## 1 Introduction  
The microbial growth and metabolic activity of participating microbial populations depend on the actual environmental conditions represented by a group of intrinsic and extrinsic factors, as well as implicit factors that cover the interaction between the present microbiota. These are generally considered the key factors contributing to the quality and safety of raw food materials and final food products. In the case of raw milk cheeses, such as traditional dairy products in some European regions, including Slovakian ones, the presence and activity of undesirable microbial populations in raw milk are the factors limiting cheese safety. Their interactions with lactic acid bacteria (LAB) are the core of scientists' interest in preventing undesirable growth during the fermentation of milk and cheese ripening. A commercially available LAB culture, Fresco DVS 1010, significantly inhibits _S. aureus_ and _E. coli_ growth during their cocultination in milk [1, 2, 3], lumpy cheeses [4], and in pasta filata types of cheese production [5, 6]. In addition, the effect of temperature and NaCl addition on the growth of Fresco culture in milk was described by [PERSON] et al. [7]. The concern about the presence of _S. aureus_ and _E. coli_ in foods is related to their frequent occurrence in raw milk and raw milk dairy products. The prevalence of _E. coli_ and _S. aureus_ in Slovak raw milk cheeses is in the range of 45-87% and 10-13%, respectively [8]. Both bacterial species are considered important process hygiene indicators as well as indicators of cheese quality defects; they are also pathogenic organisms with the ability to endanger the health of consumers. _S. aureus_ has a remarkably wide range of pathogenic factors causing different infectious diseases; it can produce 26 different staphylococcal enterotoxins and enterotoxin-like molecules causing food-borne outbreaks. _E. coli_ may be present in raw milk and other products not only as a saprophytic organism: its pathogenic strains may occur with low prevalence. Thus, in addition to food-borne diseases, enteric, entero-hemorrhagic, diarrheal, and other diseases may result from primary or secondary _E. coli_ contamination in food [9]. The application of mathematical models in predictive microbiology is useful for describing and predicting microbial growth, interactions between microbes, and their responses to environmental conditions. Traditionally, a two-step procedure is used for modeling microbial growth, inactivation, or survival. According to [PERSON] and [PERSON] [10], after collecting a sufficient number of usually isothermal curves representing kinetic changes over time under different environmental conditions, the pertinent kinetic parameters are calculated using a suitable primary model. Next, considering the effect of environmental conditions, the kinetic parameters are modeled. Finally, the secondary modeling outputs are incorporated back into the primary models to predict of microbial behavior over time. Most of the current freely available prediction tools, also considered tertiary models, were constructed using this approach [11]. However, according to [PERSON] [12], one major drawback of this standard two-step approach is the accumulation and propagation of errors in each step of data analysis, which may affect the accuracy of prediction. The traditionally used two-step modelling approach may be significantly improved by applying one-step data analysis to directly construct the models integrating primary and secondary models. This minimizes the global error. It is also believed that one-step data analysis method is superior, providing better fits to the data and producing directly interpretable regression diagnostics and standard errors [11; 13]. Although advanced knowledge and skills in statistics and computer programming are required to perform data analysis [12], an increasing number of one-step approaches applied in several growth and inactivation studies [14; 15; 16; 17; 18] have recently been published.