# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 4 Type of Food Safety Experiments
### Shelf Life Studies
Data Visualization and Data Analysis  
When conducting shelf life studies, the main objective, or the main question to be answered is, \"what is the shelf life of a certain product?\", meaning what is the maximum allowable time in which a food will remain safe and will retain all its sensorial and functional characteristics when stored under specific conditions (temperature and oxygen) [96; 102]. Many are the factors to be considered when conducting shelf life studies, as the shelf life of a product can be lost not only by reaching certain microbial concentration but also by color changes, odor, overall appearance of the product, sensorial properties, etc. For the scope of this paper, only microbial measurements are presented; however, statistical analysis and visualization plots can be further used for other types of measurement conducted in shelf life studies such as color, odor, overall liking, volatile compound analysis, oxidation byproducts, pH, etc. Since the objective of this type of study is to determine the shelf life of a product, the analysis should provide enough evidence that a product will last a certain amount of time under specific conditions with a certain level of confidence. Different approaches and examples are described below:  
1. The most common statistical approach to shelf life studies consists of the evaluation of the effect of a certain treatment over a certain amount of time. Since two factors are evaluated (treatment and time), a two-way ANOVA will provide information about the effect of treatment and time by themselves (main effects) in microbial counts or the interaction between both factors (treatment \(\times\) time). If the interaction is significant, the statistical analysis suggests that there are significant differences between your groups (treatment) and over time, in other words, the change in the dependent variable (microbial counts) over time is different depending on group membership (treatment). Then, a pairwise comparison between the combination of treatment \(\times\) time needs to be performed to find differences between treatments at specific time points (Figure 7B). However, if the interaction is not significant, the main effects should be evaluated,understanding that the change in the dependent variable over time is not different depending on group membership, and the results should be presented as they are when one-way ANOVA is performed for each of the main effects. Furthermore, some experiments may have more than two factors evaluated and each factor can have several levels. When multiple factors are analyzed, mean comparisons can be very difficult to interpret, especially when interactions are found to be significant. A possible solution is to analyze if the effect of time is statistically significant (which normally in microbiological experiments throughout time it is, as bacteria grow), and if it is, exclude it from the initial model, and then analyze differences between other factors for each specific time point (Figure 7A). Figure 7A shows a boxplot comparing microbial counts between a control group and a treatment group. Differences between the groups were assessed with a \(t\)-test for each specific time point and boxes with different letters are significantly different at \(p\) < 0.05. In this case, the effect of time was pre-assessed and found to be significant (\(p\) < 0.001), and then time was removed from the model, and the effect of treatment was evaluated at each time point. No difference was found at 0 h between the control and treated group;  
Figure 7: Different graphical representation and statistical analysis approaches to evaluate shelf life studies. (**A**) 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).