# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 4 Type of Food Safety Experiments
### Bio-Mapping of Processing Facilities and Process Monitoring
As its name implies, the R-chart uses the range of a sample set to monitor  
Figure 4: Different graphical representation and statistical analysis approaches while conducting bio-mapping of processing facilities. (**A**) Kernel density estimation of microbial counts (Log CFU/cm\({}^{2}\)) on each sampling location with distinct treatments (Treatment A and Treatment B) applied at different locations in a processing facility (\(n\) = 50 observations per location and treatment). (**B**) Microbial counts (Log CFU/cm\({}^{2}\)) from each sampling location throughout a processing facility (\(n\) = 50 observations per location). 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 are significantly different according to ANOVA followed by pairwise comparison test adjusted Tukey’s test at \(p<0.05\). The points represent the actual data points. both process variations. Control limits are established for range data sets by setting upper and lower control limits symmetrically about the target center line. Figure 5B applies the R-chart to a pork bio-map to compare the gambrel table location with the post-intervention location. With a sample size greater than \(n=12\), standard deviation or an S-chart is a better option to monitor variation [32]. When using an S-chart, s is calculated by averaging the sample standard deviations of the sample sets. Figure 5A presents the SPC of a 10-week poultry bio-map using an S-chart to establish the upper and lower control limits. This plot is very useful to compare initial and final sampling locations which provide information on the effect of interventions and to identify weeks in which process deviations occurred. 4. Statistical Process Control (XmR Chart): This type of statistical process control is commonly used to monitor industrial processes with a subgroup of size one. X is the data being measured, and mK is the mean moving range, which is calculated by the difference between consecutive data point measurements [34]. In an XmR  
Figure 5: Graphical representation of statistical microbial process control for process monitoring. (**A**) Statistical microbial process control (Log CFU/mL) using an S-chart (standard deviation chart) at harvest stage, comparing the initial (live receiving) and final (post-chilling) locations of a processing facility over ten weeks (\(n=5\) observations per location at each sampling week). The horizontal solid line represents the mean of each location, and the dashed lines represent the upper and lower control limits using \(\pm 3\sigma\). The solid line represents the change on the average of each location during 10 different sampling weeks. The solid square represents the mean of each location at each sampling week. (**B**) Statistical microbial process control (Log CFU/mL) using an R-chart (range chart) at the harvest stage, comparing the initial (gambrel table) and final (post-intervention) sampling locations of a processing facility over ten weeks (\(n=5\) observations per location at each sampling week). The horizontal solid line represents the mean of each location, and the dashed lines represent the upper and lower control limits using \(\pm 3\sigma\). The solid line represents the change in the average of each location over 10 different sampling weeks. The solid square represents the mean of each location at each sampling week. chart, as shown in Figure 6, the center line represents the mean of the data, while the dashed lines represent the upper and lower control limits. As mentioned before, these control limits can be established using standard deviation or range; however, for this type of chart, the sequential deviation is calculated, as it better explains the random process variation, while the standard deviation is strongly influenced by systematic variation [50].