Temporal and spatial patterns in the detection of veterinary drug residues in poultry and swine in Brazil

Abstract

Food Safety is an important topic for public health and international trade in food. Residues of veterinary drugs and environmental contaminants in animal products can cause diseases and acute toxicity in organisms exposed to these substances. This study evaluated official monitoring data of veterinary drug residues from the Brazilian Ministry of Agriculture, Livestock and Supply in tissues of poultry and swine in the period between 2002 and 2014 to check for hidden patterns in the occurrence of six common drugs (Closantel, Diclazuril, Nicarbazin, Sulfaquinoxaline, Doxycycline and Sulfamethazinein). The analysis of data was performed by using two machine learning methods: decision tree and neural networks, in addition to visual evaluation through graphs and maps. Contamination rates were low, varying from 0 to 0.66%. A spatial distribution pattern of detections of substances by region was identified, but no pattern of temporal distribution was observed. Nevertless, regressions showed an increase in levels when these substances were detected, so monitoring should continue. However, the results show that the products monitored during the study period presented a low risk to public health.

Keywords
Machine learning; food safety; public health; residues