Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling

Area of Science:

• Food Science and Technology
• Statistical Modeling
• Agricultural Engineering

Background:

• Effective control of carcass temperatures post-slaughter is essential for food safety and quality.
• Existing data from hazard analysis and critical control point (HACCP) systems offer a rich source for analyzing chilling processes.

Purpose of the Study:

• To identify and statistically verify the causal factors influencing beef and pork carcass chilling in slaughterhouses.
• To apply graphical modeling (GM) for understanding the complex relationships between various parameters affecting carcass temperature.

Main Methods:

• Utilized extensive data from daily business reports within a slaughterhouse environment.
• Employed graphical modeling (GM), a multivariate data analysis technique, to establish causal relationships.
• Incorporated parameters such as environmental conditions, chilling room status, and carcass temperatures, structured in a cause-effect model.

Main Results:

• Successfully applied GM to analyze chilling processes for both beef and pork carcasses.
• Visualized causal pathways through path diagrams, highlighting key influencing factors.
• Demonstrated the utility of GM in statistically verifying causality within a food manufacturing context.

Conclusions:

• Graphical modeling provides a robust method for understanding and verifying causality in food manufacturing processes like carcass chilling.
• The findings can inform strategies to optimize chilling control, minimize temperature variations, and reduce bias in slaughterhouse operations.