## 3 Results
### Building of Mathematical Model  
Regarding the building of the mathematical models, Table 1 shows the association between the log bacterial counts (output/response) and the predictors, including average daily temperature, number of days, and pH. In developing the regression models, the last datum/measurement of the whole data (<100 cfu/g, detection limit) was avoided in order to ensure data integrity. These models revealed the type of relationships that existed and are described as multiple linear regression models. These multiple regression models were employed to determine the strength of the relationship between the response and predictor variables as well as to forecast changes in the response variable, should a specific predictor variable experienced change while the others were kept constant. In addition, the ANOVA plot of the group means of log bacterial counts of the different bacteria was developed (Figure 3) and the plot indicated that there was a significant difference in the group mean log bacterial counts amongst the bacteria, but it was not clear between which bacteria. For each of the groups, the major blue plot represents the ANOVA plots for each bacterium, the horizontal red line in each ANOVA plot represents the median for the group, the lower and upper black horizontal lines in each group represent the lower and upper confidence level in the group, respectively, and the red cross marker(s) in each group represent the outliers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bacteria Species** & **Log of Counts** & **A0** & **A1(0)** & **A2(PH)** & **A3 (T)** & **R\({}^{2}\)** & **p-Values** \\ \hline _Escherichia coli Salmonella_ & Y & 3.822 & \(-\)0.057 & 0.264 & 0.040 & 0.928 & 0.956 \\ _Salmonella_ sp. & Y & 5.822 & \(-\)0.044 & \(-\)0.280 & 0.043 & 0.919 & 0.967 \\ _Y. enterocolitica_ & Y & \(-\)16.022 & \(-\)0.016 & 3.261 & 0.011 & 0.978 & 0.970 \\ _Campylobacter_ sp. & Y & \(-\)3.833 & \(-\)0.030 & 1.300 & 0.072 & 0.949 & 0.954 \\ _L. monocytogenes_ & Y & \(-\)3.920 & 0.002 & 1.251 & \(-\)0.017 & 0.984 & 0.979 \\ \hline \hline \end{tabular}  
Y, modelled log bacterial counts (response): A0, forcing constant; A1, scaling constant of the variable, number of days; A2, scaling constant of the variable, PH; A3, scaling constant of the variable, average daily temperature.  
\end{table} 
Table 1: Multiple linear regression models of log bacterial counts with number of days, pH, and average daily temperature as predictors. Figure 3: ANOVA plot of the group means of log bacterial counts of the different species. To identify which bacterial species were involved, we then employed a multiple comparison test to determine between which bacterial species there existed a difference in significance in the group means of log bacterial counts. The results are as shown in Figure 4. In Figure 4, each of the horizontal lines represents the full range of the data of log bacterial counts for each group. The circle marker at the centre of each horizontal line represents the mean of the group. The colours are produced by default with the coloured horizontal line (_E. coli_ group) representing the reference used in comparing to the rest of the groups. The two broken vertical lines (cyan colour) represent the interval estimates for the reference group (_E. coli_). If none of the other horizontal lines (different groups) overlap with either or both broken vertical lines, that implies that no mean significant difference existed between the reference group and those groups. Therefore, as the group with the red-coloured horizontal line (_Yersinia_) did not overlap with the interval estimates, there existed a mean significant difference between the _E. coli_ group and the _Yersinia_ group.