Doulgeraki, Agapi I.
Chorianopoulos, Nikos G.
Tassou, Chrysoula C.
author_facet Dourou, Dimitra
Grounta, Athena
Argyri, Anthoula A.
Froutis, George
Tsakanikas, Panagiotis
Nychas, George-John E.
Doulgeraki, Agapi I.
Chorianopoulos, Nikos G.
Tassou, Chrysoula C.
author_sort Dourou, Dimitra
collection PubMed
description Chicken liver is a highly perishable meat product with a relatively short shelf-life and that can get easily contaminated with pathogenic microorganisms. This study was conducted to evaluate the behavior of spoilage microbiota and of inoculated Salmonella enterica on chicken liver. The feasibility of Fourier-transform infrared spectroscopy (FTIR) to assess chicken liver microbiological quality through the development of a machine learning workflow was also explored. Chicken liver samples [non-inoculated and inoculated with a four-strain cocktail of ca. 10(3) colony-forming units (CFU)/g Salmonella] were stored aerobically under isothermal (0, 4, and 8°C) and dynamic temperature conditions. The samples were subjected to microbiological analysis with concomitant FTIR measurements. The developed FTIR spectral analysis workflow for the quantitative estimation of the different spoilage microbial groups consisted of robust data normalization, feature selection based on extra-trees algorithm and support vector machine (SVM) regression analysis. The performance of the developed models was evaluated in terms of the root mean square error (RMSE), the square of the correlation coefficient (R(2)), and the bias (B(f)) and accuracy (A(f)) factors. Spoilage was mainly driven by Pseudomonas spp., followed closely by Brochothrix thermosphacta, while lactic acid bacteria (LAB), Enterobacteriaceae, and yeast/molds remained at lower levels. Salmonella managed to survive at 0°C and dynamic conditions and increased by ca. 1.4 and 1.9 log CFU/g at 4 and 8°C, respectively, at the end of storage. The proposed models exhibited A(f) and B(f) between observed and predicted counts within the range of 1.071 to 1.145 and 0.995 to 1.029, respectively, while the R(2) and RMSE values ranged from 0.708 to 0.828 and 0.664 to 0.949 log CFU/g, respectively, depending on the microorganism and chicken liver samples. Overall, the results highlighted the ability of Salmonella not only to survive but also to grow at refrigeration temperatures and demonstrated the significant potential of FTIR technology in tandem with the proposed spectral analysis workflow for the estimation of total viable count, Pseudomonas spp., B. thermosphacta, LAB, Enterobacteriaceae, and Salmonella on chicken liver.
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spelling pubmed-79018992021-02-24 Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning Dourou, Dimitra Grounta, Athena Argyri, Anthoula A. Froutis, George Tsakanikas, Panagiotis Nychas, George-John E. Doulgeraki, Agapi I. Chorianopoulos, Nikos G. Tassou, Chrysoula C. Front Microbiol Microbiology Chicken liver is a highly perishable meat product with a relatively short shelf-life and that can get easily contaminated with pathogenic microorganisms. This study was conducted to evaluate the behavior of spoilage microbiota and of inoculated Salmonella enterica on chicken liver. The feasibility of Fourier-transform infrared spectroscopy (FTIR) to assess chicken liver microbiological quality through the development of a machine learning workflow was also explored. Chicken liver samples [non-inoculated and inoculated with a four-strain cocktail of ca. 10(3) colony-forming units (CFU)/g Salmonella] were stored aerobically under isothermal (0, 4, and 8°C) and dynamic temperature conditions. The samples were subjected to microbiological analysis with concomitant FTIR measurements. The developed FTIR spectral analysis workflow for the quantitative estimation of the different spoilage microbial groups consisted of robust data normalization, feature selection based on extra-trees algorithm and support vector machine (SVM) regression analysis. The performance of the developed models was evaluated in terms of the root mean square error (RMSE), the square of the correlation coefficient (R(2)), and the bias (B(f)) and accuracy (A(f)) factors. Spoilage was mainly driven by Pseudomonas spp., followed closely by Brochothrix thermosphacta, while lactic acid bacteria (LAB), Enterobacteriaceae, and yeast/molds remained at lower levels. Salmonella managed to survive at 0°C and dynamic conditions and increased by ca. 1.4 and 1.9 log CFU/g at 4 and 8°C, respectively, at the end of storage. The proposed models exhibited A(f) and B(f) between observed and predicted counts within the range of 1.071 to 1.145 and 0.995 to 1.029, respectively, while the R(2) and RMSE values ranged from 0.708 to 0.828 and 0.664 to 0.949 log CFU/g, respectively, depending on the microorganism and chicken liver samples. Overall, the results highlighted the ability of Salmonella not only to survive but also to grow at refrigeration temperatures and demonstrated the significant potential of FTIR technology in tandem with the proposed spectral analysis workflow for the estimation of total viable count, Pseudomonas spp., B. thermosphacta, LAB, Enterobacteriaceae, and Salmonella on chicken liver. Frontiers Media S.A. 2021-02-04 /pmc/articles/PMC7901899/ /pubmed/33633698 http://dx.doi.org/10.3389/fmicb.2020.623788 Text en Copyright © 2021 Dourou, Grounta, Argyri, Froutis, Tsakanikas, Nychas, Doulgeraki, Chorianopoulos and Tassou. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Dourou, Dimitra
Grounta, Athena
Argyri, Anthoula A.
Froutis, George
Tsakanikas, Panagiotis
Nychas, George-John E.
Doulgeraki, Agapi I.
Chorianopoulos, Nikos G.
Tassou, Chrysoula C.
Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning
title Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning
title_full Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning
title_fullStr Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning
title_full_unstemmed Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning
title_short Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning
title_sort rapid microbial quality assessment of chicken liver inoculated or not with salmonella using ftir spectroscopy and machine learning
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901899/
https://www.ncbi.nlm.nih.gov/pubmed/33633698
http://dx.doi.org/10.3389/fmicb.2020.623788
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