## 3 Results and Discussion
### Lag Death Rate Secondary Modelling  
Most of the phenomena associated with microbial adaptation mechanisms to new environmental conditions--involving the synthesis of cellular components, activation of genetic and metabolic processes, and accumulation of intracellular structures needed for cell division--are already known and well described. However, the lag phase is still considered poorly understood in the microbial growth [19]. At present, more than 120 years after the first mention in the literature of the lag phase, this may sound questionable; the lag kinetics have been described for individual cells [40], in different population levels, and even in the case when the microbial population declined [17]. However, the limited understanding of the lag phase is still true. To illustrate this fact, we did not find any mention of modelling the effect of environmental factors on the lag decline. In terms of suitable data for this research opportunity, we have only identified the addition of 1% NaCl in the case of _G. candidum_ growth in milk. In this study, the lag phase data obtained from the growth curves observed in suboptimal temperatures growth range were fitted using [PERSON]'s model. Then, the _LDR_ of _G. candidum_ calculated from first decimal reductions \(\delta\) (Table 1) was modelled using the secondary Arrhenius equations (Equations (3) and (4)) as well as with the square root model (Equation (5)). The calculated model parameters are summarized in Table 2. Both of the modified Arrhenius equations clearly demonstrated that the activation energy of 100.88 kJ mol\({}^{-1}\) was needed for _G. candidum_ arthospores to germinate. In our case, they formed a substantial part of the inoculums (microscopic observation not presented in this study). The modified Arrhenius equations also provided the identical graphical outputs presented in Figure 2a, with a high coefficient of determination (\(R^{2}\) = 0.927). However, the application of the square root model presented in Figure 2b showed better indices relating to the goodness of fit involving _RMSE_ (_RMSE_ = 0.028 log CFU mL\({}^{-1}\)), a higher coefficient of determination (\(R^{2}\) = 0.978), and the lowest value of _AIC_ value (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(T\) (\({}^{\circ}\)C) & \(\delta\) (h) & \(p\) & \(\log N_{0}\) (log CFU mL\({}^{-1}\)) & \(n\) & _RMSE_ (log CFU mL\({}^{-1}\)) & \(R^{2}\) \\ \hline
6 & 111.94 & 1.53 & 2.01 & 46 & 0.200 & 0.491 \\
8 & 52.35 & 1.90 & 2.13 & 30 & 0.349 & 0.608 \\
12 & 20.96 & 1.85 & 1.78 & 8 & 0.164 & 0.959 \\
15 & 17.96 & 3.57 & 1.93 & 6 & 0.086 & 0.989 \\
18 & 9.91 & 5.91 & 1.97 & 8 & 0.052 & 0.972 \\
21 & 7.71 & 2.03 & 1.93 & 8 & 0.087 & 0.984 \\
25 & 6.14 & 1.27 & 1.97 & 7 & 0.377 & 0.788 \\ \hline \end{tabular} \(T\)—incubation temperature, \(\delta\)—first decimal reduction in _G. candidum_ cell counts, \(p\)—shape parameter of [PERSON]’s model, log \(N_{0}\)—initial cell counts of _G. candidum_, \(n\)—number of observations, _RMSE_—root mean sum of square error, \(R^{2}\)—coefficient of determination.  
\end{table}
Table 1: Parameters of the primary model and statistical indicators for the rate of decrease in _G. candidum_ cell viable counts in dependence on the incubation temperature.