Literature DB >> 33633698

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

Dimitra Dourou1, Athena Grounta1, Anthoula A Argyri1, George Froutis2, Panagiotis Tsakanikas2, George-John E Nychas2, Agapi I Doulgeraki1, Nikos G Chorianopoulos1, Chrysoula C Tassou1.   

Abstract

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. 103 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.
Copyright © 2021 Dourou, Grounta, Argyri, Froutis, Tsakanikas, Nychas, Doulgeraki, Chorianopoulos and Tassou.

Entities:  

Keywords:  Fourier-transform infrared spectroscopy; Salmonella; chicken liver; machine learning; poultry; spoilage; support vector regression

Year:  2021        PMID: 33633698      PMCID: PMC7901899          DOI: 10.3389/fmicb.2020.623788

Source DB:  PubMed          Journal:  Front Microbiol        ISSN: 1664-302X            Impact factor:   5.640


  64 in total

1.  Meat spoilage during distribution.

Authors:  George-John E Nychas; Panos N Skandamis; Chrysoula C Tassou; Konstantinos P Koutsoumanis
Journal:  Meat Sci       Date:  2007-07-05       Impact factor: 5.209

2.  Predicting bacterial growth in raw, salted, and cooked chicken breast fillets during storage.

Authors:  Liane Aldrighi Galarz; Gustavo Graciano Fonseca; Carlos Prentice
Journal:  Food Sci Technol Int       Date:  2015-12-18       Impact factor: 2.023

3.  Revealing covariance structures in fourier transform infrared and Raman microspectroscopy spectra: a study on pork muscle fiber tissue subjected to different processing parameters.

Authors:  Ulrike Böcker; Ragni Ofstad; Zhiyun Wu; Hanne Christine Bertram; Ganesh D Sockalingum; Michel Manfait; Bjørg Egelandsdal; Achim Kohler
Journal:  Appl Spectrosc       Date:  2007-10       Impact factor: 2.388

4.  A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints.

Authors:  Efstathios Z Panagou; Fady R Mohareb; Anthoula A Argyri; Conrad M Bessant; George-John E Nychas
Journal:  Food Microbiol       Date:  2010-06-09       Impact factor: 5.516

5.  Microbial and Organoleptic Qualities of Lamb Liver During Storage at 0 or 3°C.

Authors:  Teresa Rivas; Antonio Herrera; Javier Yangüela
Journal:  J Food Prot       Date:  1992-11       Impact factor: 2.077

6.  An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

Authors:  Lucia Estelles-Lopez; Athina Ropodi; Dimitris Pavlidis; Jenny Fotopoulou; Christina Gkousari; Audrey Peyrodie; Efstathios Panagou; George-John Nychas; Fady Mohareb
Journal:  Food Res Int       Date:  2017-05-20       Impact factor: 6.475

7.  Evidence for a role of biosurfactants produced by Pseudomonas fluorescens in the spoilage of fresh aerobically stored chicken meat.

Authors:  Glen E Mellor; Jessica A Bentley; Gary A Dykes
Journal:  Food Microbiol       Date:  2011-02-13       Impact factor: 5.516

8.  Development and validation of a predictive microbiology model for survival and growth of Salmonella on chicken stored at 4 to 12 °C.

Authors:  Thomas P Oscar
Journal:  J Food Prot       Date:  2011-02       Impact factor: 2.077

9.  Prevalence, Levels, and Viability of Salmonella in and on Raw Chicken Livers.

Authors:  Yangjin Jung; Anna C S Porto-Fett; Bradley A Shoyer; Elizabeth Henry; Laura E Shane; Manuela Osoria; John B Luchansky
Journal:  J Food Prot       Date:  2019-05       Impact factor: 2.077

Review 10.  Growth and inactivation of Salmonella at low refrigerated storage temperatures and thermal inactivation on raw chicken meat and laboratory media: mixed effect meta-analysis.

Authors:  Hanan Smadi; Jan M Sargeant; Harry S Shannon; Parminder Raina
Journal:  J Epidemiol Glob Health       Date:  2012-12-27
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  3 in total

Review 1.  Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective.

Authors:  George Pampoukis; Anastasia E Lytou; Anthoula A Argyri; Efstathios Z Panagou; George-John E Nychas
Journal:  Sensors (Basel)       Date:  2022-04-06       Impact factor: 3.576

2.  Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers.

Authors:  Lemonia-Christina Fengou; Yunge Liu; Danai Roumani; Panagiotis Tsakanikas; George-John E Nychas
Journal:  Foods       Date:  2022-08-09

3.  The Clash of Microbiomes: From the Food Matrix to the Host Gut.

Authors:  Despoina Eugenia Kiousi; Nikos Chorianopoulos; Chrysoula C Tassou; Alex Galanis
Journal:  Microorganisms       Date:  2022-01-06
  3 in total

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