# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 4 Type of Food Safety Experiments
### Bio-Mapping of Processing Facilities and Process Monitoring
A robust bio-mapping is one that assesses process control based on relevant microbial indicators and/or pathogen tests to the facility that ultimately facilitates food safety management. For example, 1000 samples can be collected over the course of 10 weeks (5 samples per sampling location, 2 shifts, 10 sampling locations) to construct a poultry facility's bio map. The proposed experimental design can result in a microbial baseline before and after the implementation of higher line speeds under the New Poultry Inspection System (NPIS). This baseline can also serve as an assessment of the impact of antimicrobial intervention modifications and process optimization based on the facility's performance before and after interventions. In this example, 5 samples per shift are used to account for both origin and microbial accumulation at each processing location by ensuring coverage and shift changes over 10 weeks (may provide data on seasonality) within the facility. Sampling for 10 weeks can provide information on week-to-week variability and insight regarding any facility operational or logistical issues. Data Visualization and Data Analysis  
1. Boxplots and Kernel Density Estimations: one way to display bio-mapping data is through the use of boxplots as they constitute a powerful graphing tool to visualize sample data and for comparisons across samples [90]. Box plots can be used with a sample size greater than 5 and deliver more information regarding the tails of the distribution. A full description of the components of a boxplot is presented in Section 4.1.1. In practice, the statistical analysis of bio-mapping data for microbial indicators in a processing facility consists of an analysis of variance, when parametric assumptions are satisfied, as displayed in Figure 4B with a bio-mapping of a beef processing facility encompassing 5 sampling locations. ANOVA is a statistical method applied to uncover the main effects of independent variables on a dependent variable [32]. The estimate of variance is calculated from the sum of squares of the differences between each mean data value and the overall mean value. A Kruskal-Wallis test is used as a non-parametric alternative to ANOVA for data analysis in the case when parametric assumptions are not met. In situations when the ANOVA or Kruskal-Wallis are found to be significant, a pairwise comparison assessment is applied, typically using a pairwise \(t\)-test for significant ANOVA or a Wilcoxon's test for significant Kruskal-Wallis tests [37]. 2. Kernel Density Estimation: A kernel density estimation is an alternative technique used for visualization of bio-mapping data, based on the estimation of probability density function (pdf). The kernel estimation yields a smooth empirical pdf using the individual locations of all sample data [91]. Consequently, using this probability density function estimate is a better option for representation of a \"true\" pdf of a continuous variable. This approach is presented in Figure 4A with a bio-map of a beef processing facility, including 5 sampling locations and the assessment of 2 treatments. 3. Shewart's Statistical Process Control: statistical process control (SPC) is an approach for continuous control of quality and safety which provides tools to monitor, analyze, and evaluate the control of a process with the goal of process efficiency optimization [32]. In terms of process optimization, SPC utilizes monitoring, analysis, and evaluation to achieve a stable process. SPC represents another way to present and visualize microbiological data as that obtained from bio-mapping. Control charts serve to indicate whether a process is in a state of control and can help determine when changes or deviations occur. Two control charts used to display bio-mapping data are the R-chart (based on the range of the results), as seen in Figure 5B, and the S-chart (based on the sample standard deviation), presented in Figure 5A. The applicability of these two types is based on their ability to monitor variations within a process [32].