Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning.
Autor: | Dourou D; Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece., Grounta A; Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece., Argyri AA; Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece., Froutis G; Laboratory of Food Microbiology and Biotechnology, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Athens, Greece., Tsakanikas P; Laboratory of Food Microbiology and Biotechnology, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Athens, Greece., Nychas GE; Laboratory of Food Microbiology and Biotechnology, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Athens, Greece., Doulgeraki AI; Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece., Chorianopoulos NG; Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece., Tassou CC; Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Athens, Greece. |
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Jazyk: | angličtina |
Zdroj: | Frontiers in microbiology [Front Microbiol] 2021 Feb 04; Vol. 11, pp. 623788. Date of Electronic Publication: 2021 Feb 04 (Print Publication: 2020). |
DOI: | 10.3389/fmicb.2020.623788 |
Abstrakt: | 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 Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Dourou, Grounta, Argyri, Froutis, Tsakanikas, Nychas, Doulgeraki, Chorianopoulos and Tassou.) |
Databáze: | MEDLINE |
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