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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-02-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2020.623788/full |
| _version_ | 1818576353719484416 |
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| author | Dimitra Dourou Athena Grounta Anthoula A. Argyri George Froutis Panagiotis Tsakanikas George-John E. Nychas Agapi I. Doulgeraki Nikos G. Chorianopoulos Chrysoula C. Tassou |
| author_facet | Dimitra Dourou Athena Grounta Anthoula A. Argyri George Froutis Panagiotis Tsakanikas George-John E. Nychas Agapi I. Doulgeraki Nikos G. Chorianopoulos Chrysoula C. Tassou |
| author_sort | Dimitra Dourou |
| collection | DOAJ |
| 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. 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 (R2), and the bias (Bf) and accuracy (Af) 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 Af and Bf between observed and predicted counts within the range of 1.071 to 1.145 and 0.995 to 1.029, respectively, while the R2 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. |
| first_indexed | 2024-12-16T06:12:40Z |
| format | Article |
| id | doaj.art-9f18b25bb98d4aa2a5626896affb337f |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-12-16T06:12:40Z |
| publishDate | 2021-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-9f18b25bb98d4aa2a5626896affb337f2022-12-21T22:41:21ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2021-02-011110.3389/fmicb.2020.623788623788Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine LearningDimitra Dourou0Athena Grounta1Anthoula A. Argyri2George Froutis3Panagiotis Tsakanikas4George-John E. Nychas5Agapi I. Doulgeraki6Nikos G. Chorianopoulos7Chrysoula C. Tassou8Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, GreeceInstitute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, GreeceInstitute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, GreeceLaboratory of Food Microbiology and Biotechnology, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Athens, GreeceLaboratory of Food Microbiology and Biotechnology, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Athens, GreeceLaboratory of Food Microbiology and Biotechnology, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Athens, GreeceInstitute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, GreeceInstitute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, GreeceInstitute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, GreeceChicken 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 (R2), and the bias (Bf) and accuracy (Af) 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 Af and Bf between observed and predicted counts within the range of 1.071 to 1.145 and 0.995 to 1.029, respectively, while the R2 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.https://www.frontiersin.org/articles/10.3389/fmicb.2020.623788/fullchicken liverpoultryspoilageSalmonellaFourier-transform infrared spectroscopymachine learning |
| spellingShingle | Dimitra Dourou Athena Grounta Anthoula A. Argyri George Froutis Panagiotis Tsakanikas George-John E. Nychas Agapi I. Doulgeraki Nikos G. Chorianopoulos Chrysoula C. Tassou Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning Frontiers in Microbiology chicken liver poultry spoilage Salmonella Fourier-transform infrared spectroscopy 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 | chicken liver poultry spoilage Salmonella Fourier-transform infrared spectroscopy machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2020.623788/full |
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