## 3 Results and Discussion
### Shelf-Life Study
#### 3.1.1 Microbial Growth
Figure 3: Isothermal growth data and fitted [PERSON] and [PERSON] (1994) [31] model curves for lactic acid bacteria in the four different examined bacon categories, under storage conditions of different temperatures, namely at **(a)** 5, **(b)** 10, and **(c)** 15 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{3}{c}{**Maximum Specific Growth Rate (\(\mu_{\text{max}}\))**} \\ \hline Samples & E\({}_{\text{A},\mu_{\text{th}}}\) (J/mole) & \(\mu_{\text{ref}}\) (d\({}^{-1}\)), at T = 4 \({}^{\circ}\)C & SSE & R\({}^{2}\) \\ \hline CNT & 86,491.17 & 0.1426 & 0.0043 & 0.9091 \\ RN & 70,648.81 & 0.2024 & 0.0090 & 0.8276 \\ RSE & 105,976.05 & 0.1067 & 0.0033 & 0.9297 \\ RSE–RSME & 76,777.55 & 0.1673 & 0.0031 & 0.9347 \\ \hline \hline \multicolumn{5}{c}{**Lag phase (d)**} \\ \hline Samples & E\({}_{\text{A},\lambda}\) (J/mole) & \(\lambda_{\text{ref}}\) (d), at T = 4 \({}^{\circ}\)C & SSE & R\({}^{2}\) \\ \hline CNT & \(-\)116,783.84 & 12.7 & 0.0253 & 0.9985 \\ RN & \(-\)136,132.21 & 8.3 & 0.0385 & 0.9938 \\ RSE & \(-\)124,309.74 & 16.0 & 0.0076 & 0.9997 \\ RSE–RSME & \(-\)91,577.67 & 9.4 & 0.8724 & 0.9464 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters derived from Arrhenius equations (secondary model) that describe the dependence of the maximum specific growth rate and the lag phase of APC on the temperature in the different bacon samples examined. Based on Figure 3, [PERSON] and [PERSON]'s 1994 model [31] is a well-fitted primary model for assessing lactic acid bacteria microbial growth in bacon samples. Regarding the estimated SSE values, the highest SSE value was observed at 0.601 for all of the examined experimental vs. predicted data, which is an acceptable level for model predictions. For a secondary model, the Arrhenius model was used to quantify the effect of the temperature on LAB growth. As expected, at the lowest temperature, 5 \({}^{\circ}\)C, the lowest LAB microbial growth rate and the highest lag phase (\(\lambda\)) was estimated for all of the examined bacon samples. In contrast to the APC data, for the case of LAB at the highest temperatures studied (10 and 15 \({}^{\circ}\)C), no significant differences in the growth rate of LAB were observed between the different groups of bacon samples. As shown in Figure 3, at 15 \({}^{\circ}\)C, a slight difference was observed between the growth curve and growth rate of the different samples, which is in the range of experimental and statistical error. Similar to the APC results, the estimated parameters regarding the application of the Arrhenius model to describe the effect of temperature are summarized in Table 3. Once more it is confirmed that in the temperature range of refrigeration (the lowest temperature tested, 5 \({}^{\circ}\)C), the antimicrobial effect of rose extract appears to be more efficient on LAB microbial growth inhibition in bacon samples, followed by the traditional recipe containing the full amount of nitrites (CNT) and the rose-rosemary extract bacon samples (RSE-RSME), and last, with a significant difference, are the samples containing half of the nitrites (RN). Modeling the kinetics of growth of both APC and LAB, it is interesting to mention that the microbial growth rate appears to be very similar for both examined microorganisms, while at the increased (abusive) temperatures tested, the antimicrobials used appear to have no significant impact, an observation that emphasizes the importance of maintaining proper temperature conditions during storage and handling in the chill chain. Furthermore, LAB were identified as the dominant microorganisms of the examined meat products. The presence of the vacuum packaging and the low storage temperature favor the growth of psychrotrophic LAB [35]. LAB naturally constitute the predominant microflora of raw meats and other meat products that are vacuum packed and chill stored, causing their spoilage.