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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Egg and Poultry Production Safety Research Unit » Research » Publications at this Location » Publication #365574
Research Project: Reduction of Invasive Salmonella enterica in Poultry through Genomics, Phenomics and Field Investigations of Small Multi-Species Farm Environments

Location: Egg and Poultry Production Safety Research Unit

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

Author
item GOLDEN, CHASE - University Of Georgia
item Rothrock, Michael
item MISHRA, ABHINAV - University Of Georgia

Submitted to: Food Research International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/26/2019
Publication Date: 3/28/2019
Citation: Golden, C.E., Rothrock Jr, M.J., Misra, A. 2019. Comparison between random forest and gradient boosting machine methods for predicting Listeria spp. prevalence in the environment of pastured poultry farms. Food Research International. 122:47-55. https://doi.org/10.1016/j.foodres.2019.03.062.
DOI: https://doi.org/10.1016/j.foodres.2019.03.062

Interpretive Summary: 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.

Technical Abstract: 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.