Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Abstract

The purpose of this study was to evaluate the performance of artificial intelligence tools for the prediction of Salmonella presence and absence in agricultural surface waters based on the population of microbiological indicators (total coliform, generic Escherichia coli, and enterococci) and physicochemical attributes of water (air and water temperature, conductivity, ORP, pH, and turbidity). Previously collected data set from six agricultural ponds monitored for two growing seasons were used for analysis. Classification algorithms including artificial neural networks (ANNs), the nearest neighborhood algorithm (kNN), and support vector machines (SVM) were trained and tested with a 539-point data set for optimum prediction accuracy. Classification accuracy performances were validated with data set (400 samples) collected from different agricultural surface water sources. All tested algorithms yielded the highest accuracy around 75 ± 1% for generic E. coli followed by enterococci (65 ± 5%) and total coliform (60 ± 10%). Classifiers calculated 6–15% higher accuracy ranging from 62 to 66% for turbidity than all other tested physicochemical attributes. Based on E. coli populations measured in other water sources, trained algorithms predicted the presence and absence of Salmonella with an accuracy between 58.15 and 59.23%. The classification performance of ANN, kNN, and SVM algorithms are encouraging for the prediction of Salmonella in agricultural surface waters. © 2019 Wiley Periodicals, Inc.