journal article·2021-09-17Open access

One- and Two-Step Kinetic Data Analysis Applied for Single and Co-Culture Growth of Staphylococcus aureus, Escherichia coli, and Lactic Acid Bacteria in Milk

Pavel Ačai, Ľubomír Valík, Alžbeta Medveďová

Applied Sciences· Volume 11· Issue 18

Abstract

The objective of this study was to compare one- and two-step kinetic data analysis approaches to describe the growth of Staphylococcus aureus, Escherichia coli, and lactic acid bacteria Fresco 1010 starter culture in milk under isothermal conditions between 10 and 37 °C. The primary Huang model (HM) and secondary square root model were applied to lag times and growth rates of each of the population. The one-step approach for single cultures data enabled the direct construction of a tertiary model combining primary and secondary models to determine parameters from all growth data, thus minimizing the transfer of errors from one model to another. The statistical indices showed a significant improvement in the prediction capability provided by this approach. Then, a one-step approach combining the primary Huang, Giménez, and Dalgaard model (H-GD) with the secondary square root model was used to simultaneously model the growth of the populations mentioned above in co-culture under the same conditions. Independent isothermal data sets were chosen for validation of the growth description of single cultures (HM) and co-culture (H-GD) using validation factors, including the bias (Bf) and accuracy (Af). For example, the values of Af for the one-step approach range from 1.17 to 1.20 and 1.04 to 1.08 for single cultures and co-culture, respectively, demonstrating high accuracy. Thus, this approach may be used for co-culture growth description in general or specifically, e.g., in various types of lactic acid fermentation, including artisanal cheese-making technology.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Listeria monocytogenes in Food Safety · Microbial Inactivation Methods · Meat and Animal Product Quality

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Chemical and Environmental Engineering, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, SK-812 37 Bratislava, Slovakia

Department of Nutrition and Food Quality Assessment, Institute of Food Science and Nutrition, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-812 37 Bratislava, Slovakia

Funding

Slovak Research and Development Agency · Scientific Grant Agency of the Ministry of Education, Science, Research and Sports of the Slovak Republic and Slovak Academy of Sciences

References (33)

Show source-provided references
1. Modelling the effect of the starter culture on the growth of Staphylococcus aureus in milk10.1016/j.ijfoodmicro.2008.12.015 ↗
2. Application of competitive models in predicting the simultaneous growth of Staphylococcus aureus and lactic acid bacteria in milk10.1016/j.foodcont.2017.12.018 ↗
3. Growth prediction of two bacterial populations in co-culture with lactic acid bacteria10.1177/1082013219860360 ↗
4. Study of the Fresco culture inhibitory effect against Staphylococcus aureus in milk and in lump cheeses
5. Effect of Lactic Acid Bacteria Addition on the Microbiological Safety of Pasta-Filata Types of Cheeses10.3389/fmicb.2020.612528 ↗
6. Lehotová, V., Antálková, V., Medveďová, A., and Valík, Ľ. (2021). Quantitative Microbiological Analysis of Artisanal Stretched Cheese Manufacture. Appl. Sci., 11.10.20944/preprints202102.0312.v1 ↗
7. Medveďová, A., Šipošová, P., Mančušková, T., and Valík, Ľ. (2019). The effect of salt and temperature on the growth of Fresco culture. Fermentation, 5.10.3390/fermentation5010002 ↗
8. (2021, July 30). Summary Reports of Zoonoses, Food-Borne and Water-Borne Diseases in the Slovak Republic (2016–2019), Available online: https://www.mpsr.sk/?navID=47&sID=111&navID2=506.
9. Escherichia coli
10. A classification of models for predictive microbiology
11. Comparison of 1-step and 2-step methods of fitting microbiological models10.1016/j.ijfoodmicro.2012.09.017 ↗
12. IPMP Global Fit—A one-step direct data analysis tool for predictive10.1016/j.ijfoodmicro.2017.09.010 ↗
13. Parameter estimation in food science10.1146/annurev-food-022811-101247 ↗
14. Two complementary approaches to quantify variability in heat resistance of spores of Bacillus subtilis10.1016/j.ijfoodmicro.2017.04.014 ↗
15. Growth of Clostridium perfringens in roasted chicken and braised beef during cooling—One-step dynamic analysis and modeling10.1016/j.foodcont.2019.106739 ↗
16. One-step kinetic analysis of competitive growth of Salmonella spp. and background flora in ground chicken10.1016/j.foodcont.2020.107103 ↗
17. Development and validation of one-step modelling approach for prediction of mushroom spoilage
18. Development and validation of growth models using one-step modelling approach for determination of chicken meat shelf-life under isothermal and non-isothermal storage conditions
19. Modelling and predicting the simultaneous growth of Escherichia coli and lactic acid bacteria in milk10.1177/1082013215622840 ↗
20. ISO 6888-1 (2001). Microbiology of Food and Animal Feeding Stuffs. Horizontal Method for the Enumeration of Coagulase-Positive Staphylococci (Staphylococcus aureus and Other Species). Part 1: Technique Using Baird-Parker Agar Medium, International Organization of Standardization (ISO).
21. National Standard Method F23 (2005). Enumeration of Enterobacteriaceae by the Colony Count Technique, Evaluations and Standards Laboratory, Public Health Authority.
22. ISO 15214 (2002). Microbiology of Food and Animal Feeding Stuffs. Horizontal Method for the Enumeration of Mesophilic Lactic Acid Bacteria. Colony-Count Technique at 30 °C, International Organization of Standardization (ISO).
23. Havlicek, L.L., and Crain, R.D. (1988). Practical Statistics for the Physical Sciences, American Chemical Society. ACS Professional Reference Book.
24. Optimization of a new mathematical model for bacterial growth10.1016/j.foodcont.2012.11.019 ↗
25. Modelling and predicting the simultaneous growth of Listeria monocytogenes and spoilage micro-organisms in cold-smoked salmon10.1046/j.1365-2672.2003.02137.x ↗
26. Relationship between temperature and growth rate of bacterial cultures10.1128/jb.149.1.1-5.1982 ↗
27. Mathematical modeling and validation of growth of Salmonella Enteritidis and background microorganisms in potato salad—One-step kinetic analysis and model development10.1016/j.foodcont.2016.03.039 ↗
28. Validating and comparing predictive models10.1016/S0168-1605(99)00035-5 ↗
29. Growth characterization of Staphylococcus aureus in milk: A quantitative approach
30. Quantitative analysis of growth of Escherichia coli in co-culture with Lactococcus lactis subsp. lactis in milk
31. General regression neural network and Monte Carlo simulation model for survival and growth of Salmonella on raw chicken skin as a function of serotype, temperature and time for use in risk assessment10.4315/0362-028X-72.10.2078 ↗
32. Dynamic kinetic analysis of growth of Listeria monocytogenes in pasteurized cow milk10.3168/jds.2020-19442 ↗
33. Pérez-Rodríguez, F., and Valero, A. (2013). Predictive Microbiology in Foods, Springer. [1st ed.].10.1007/978-1-4614-5520-2 ↗