# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 4 Type of Food Safety Experiments
### Shelf Life Studies
In each boxplot, the horizontal line crossing the box represents the median, the bottom and top box are the lower and upper quartiles, the vertical top line represents 1.5 times the interquartile range, and the vertical bottom line represents 1.5 times the lower interquartile range. Boxes with different letters or symbols are significantly different according to \(t\)-test at \(p\) < 0.05. The points represent the actual data points. (**B**) Microbial counts (Log CFU/mL) of samples with (Treatment A) and without (control) an intervention retail displayed for 96 h at refrigerated conditions (\(n\) = 50 samples per treatment/retail display time). Solid symbols represent mean counts per treatment and retail display time. Different letters are significantly different according to a 2-Way ANOVA followed by pairwise comparison test adjusted Tukey at \(p\) < 0.05.  
however, at 48 h and 96 h, differences between the groups were found to be significant. On the other hand, Figure 7B shows a line plot of the same information presented in Figure 7A. Nevertheless, a two-way ANOVA was performed with significant interactions, followed by pairwise comparison between the treatment \(\times\) time groups. Different letters are significantly different at \(p\) < 0.05. In this case, unlike Figure 7A, the analysis allows us to compare means between control and treated groups at 0 h, 48 h, and 96 h. Choosing between this type of analysis is dependent on the type of outcome you want to achieve. 2. Multivariate analysis: more complex analysis can be performed in shelf life studies, especially when multiple dependent variables should be analyzed, which is the case while conducting shelf life studies. As mentioned before, many are the factors to be measured in order to establish the shelf life of a product, which is why multivariate analysis such as principal component analysis (PCA) or linear discriminant analysis (LDA) are great tools to simplify the complexity of high-dimensional data while enabling us to observe trends and patterns. When multiple factors are evaluated, an increased error exists due to multiple test correction when evaluating each of the factors and their association with one or several outcomes. The main objective of PCA is to reduce data by projecting them onto dimensions called principal components (PCs) that will summarize the data by maximizing the correlation between data and their projection onto the PCs while trying to maximize the explained variance of the model (Figure 8A). Scale matters with PCA, as a set of variables with larger magnitude than others will overweight the PCA, resulting in variables ignored and results highly affected by these high-magnitude variables, reason why standardization is a key step before conducting PCA. Figure 8A shows a PCA of microbial counts of three different microorganisms for samples with and without an intervention (Control and Treatment A) and displayed for 96 h. Microbial enumeration was performed at 0 h, 48 h, and 96 h of display under refrigated conditions. For the raw-data PCA, PC1 explained 96.76% and PC2 explained 2.47% of the variation associated with microbial counts of the treatment \(\times\) time combinations. PC1 clearly separated all group combinations by time, while PC2 separated all group combinations by treatment. Bacteria C are more associated with the control group, suggesting that more growth of bacteria C was promoted when no treatment was applied. Moreover, bacteria A and bacteria B are more associated with the treatment group, suggesting that more growth of bacteria A and B was promoted when the treatment was applied. Of course, this is a simple example with only three dependent variables, but several variables can be analyzed, and association can be estimated between groups combinations and the dependent variables [96; 103]. 3. Multiple linear regression: when conducting shelf life studies, typically a threshold value is established as a measurement to assess if certain attribute will fail.