Data-Driven Insights into Food Contamination

Food contamination poses a persistent threat to public health, necessitating robust monitoring strategies. This study utilizes a comprehensive dataset from the Pan American Health Organization (PAHO) from 2016 to 2021 to examine chemical contaminants across food categories. The analysis focuses on contaminant types, food origins, detection limits, and quantification metrics. We uncover significant insights into contamination patterns by employing data cleaning, exploratory data analysis (EDA), hypothesis testing, and ARIMA-based forecasting. Key findings include the predominance of Aflatoxin and Lead in specific food categories, higher contamination in imported foods, and stable detection trends over time. These insights are pivotal for enhancing food safety measures and guiding regulatory frameworks.