Comparison between random forest and gradient boosting machine methods for predicting Listeria spp. prevalence in the environment of pastured poultry farms.

Foodborne pathogens such as Listeria spp. contain the ability to survive and multiply in poultry farming environments, which provides a route of contamination for poultry processing environments and final poultry products. An understanding of the effect of meteorological variables on the prevalence of Listeria spp. in the farming environment is lacking. Soil and feces samples were collected from 11 pastured poultry farms from 2014 to 2017. Random forest (RF) and gradient boosting machine (GBM) predictive models were generated to describe and predict Listeria spp. prevalence in feces and soil samples based on meteorological factors at the farming location. This study attempted to demonstrate the use of GBM models in a food safety context and compare their use to RF models. Both feces models performed very well, with area under the curve (AUC) values of 0.905 and 0.855 for the RF and GBM models, respectively. The soil GBM model outperformed the RF model with AUCs of 0.873 and 0.700, respectively. The developed models can be used to predict the prevalence of Listeria spp. in pastured poultry farm environments and should be of great use to poultry farmers, producers, and risk managers. Unlabelled Image • Environmental samples were evaluated from poultry farms for Listeria prevalence. • Listeria prevalence can be predicted by machine learning models based on weather. • Wind speed, temperature, and humidity affect Listeria spp. prevalence. • This study provides a framework for future use of machine learning in food safety. [ABSTRACT FROM AUTHOR]