Landscape, Water Quality, and Weather Factors Associated With an Increased Likelihood of Foodborne Pathogen Contamination of New York Streams Used to Source Water for Produce Production

Weller, Daniel; Belias, Alexandra; Green, Hyatt; Roof, Sherry; Wiedmann, Martin

doi:10.3389/fsufs.2019.00124

Cited by 55 publications

(96 citation statements)

References 132 publications

(200 reference statements)

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“…eaeA and stx Codetection A PCR-screen for the eaeA and stx genes (stx1 and stx2) was performed using a real-time BAX Shiga-toxin producing E. coli (STEC) assay (Hygiena as previously described, Weller et al, 2019). Co-detection of the eaeA and stx genes indicates either that (i) an organism (i.e., enterohemorrhagic E. coli) with both genes was present in the sample, or (ii) separate organisms each with one of the genes was present (i.e., Shigatoxin producing E. coli and enteropathogenic E. coli).…”

Section: Salmonella Enrichment and Isolationmentioning

confidence: 99%

“…Following incubation at 41 • C for 24 h, the BAX assay was performed per the manufacturer's instructions. The protocol for performing primary enrichment as well as the BAX Assay can be found in the Supplementary Materials of Weller et al (2019). under Protocol for eaeA-stx Codetection using the Real-time BAX STEC Assay.…”

Section: Salmonella Enrichment and Isolationmentioning

confidence: 99%

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Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

et al. 2020

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Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature of freshwater environments. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between Escherichia coli levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds Ratio [OR] = 2.18), and eaeA-stx codetection (OR = 6.49) was significantly greater for MS compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also showed that eaeA-stx codetection in AZ (OR = 50.2) and NY (OR = 18.4), and Salmonella detection in AZ (OR = 4.4) were significantly associated with E. coli levels, while Salmonella detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not be a suitable indicator of food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural

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Section: Salmonella Enrichment and Isolationmentioning

confidence: 99%

Section: Salmonella Enrichment and Isolationmentioning

confidence: 99%

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

et al. 2020

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“…Following incubation at 35 • C for 24 h, Salmonella negative samples and presumptive Salmonella positive samples were identified using real-time BAX Salmonella a The outcome of the eaeA-stx models was codetection of both the eaeA and the stx genes; in both years all stx-positive samples were also eaeA-positive, as a result the prevalence of samples that were positive for both genes was 69% in 2017 and 68% in 2018. assays (Hygiena, Wilmington, DE). BAX negative samples were considered Salmonella negative, while BAX positive samples were culture-confirmed as Salmonella-positive as previously described (Weller et al, 2020b). The eaeA and stx genes are considered biomarkers for enteropathogenic E. coli, Shiga-toxin producing E. coli (STEC), and/or enterohemorrhagic E. coli.…”

Section: Salmonella and Eaea-stx Detectionmentioning

confidence: 99%

“…Model training and testing were performed using the mlr package 1 . The 2018 dataset (Weller et al, 2020b) FIGURE 2 | Visualization of the inverse distance weighting approach used to calculate the percent of the watershed (A), floodplain (B), and riparian buffer (C) under different land uses. (D-F) Provide a close-up view of (A-C), respectively, for areas near the sampling site.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Three penalized regression learners were used here: lasso, ridge, and elastic net. Regression analysis is commonly used to characterize the association between environmental factors and foodborne pathogen detection (e.g., Benjamin et al, 2015;Ceuppens et al, 2015;Weller et al, 2015Weller et al, , 2020b. A key advantage of regression over "black-box" methods (e.g., RF, SVM) is that the output from regression is highly interpretable (Kuhn and Johnson, 2016).…”

Section: Penalized Regressionmentioning

confidence: 99%

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Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

Weller

Love

Belias

et al. 2020

Front. Sustain. Food Syst.

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While the Food Safety Modernization Act established standards for the use of surface water for produce production, water quality is known to vary over space and time. Targeted approaches for identifying hazards in water that account for this variation may improve growers' ability to address pre-harvest food safety risks. Models that utilize publicly-available data (e.g., land-use, real-time weather) may be useful for developing these approaches. The objective of this study was to use pre-existing datasets collected in 2017 (N = 181 samples) and 2018 (N = 191 samples) to train and test models that predict the likelihood of detecting Salmonella and pathogenic E. coli markers (eaeA, stx) in agricultural water. Four types of features were used to train the models: microbial, physicochemical, spatial and weather. "Full models" were built using all four features types, while "nested models" were built using between one and three types. Twenty learners were used to develop separate full models for each pathogen. Separately, to assess information gain associated with using different feature types, six learners were randomly selected and used to develop nine, nested models each. Performance measures for each model were then calculated and compared against baseline models where E. coli concentration was the sole covariate. In the methods, we outline the advantages and disadvantages of each learner. Overall, full models built using ensemble (e.g., Node Harvest) and "blackbox" (e.g., SVMs) learners out-performed full models built using more interpretable learners (e.g., tree-and rule-based learners) for both outcomes. However, nested eaeAstx models built using interpretable learners and microbial data performed almost as well as these full models. While none of the nested Salmonella models performed as well as the full models, nested models built using spatial data consistently outperformed models that excluded spatial data. These findings demonstrate that machine learning approaches can be used to predict when and where pathogens are likely to be present in agricultural water. This study serves as a proof-of-concept that can be built upon once larger datasets become available and provides guidance on Weller et al. Predicting Foodborne Pathogen Presence in Water the learner-data combinations that should be the foci of future efforts (e.g., tree-based microbial models for pathogenic E. coli).

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Irrigation water and contamination of fresh produce with bacterial foodborne pathogens

Gurtler

Gibson

2022

Current Opinion in Food Science

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Landscape, Water Quality, and Weather Factors Associated With an Increased Likelihood of Foodborne Pathogen Contamination of New York Streams Used to Source Water for Produce Production

Cited by 55 publications

References 132 publications

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

Irrigation water and contamination of fresh produce with bacterial foodborne pathogens

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