Browsing by Subject "Data analysis"

The field of food control is currently facing challenges due to phenomena such as climate change, globalization of food, and scarcity of official control resources. One important approach in improving efficient control of the safety and quality of food chains is risk-based food control. In this study, data-driven approaches were utilized to provide insights into some of the factors that have affected Finnish food business operators’ (FBOs’) compliance with food safety requirements in recent years. Qlik Sense, a data analytics solution built on the concept of visual analytics, and Python, a programming language suitable for data analysis, were used to analyze food inspection data recorded by local food safety authorities. Interactive data visualizations built in Qlik Sense aimed in supporting open government data (OGD) policies and risk-based control of the Finnish food chain, and so far, received good feedback from end-users. Additional insights into FBOs’ food safety compliance were gained by further analyzing the inspection data in Python along with municipal data. A logistic regression model fit to a subset of the study data found multiple statistically significant predictor variables from both datasets, but its performance was weak. The factors that affected food safety compliance most significantly were the number of years an FBO had operated and the basis for conducting an inspection. Operating years showed a positive correlation with compliance, while a negative relationship was observed with a variety of unplanned inspections, especially when they were conducted based on food poisoning suspects, inspection requests, or some other forms of contact. Out of all inspected food sectors, the one that increased the odds of compliance the most was the food transportation sector. The results of the study advocate for the potential of data-driven approaches in improving risk-based food control, as they are an effective way to gain insights into factors affecting the safety and quality of complex food chains.