# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 4 Type of Food Safety Experiments
### Intervention Studies
[81] evaluated different practices performed in six different processing plants to assess before and after interventions by establishing a baseline to assess which quantity of peroxyacetic acid, an antimicrobial that is widely used, was the most effective in reducing _Campylobacter_ and _Salmonella_ on carcass samples. [PERSON] et al. [82] evaluated the antimicrobial efficacy of a sequential spray application of \(\varepsilon\)-polylysine (EPL) + acidic calcium sulfate (ACS) or lauramide arginine ethyl ester (LAE) + ACS on chicken carcasses inoculated with _Salmonella_, showing that both treatments effectively reduced _Salmonella_ when comparing before and after treatment. Data Visualization and Data Analysis  
The objective for this type of experiment is to show how an intervention or a treatment applied to a certain product or surface impacts its microbial loads. Typically, enumeration or detection of microorganisms is performed before and after the application of the intervention; therefore, the outcome should be any measurement that demonstrates that the counts or presence of a certain microorganism before and after treatment are similar or different with a certain level of confidence. Similar methods as described in Section 4.1.1 can be used to analyze this type of study. 1. Means comparison: As described in Section 4.1.1, the same principle of means comparison is used to evaluate differences before and after the application of an intervention. If the study was designed with random samples taken before and after intervention, a Student's \(t\)-test or non-parametric alternative Wilcoxon's test are the best alternatives for statistical analysis (Figure 3B). However, if results are desired to be presented as reductions (difference between before and after intervention), it is important to design the study properly, with paired samples, in order to use a Student's \(t\)-test for paired samples or a non-parametric alternative Wilcoxon's test for paired samples, which will give more robust results for analysis. Moreover, if two interventions are compared before and after, an ANOVA test followed by a pairwise comparison \(t\)-test or non-parametric alternative Kruskal-Wallis test followed by a pairwise Wilcoxon's test can be used for these purposes (Figure 3A). Figure 3A presents a boxplot with an ANOVA followed by a pairwise comparison-adjusted Tukey's test for two antimicrobials tested before and after its use. Boxes with different letters are significantly different at \(p<0.05\). This analysis shows differences before and after each intervention. In addition, it compares the interventions at each sampling point (before and after). In this case, the initial loads before intervention for both antimicrobials were similar, and both antimicrobials had a significant reduction after application. Moreover, no difference in microbial loads after interventions for either antimicrobial was observed. On the other hand, Figure 3B presents a bar chart plot with error bars (\(\pm 3\)SE) of the two antimicrobials tested before and after their use. This analysis only allows for differences before and after the use of each specific antimicrobial. For instance, there were significant differences before and after the use of the intervention for each specific antimicrobial. Furthermore, if it is desired that reduction be presented, Figure 1D presents the graphical representation for this type of analysis. 2. Proportion comparison: When presence or non-detection should be evaluated before and after the use of a certain intervention, the methods and graphical representation described in Section 4.1.1 can be applied on this case. Briefly, [PERSON]'s chi-square test or a test for the difference between two proportions can be implemented. In both cases, a significant \(p\)-value serves as evidence to conclude that both methods are different at detecting positive (or negative) results.