This page has only limited features, please log in for full access.

Unclaimed
Daniel Weller
Department of Food Science, Cornell University, United States

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Original research article
Published: 05 August 2021 in Frontiers in Water
Reads 0
Downloads 0

Pathogen contamination of agricultural water has been identified as a probable cause of recalls and outbreaks. However, variability in pathogen presence and concentration complicates the reliable identification of agricultural water at elevated risk of pathogen presence. In this study, we collected data on the presence of Salmonella and genetic markers for enterohemorrhagic E. coli (EHEC; PCR-based detection of stx and eaeA) in southwestern US canal water, which is used as agricultural water for produce. We developed and assessed the accuracy of models to predict the likelihood of pathogen contamination of southwestern US canal water. Based on 169 samples from 60 surface water canals (each sampled 1–3 times), 36% (60/169) and 21% (36/169) of samples were positive for Salmonella presence and EHEC markers, respectively. Water quality parameters (e.g., generic E. coli level, turbidity), surrounding land-use (e.g., natural cover, cropland cover), weather conditions (e.g., temperature), and sampling site characteristics (e.g., canal type) data were collected as predictor variables. Separate conditional forest models were trained for Salmonella isolation and EHEC marker detection, and cross-validated to assess predictive performance. For Salmonella, turbidity, day of year, generic E. coli level, and % natural cover in a 500–1,000 ft (~150–300 m) buffer around the sampling site were the top 4 predictors identified by the conditional forest model. For EHEC markers, generic E. coli level, day of year, % natural cover in a 250–500 ft (~75–150 m) buffer, and % natural cover in a 500–1,000 ft (~150–300 m) buffer were the top 4 predictors. Predictive performance measures (e.g., area under the curve [AUC]) indicated predictive modeling shows potential as an alternative method for assessing the likelihood of pathogen presence in agricultural water. Secondary conditional forest models with generic E. coli level excluded as a predictor showed < 0.01 difference in AUC as compared to the AUC values for the original models (i.e., with generic E. coli level included as a predictor) for both Salmonella (AUC = 0.84) and EHEC markers (AUC = 0.92). Our data suggests models that do not require the inclusion of microbiological data (e.g., indicator organism) show promise for real-time prediction of pathogen contamination of agricultural water (e.g., in surface water canals).

ACS Style

Alexandra Belias; Natalie Brassill; Sherry Roof; Channah Rock; Martin Wiedmann; Daniel Weller. Cross-Validation Indicates Predictive Models May Provide an Alternative to Indicator Organism Monitoring for Evaluating Pathogen Presence in Southwestern US Agricultural Water. Frontiers in Water 2021, 3, 1 .

AMA Style

Alexandra Belias, Natalie Brassill, Sherry Roof, Channah Rock, Martin Wiedmann, Daniel Weller. Cross-Validation Indicates Predictive Models May Provide an Alternative to Indicator Organism Monitoring for Evaluating Pathogen Presence in Southwestern US Agricultural Water. Frontiers in Water. 2021; 3 ():1.

Chicago/Turabian Style

Alexandra Belias; Natalie Brassill; Sherry Roof; Channah Rock; Martin Wiedmann; Daniel Weller. 2021. "Cross-Validation Indicates Predictive Models May Provide an Alternative to Indicator Organism Monitoring for Evaluating Pathogen Presence in Southwestern US Agricultural Water." Frontiers in Water 3, no. : 1.

Article
Published: 15 July 2021 in Nature Microbiology
Reads 0
Downloads 0

Natural bacterial populations can display enormous genomic diversity, primarily in the form of gene content variation caused by the frequent exchange of DNA with the local environment. However, the ecological drivers of genomic variability and the role of selection remain controversial. Here, we address this gap by developing a nationwide atlas of 1,854 Listeria isolates, collected systematically from soils across the contiguous United States. We found that Listeria was present across a wide range of environmental parameters, being mainly controlled by soil moisture, molybdenum and salinity concentrations. Whole-genome data from 594 representative strains allowed us to decompose Listeria diversity into 12 phylogroups, each with large differences in habitat breadth and endemism. ‘Cosmopolitan’ phylogroups, prevalent across many different habitats, had more open pangenomes and displayed weaker linkage disequilibrium, reflecting higher rates of gene gain and loss, and allele exchange than phylogroups with narrow habitat ranges. Cosmopolitan phylogroups also had a large fraction of genes affected by positive selection. The effect of positive selection was more pronounced in the phylogroup-specific core genome, suggesting that lineage-specific core genes are important drivers of adaptation. These results indicate that genome flexibility and recombination are the consequence of selection to survive in variable environments. A population genomic analysis of 1,854 Listeria soil isolates collected across the contiguous United States identifies geographically prevalent phylogroups with increased pangenome openness and recombination, as a result of adaptation to variable environments.

ACS Style

Jingqiu Liao; Xiaodong Guo; Daniel L. Weller; Shaul Pollak; Daniel H. Buckley; Martin Wiedmann; Otto X. Cordero. Nationwide genomic atlas of soil-dwelling Listeria reveals effects of selection and population ecology on pangenome evolution. Nature Microbiology 2021, 6, 1021 -1030.

AMA Style

Jingqiu Liao, Xiaodong Guo, Daniel L. Weller, Shaul Pollak, Daniel H. Buckley, Martin Wiedmann, Otto X. Cordero. Nationwide genomic atlas of soil-dwelling Listeria reveals effects of selection and population ecology on pangenome evolution. Nature Microbiology. 2021; 6 (8):1021-1030.

Chicago/Turabian Style

Jingqiu Liao; Xiaodong Guo; Daniel L. Weller; Shaul Pollak; Daniel H. Buckley; Martin Wiedmann; Otto X. Cordero. 2021. "Nationwide genomic atlas of soil-dwelling Listeria reveals effects of selection and population ecology on pangenome evolution." Nature Microbiology 6, no. 8: 1021-1030.

Original research article
Published: 30 June 2021 in Frontiers in Environmental Science
Reads 0
Downloads 0

Recent studies have shown that predictive models can supplement or provide alternatives to E. coli-testing for assessing the potential presence of food safety hazards in water used for produce production. However, these studies used balanced training data and focused on enteric pathogens. As such, research is needed to determine 1) if predictive models can be used to assess Listeria contamination of agricultural water, and 2) how resampling (to deal with imbalanced data) affects performance of these models. To address these knowledge gaps, this study developed models that predict nonpathogenic Listeria spp. (excluding L. monocytogenes) and L. monocytogenes presence in agricultural water using various combinations of learner (e.g., random forest, regression), feature type, and resampling method (none, oversampling, SMOTE). Four feature types were used in model training: microbial, physicochemical, spatial, and weather. “Full models” were trained using all four feature types, while “nested models” used between one and three types. In total, 45 full (15 learners*3 resampling approaches) and 108 nested (5 learners*9 feature sets*3 resampling approaches) models were trained per outcome. Model performance was compared against baseline models where E. coli concentration was the sole predictor. Overall, the machine learning models outperformed the baseline E. coli models, with random forests outperforming models built using other learners (e.g., rule-based learners). Resampling produced more accurate models than not resampling, with SMOTE models outperforming, on average, oversampling models. Regardless of resampling method, spatial and physicochemical water quality features drove accurate predictions for the nonpathogenic Listeria spp. and L. monocytogenes models, respectively. Overall, these findings 1) illustrate the need for alternatives to existing E. coli-based monitoring programs for assessing agricultural water for the presence of potential food safety hazards, and 2) suggest that predictive models may be one such alternative. Moreover, these findings provide a conceptual framework for how such models can be developed in the future with the ultimate aim of developing models that can be integrated into on-farm risk management programs. For example, future studies should consider using random forest learners, SMOTE resampling, and spatial features to develop models to predict the presence of foodborne pathogens, such as L. monocytogenes, in agricultural water when the training data is imbalanced.

ACS Style

Daniel Lowell Weller; Tanzy M. T. Love; Martin Wiedmann. Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water. Frontiers in Environmental Science 2021, 9, 1 .

AMA Style

Daniel Lowell Weller, Tanzy M. T. Love, Martin Wiedmann. Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water. Frontiers in Environmental Science. 2021; 9 ():1.

Chicago/Turabian Style

Daniel Lowell Weller; Tanzy M. T. Love; Martin Wiedmann. 2021. "Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water." Frontiers in Environmental Science 9, no. : 1.

Journal article
Published: 17 May 2021 in International Journal of Systematic and Evolutionary Microbiology
Reads 0
Downloads 0

A total of 27 Listeria isolates that could not be classified to the species level were obtained from soil samples from different locations in the contiguous United States and an agricultural water sample from New York. Whole-genome sequence-based average nucleotide identity blast (ANIb) showed that the 27 isolates form five distinct clusters; for each cluster, all draft genomes showed ANI values of <95 % similarity to each other and any currently described Listeria species, indicating that each cluster represents a novel species. Of the five novel species, three cluster with the Listeria sensu stricto clade and two cluster with sensu lato. One of the novel sensu stricto species, designated L. cossartiae sp. nov., contains two subclusters with an average ANI similarity of 94.9%, which were designated as subspecies. The proposed three novel sensu stricto species (including two subspecies) are Listeria farberi sp. nov. (type strain FSL L7-0091T=CCUG 74668T=LMG 31917T; maximum ANI 91.9 % to L. innocua ), Listeria immobilis sp. nov. (type strain FSL L7-1519T=CCUG 74666T=LMG 31920T; maximum ANI 87.4 % to L. ivanovii subsp. londoniensis ) and Listeria cossartiae sp. nov. [subsp. cossartiae (type strain FSL L7-1447T=CCUG 74667T=LMG 31919T; maximum ANI 93.4 % to L. marthii ) and subsp. cayugensis (type strain FSL L7-0993T=CCUG 74670T=LMG 31918T; maximum ANI 94.7 % to L. marthii ). The two proposed novel sensu lato species are Listeria portnoyi sp. nov. (type strain FSL L7-1582T=CCUG 74671T=LMG 31921T; maximum ANI value of 88.9 % to L. cornellensis and 89.2 % to L. newyorkensis ) and Listeria rustica sp. nov. (type strain FSL W9-0585T=CCUG 74665T=LMG 31922T; maximum ANI value of 88.7 % to L. cornellensis and 88.9 % to L . newyorkensis ). L. immobilis is the first sensu stricto species isolated to date that is non-motile. All five of the novel species are non-haemolytic and negative for phosphatidylinositol-specific phospholipase C activity; the draft genomes lack the virulence genes found in Listeria pathogenicity island 1 (LIPI-1), and the internalin genes inlA and inlB, indicating that they are non-pathogenic.

ACS Style

Catharine R. Carlin; Jingqiu Liao; Dan Weller; Xiaodong Guo; Renato Orsi; Martin Wiedmann. Listeria cossartiae sp. nov., Listeria immobilis sp. nov., Listeria portnoyi sp. nov. and Listeria rustica sp. nov., isolated from agricultural water and natural environments. International Journal of Systematic and Evolutionary Microbiology 2021, 71, 004795 .

AMA Style

Catharine R. Carlin, Jingqiu Liao, Dan Weller, Xiaodong Guo, Renato Orsi, Martin Wiedmann. Listeria cossartiae sp. nov., Listeria immobilis sp. nov., Listeria portnoyi sp. nov. and Listeria rustica sp. nov., isolated from agricultural water and natural environments. International Journal of Systematic and Evolutionary Microbiology. 2021; 71 (5):004795.

Chicago/Turabian Style

Catharine R. Carlin; Jingqiu Liao; Dan Weller; Xiaodong Guo; Renato Orsi; Martin Wiedmann. 2021. "Listeria cossartiae sp. nov., Listeria immobilis sp. nov., Listeria portnoyi sp. nov. and Listeria rustica sp. nov., isolated from agricultural water and natural environments." International Journal of Systematic and Evolutionary Microbiology 71, no. 5: 004795.

Artificial intelligence
Published: 14 May 2021 in Frontiers in Artificial Intelligence
Reads 0
Downloads 0

Since E. coli is considered a fecal indicator in surface water, government water quality standards and industry guidance often rely on E. coli monitoring to identify when there is an increased risk of pathogen contamination of water used for produce production (e.g., for irrigation). However, studies have indicated that E. coli testing can present an economic burden to growers and that time lags between sampling and obtaining results may reduce the utility of these data. Models that predict E. coli levels in agricultural water may provide a mechanism for overcoming these obstacles. Thus, this proof-of-concept study uses previously published datasets to train, test, and compare E. coli predictive models using multiple algorithms and performance measures. Since the collection of different feature data carries specific costs for growers, predictive performance was compared for models built using different feature types [geospatial, water quality, stream traits, and/or weather features]. Model performance was assessed against baseline regression models. Model performance varied considerably with root-mean-squared errors and Kendall’s Tau ranging between 0.37 and 1.03, and 0.07 and 0.55, respectively. Overall, models that included turbidity, rain, and temperature outperformed all other models regardless of the algorithm used. Turbidity and weather factors were also found to drive model accuracy even when other feature types were included in the model. These findings confirm previous conclusions that machine learning models may be useful for predicting when, where, and at what level E. coli (and associated hazards) are likely to be present in preharvest agricultural water sources. This study also identifies specific algorithm-predictor combinations that should be the foci of future efforts to develop deployable models (i.e., models that can be used to guide on-farm decision-making and risk mitigation). When deploying E. coli predictive models in the field, it is important to note that past research indicates an inconsistent relationship between E. coli levels and foodborne pathogen presence. Thus, models that predict E. coli levels in agricultural water may be useful for assessing fecal contamination status and ensuring compliance with regulations but should not be used to assess the risk that specific pathogens of concern (e.g., Salmonella, Listeria) are present.

ACS Style

Daniel L. Weller; Tanzy M. T. Love; Martin Wiedmann. Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water. Frontiers in Artificial Intelligence 2021, 4, 1 .

AMA Style

Daniel L. Weller, Tanzy M. T. Love, Martin Wiedmann. Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water. Frontiers in Artificial Intelligence. 2021; 4 ():1.

Chicago/Turabian Style

Daniel L. Weller; Tanzy M. T. Love; Martin Wiedmann. 2021. "Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water." Frontiers in Artificial Intelligence 4, no. : 1.

Original research article
Published: 06 October 2020 in Frontiers in Sustainable Food Systems
Reads 0
Downloads 0

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 “black-box” (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 eaeA-stx 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 out-performed 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 the learner-data combinations that should be the foci of future efforts (e.g., tree-based microbial models for pathogenic E. coli).

ACS Style

Daniel L. Weller; Tanzy M. T. Love; Alexandra Belias; Martin Wiedmann. 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. Frontiers in Sustainable Food Systems 2020, 4, 1 .

AMA Style

Daniel L. Weller, Tanzy M. T. Love, Alexandra Belias, Martin Wiedmann. 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. Frontiers in Sustainable Food Systems. 2020; 4 ():1.

Chicago/Turabian Style

Daniel L. Weller; Tanzy M. T. Love; Alexandra Belias; Martin Wiedmann. 2020. "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." Frontiers in Sustainable Food Systems 4, no. : 1.

Article
Published: 18 August 2020 in Applied and Environmental Microbiology
Reads 0
Downloads 0

The log-linear die-off rate proposed by FSMA is not always appropriate, as the die-off rates of foodborne bacterial pathogens and specified agricultural water quality indicator organisms appear to commonly follow a biphasic pattern with an initial rapid decline followed by a period of tailing. While we observed substantial variation in the net culturable population levels of Salmonella and E. coli at each time point, die-off rate and FSMA compliance (i.e., at least a 2 log 10 die-off over 4 days) appear to be impacted by produce type, bacteria, and weather; die-off on lettuce tended to be faster than that on spinach, die-off of E. coli tended to be faster than that of attenuated Salmonella , and die-off tended to become faster as relative humidity decreased. Thus, the use of a single die-off rate for estimating time-to-harvest intervals across different weather conditions, produce types, and bacteria should be revised.

ACS Style

Alexandra M. Belias; Adrian Sbodio; Pilar Truchado; Daniel Weller; Janneth Pinzon; Mariya Skots; Ana Allende; Daniel Munther; Trevor Suslow; Martin Wiedmann; Renata Ivanek. Effect of Weather on the Die-Off of Escherichia coli and Attenuated Salmonella enterica Serovar Typhimurium on Preharvest Leafy Greens following Irrigation with Contaminated Water. Applied and Environmental Microbiology 2020, 86, 1 .

AMA Style

Alexandra M. Belias, Adrian Sbodio, Pilar Truchado, Daniel Weller, Janneth Pinzon, Mariya Skots, Ana Allende, Daniel Munther, Trevor Suslow, Martin Wiedmann, Renata Ivanek. Effect of Weather on the Die-Off of Escherichia coli and Attenuated Salmonella enterica Serovar Typhimurium on Preharvest Leafy Greens following Irrigation with Contaminated Water. Applied and Environmental Microbiology. 2020; 86 (17):1.

Chicago/Turabian Style

Alexandra M. Belias; Adrian Sbodio; Pilar Truchado; Daniel Weller; Janneth Pinzon; Mariya Skots; Ana Allende; Daniel Munther; Trevor Suslow; Martin Wiedmann; Renata Ivanek. 2020. "Effect of Weather on the Die-Off of Escherichia coli and Attenuated Salmonella enterica Serovar Typhimurium on Preharvest Leafy Greens following Irrigation with Contaminated Water." Applied and Environmental Microbiology 86, no. 17: 1.

Brief report
Published: 30 July 2020 in Horticulturae
Reads 0
Downloads 0

Although many studies have investigated foodborne pathogen prevalence in conventional produce production environments, relatively few have investigated prevalence in aquaponics and hydroponics systems. This study sought to address this knowledge gap by enumerating total coliform and generic E. coli levels, and testing for Salmonella presence in circulating water samples collected from five hydroponic systems and three aquaponic systems (No. of samples = 79). While total coliform levels ranged between 6.3 Most Probable Number (MPN)/100-mL and the upper limit of detection (2496 MPN/100-mL), only three samples had detectable levels of E. coli and no samples had detectable levels of Salmonella. Of the three E. coli positive samples, two samples had just one MPN of E. coli/100-mL while the third had 53.9 MPN of E. coli/100-mL. While the sample size reported here was small and site selection was not randomized, this study adds key data on the microbial quality of aquaponics and hydroponics systems to the literature. Moreover, these data suggest that contamination in these systems occurs at relatively low-levels, and that future studies are needed to more fully explore when and how microbial contamination of aquaponics and hydroponic systems is likely to occur.

ACS Style

Daniel Weller; Lauren Saylor; Paula Turkon. Total Coliform and Generic E. coli Levels, and Salmonella Presence in Eight Experimental Aquaponics and Hydroponics Systems: A Brief Report Highlighting Exploratory Data. Horticulturae 2020, 6, 42 .

AMA Style

Daniel Weller, Lauren Saylor, Paula Turkon. Total Coliform and Generic E. coli Levels, and Salmonella Presence in Eight Experimental Aquaponics and Hydroponics Systems: A Brief Report Highlighting Exploratory Data. Horticulturae. 2020; 6 (3):42.

Chicago/Turabian Style

Daniel Weller; Lauren Saylor; Paula Turkon. 2020. "Total Coliform and Generic E. coli Levels, and Salmonella Presence in Eight Experimental Aquaponics and Hydroponics Systems: A Brief Report Highlighting Exploratory Data." Horticulturae 6, no. 3: 42.

Original research article
Published: 29 July 2020 in Frontiers in Microbiology
Reads 0
Downloads 0

Surface water used for produce production is a potential source of pre-harvest contamination with foodborne pathogens. Decisions on how to mitigate food safety risks associated with pre-harvest water use currently rely on generic Escherichia coli-based water quality tests, although multiple studies have suggested that E. coli levels are not a suitable indicator of the food safety risks under all relevant environmental conditions. Hence, improved understanding of spatiotemporal variability in surface water microbiota composition is needed to facilitate identification of alternative or supplementary indicators that co-occur with pathogens. To this end, we aimed to characterize the composition of bacterial and fungal communities in the sediment and water fractions of 68 agricultural water samples collected from six New York streams. We investigated potential associations between the composition of microbial communities, environmental factors and Salmonella and/or Listeria monocytogenes isolation. We found significantly different composition of fungal and bacterial communities among sampled streams and among water fractions of collected samples. This indicates that geography and the amount of sediment in a collected water sample may affect its microbial composition, which was further supported by identified associations between the flow rate, turbidity, pH and conductivity, and microbial community composition. Lastly, we identified specific microbial families that were weakly associated with the presence of Salmonella or Listeria monocytogenes, however, further studies on samples from additional streams are needed to assess whether identified families may be used as indicators of pathogen presence.

ACS Style

Taejung Chung; Daniel Weller; Jasna Kovac. The Composition of Microbial Communities in Six Streams, and Its Association With Environmental Conditions, and Foodborne Pathogen Isolation. Frontiers in Microbiology 2020, 11, 1757 .

AMA Style

Taejung Chung, Daniel Weller, Jasna Kovac. The Composition of Microbial Communities in Six Streams, and Its Association With Environmental Conditions, and Foodborne Pathogen Isolation. Frontiers in Microbiology. 2020; 11 ():1757.

Chicago/Turabian Style

Taejung Chung; Daniel Weller; Jasna Kovac. 2020. "The Composition of Microbial Communities in Six Streams, and Its Association With Environmental Conditions, and Foodborne Pathogen Isolation." Frontiers in Microbiology 11, no. : 1757.

Journal article
Published: 01 July 2020 in Journal of Food Protection
Reads 0
Downloads 0

Past studies have shown that the on-farm distribution of Listeria monocytogenes is affected by environmental factors (e.g., weather). However, most studies were conducted at large scales (e.g., across farms), whereas few studies examined drivers of L. monocytogenes prevalence at smaller scales (e.g., within a single field). This study was performed to address this knowledge gap by (i) tracking L. monocytogenes distribution in two fields on one farm over a growing season and (ii) identifying factors associated with L. monocytogenes isolation from drag swab, soil, and agricultural water samples. Overall, L. monocytogenes was detected in 78% (21 of 27), 19% (7 of 36), and 8% (37 of 486) of water, drag swab, and soil samples, respectively. All isolates were characterized by pulsed-field gel electrophoresis. Of the 43 types identified, 14 were isolated on multiple sampling visits and/or from multiple sample types, indicating persistence in or repeated introduction into the farm environment during the study. Our findings also suggest that L. monocytogenes prevalence, even at the small spatial scale studied here, (i) was not uniform and (ii) varied more within fields than between fields or over time. This is illustrated by plot (in-field variation), field (between-field variation), and sampling visit (time), accounting for 18, 2, and 3% of variance in odds of isolating L. monocytogenes, respectively. Moreover, according to random forest analysis, water-related factors were among the top-ranked factors associated with L. monocytogenes isolation from all sample types. For example, the likelihood of isolating L. monocytogenes from drag and soil samples increased monotonically as rainfall increased. Overall, findings from this single-farm study suggests that mitigation strategies for L. monocytogenes in produce fields should focus on water-associated risk factors (e.g., rain and distance to water) and be tailored to specific high-risk in-field areas. HIGHLIGHTS

ACS Style

Anna Sophia Harrand; Laura Strawn; Paola Mercedes Illas-Ortiz; Martin Wiedmann; Daniel Weller. Listeria monocytogenes Prevalence Varies More within Fields Than between Fields or over Time on Conventionally Farmed New York Produce Fields. Journal of Food Protection 2020, 83, 1958 -1966.

AMA Style

Anna Sophia Harrand, Laura Strawn, Paola Mercedes Illas-Ortiz, Martin Wiedmann, Daniel Weller. Listeria monocytogenes Prevalence Varies More within Fields Than between Fields or over Time on Conventionally Farmed New York Produce Fields. Journal of Food Protection. 2020; 83 (11):1958-1966.

Chicago/Turabian Style

Anna Sophia Harrand; Laura Strawn; Paola Mercedes Illas-Ortiz; Martin Wiedmann; Daniel Weller. 2020. "Listeria monocytogenes Prevalence Varies More within Fields Than between Fields or over Time on Conventionally Farmed New York Produce Fields." Journal of Food Protection 83, no. 11: 1958-1966.

Original research article
Published: 06 February 2020 in Frontiers in Sustainable Food Systems
Reads 0
Downloads 0

There is a need for science-based tools to (i) help manage microbial produce safety hazards associated with preharvest surface water use, and (ii) facilitate comanagement of agroecosystems for competing stakeholder aims. To develop these tools an improved understanding of foodborne pathogen ecology in freshwater systems is needed. The purpose of this study was to identify (i) sources of potential food safety hazards, and (ii) combinations of factors associated with an increased likelihood of pathogen contamination of agricultural water. Sixty-eight streams were sampled between April and October 2018 (196 samples). At each sampling event separate 10-L grab samples (GS) were collected and tested for Listeria, Salmonella, and the stx and eaeA genes. A 1-L GS was also collected and used for Escherichia coli enumeration and detection of four host-associated fecal source-tracking markers (FST). Regression analysis was used to identify individual factors that were significantly associated with pathogen detection. We found that eaeA-stx codetection [Odds Ratio (OR) = 4.2; 95% Confidence Interval (CI) = 1.3, 13.4] and Salmonella isolation (OR = 1.8; CI = 0.9, 3.5) were strongly associated with detection of ruminant and human FST markers, respectively, while Listeria spp. (excluding Listeria monocytogenes) was negatively associated with log10 E. coli levels (OR = 0.50; CI = 0.26, 0.96). L. monocytogenes isolation was not associated with the detection of any fecal indicators. This observation supports the current understanding that, unlike enteric pathogens, Listeria is not fecally-associated and instead originates from other environmental sources. Separately, conditional inference trees were used to identify scenarios associated with an elevated or reduced risk of pathogen contamination. Interestingly, while the likelihood of isolating L. monocytogenes appears to be driven by complex interactions between environmental factors, the likelihood of Salmonella isolation and eaeA-stx codetection were driven by physicochemical water quality (e.g., dissolved oxygen) and temperature, respectively. Overall, these models identify environmental conditions associated with an enhanced risk of pathogen presence in agricultural water (e.g., rain events were associated with L. monocytogenes isolation from samples collected downstream of dairy farms; P = 0.002). The information presented here will enable growers to comanage their operations to mitigate the produce safety risks associated with preharvest surface water use.

ACS Style

Daniel Weller; Alexandra Belias; Hyatt Green; Sherry Roof; Martin Wiedmann. 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. Frontiers in Sustainable Food Systems 2020, 3, 1 .

AMA Style

Daniel Weller, Alexandra Belias, Hyatt Green, Sherry Roof, Martin Wiedmann. 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. Frontiers in Sustainable Food Systems. 2020; 3 ():1.

Chicago/Turabian Style

Daniel Weller; Alexandra Belias; Hyatt Green; Sherry Roof; Martin Wiedmann. 2020. "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." Frontiers in Sustainable Food Systems 3, no. : 1.

Original research article
Published: 06 February 2020 in Frontiers in Microbiology
Reads 0
Downloads 0

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 water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.

ACS Style

Daniel Weller; Natalie Brassill; Channah Rock; Renata Ivanek; Erika Mudrak; Sherry Roof; Erika Ganda; Martin Wiedmann. Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water. Frontiers in Microbiology 2020, 11, 134 .

AMA Style

Daniel Weller, Natalie Brassill, Channah Rock, Renata Ivanek, Erika Mudrak, Sherry Roof, Erika Ganda, Martin Wiedmann. Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water. Frontiers in Microbiology. 2020; 11 ():134.

Chicago/Turabian Style

Daniel Weller; Natalie Brassill; Channah Rock; Renata Ivanek; Erika Mudrak; Sherry Roof; Erika Ganda; Martin Wiedmann. 2020. "Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water." Frontiers in Microbiology 11, no. : 134.

Preprint content
Published: 02 January 2020
Reads 0
Downloads 0

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, and because environmental conditions affect the likelihood of pathogen contamination and the relationship between indicator organism levels (e.g., E. coli) and pathogen presence. 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 E. 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 the 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 water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.

ACS Style

Daniel Lowell Weller; Natalie Brassill; Channah Rock; Renata Ivanek; Erika Mudrak; Sherry Roof; Erika Ganda; Martin Wiedmann. Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water. 2020, 1 .

AMA Style

Daniel Lowell Weller, Natalie Brassill, Channah Rock, Renata Ivanek, Erika Mudrak, Sherry Roof, Erika Ganda, Martin Wiedmann. Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water. . 2020; ():1.

Chicago/Turabian Style

Daniel Lowell Weller; Natalie Brassill; Channah Rock; Renata Ivanek; Erika Mudrak; Sherry Roof; Erika Ganda; Martin Wiedmann. 2020. "Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water." , no. : 1.

Journal article
Published: 17 October 2019 in Water
Reads 0
Downloads 0

Fecal contamination of waterbodies due to poorly managed human and animal waste is a pervasive problem that can be particularly costly to address, especially if mitigation strategies are ineffective at sufficiently reducing the level of contamination. Identifying the most worrisome sources of contamination is particularly difficult in periurban streams with multiple land uses and requires the distinction of municipal, agricultural, domestic pet, and natural (i.e., wildlife) wastes. Microbial source-tracking (MST) methods that target host-specific members of the bacterial order Bacteroidales and others have been used worldwide to identify the origins of fecal contamination. We conducted a dry-weather study of Onondaga Creek, NY, where reducing fecal contamination has been approached mainly by mitigating combined sewer overflow events (CSOs). Over three sampling dates, we measured in-stream concentrations of fecal indicator bacteria; MST markers targeting human, ruminant, and canine sources; and various physical–chemical parameters to identify contaminants not attributable to CSOs or stormwater runoff. We observed that despite significant ruminant inputs upstream, these contaminants eventually decayed and/or were diluted out and that high levels of urban bacterial contamination are most likely due to failing infrastructure and/or illicit discharges independent of rain events. Similar dynamics may control other streams that transition from agricultural to urban areas with failing infrastructure.

ACS Style

Hyatt Green; Daniel Weller; Stephanie Johnson; Edward Michalenko. Microbial Source-Tracking Reveals Origins of Fecal Contamination in a Recovering Watershed. Water 2019, 11, 2162 .

AMA Style

Hyatt Green, Daniel Weller, Stephanie Johnson, Edward Michalenko. Microbial Source-Tracking Reveals Origins of Fecal Contamination in a Recovering Watershed. Water. 2019; 11 (10):2162.

Chicago/Turabian Style

Hyatt Green; Daniel Weller; Stephanie Johnson; Edward Michalenko. 2019. "Microbial Source-Tracking Reveals Origins of Fecal Contamination in a Recovering Watershed." Water 11, no. 10: 2162.

Journal article
Published: 24 May 2019 in Journal of Food Protection
Reads 0
Downloads 0

Results of previous studies revealed that (i) splash can transfer microbes from in-field feces to preharvest produce and (ii) wildlife can be vectors for the introduction of foodborne pathogens into produce fields. However, few peer-reviewed studies have been conducted to examine pathogen transfer from wildlife feces to in-field produce via splash during irrigation. Although two previous studies found a significant relationship between distance and Escherichia coli transfer via splash, the studies sampled produce <1 m from the feces. The present study was conducted to refine our understanding of the impact of distance on E. coli splash. Two trials were conducted 1 month apart. For each trial, fecal pellets inoculated with a three-strain E. coli cocktail were placed in a lettuce field 2.5 h before irrigation. After irrigation, E. coli levels on lettuce heads 0 to 6 m from the pellets were determined. Although E. coli was not detected in any of the heads ≥2 m from the fecal pellets (n = 39), 39% of heads (13 of 33) <2 m from the pellets tested positive for E. coli. According to logistic regression, the odds of harvesting a head that tested positive for E. coli decreased by a factor of 50 (odds ratio, 0.02; 95% confidence interval, <0.01, 0.28; P = 0.004) for each meter increase in the distance between the lettuce and the feces. Thus, the likelihood of E. coli transfer from feces to produce should be minimal at a given distance from the feces. Our model can be used to predict the probability of harvesting a microbially contaminated lettuce head following implementation of a no-harvest buffer around in-field feces. For example, our model suggests that the probability of harvesting a contaminated head was 0.1% at 3 m from the feces. Although the approaches utilized in this study provide a conceptual framework that can be used to help define appropriate no-harvest buffers, delineation of appropriate buffer zones requires additional information (e.g., acceptable risk and regional data). HIGHLIGHTS

ACS Style

Daniel L. Weller; Jasna Kovac; David J. Kent; Sherry Roof; Jeffrey I. Tokman; Erika Mudrak; Martin Wiedmann. A Conceptual Framework for Developing Recommendations for No-Harvest Buffers around In-Field Feces. Journal of Food Protection 2019, 82, 1052 -1060.

AMA Style

Daniel L. Weller, Jasna Kovac, David J. Kent, Sherry Roof, Jeffrey I. Tokman, Erika Mudrak, Martin Wiedmann. A Conceptual Framework for Developing Recommendations for No-Harvest Buffers around In-Field Feces. Journal of Food Protection. 2019; 82 (6):1052-1060.

Chicago/Turabian Style

Daniel L. Weller; Jasna Kovac; David J. Kent; Sherry Roof; Jeffrey I. Tokman; Erika Mudrak; Martin Wiedmann. 2019. "A Conceptual Framework for Developing Recommendations for No-Harvest Buffers around In-Field Feces." Journal of Food Protection 82, no. 6: 1052-1060.

Journal article
Published: 01 December 2017 in Food Microbiology
Reads 0
Downloads 0

Wildlife intrusion has been associated with pathogen contamination of produce. However, few studies have examined pathogen transfer from wildlife feces to pre-harvest produce. This study was performed to calculate transfer coefficients for Escherichia coli from simulated wildlife feces to field-grown lettuce during irrigation. Rabbit feces inoculated with a 3-strain cocktail of non-pathogenic E. coli were placed in a lettuce field 2.5-72 h before irrigation. Following irrigation, the E. coli concentration on the lettuce was determined. After exclusion of an outlier with high E. coli levels (Most Probable Number = 5.94*10(8)), the average percent of E. coli in the feces that transferred to intact lettuce heads was 0.0267% (Standard Error [SE] = 0.0172). Log-linear regression showed that significantly more E. coli transferred to outer leaves compared to inner leaves (Effect = 1.3; 95% Confidence Interval = 0.4, 2.1). Additionally, the percent of E. coli that transferred from the feces to the lettuce decreased significantly with time after fecal placement, and as the distance between the lettuce and the feces, and the lettuce and the sprinklers increased. These findings provide key data that may be used in future quantitative risk assessments to identify potential intervention strategies for reducing food safety risks associated with fresh produce.

ACS Style

Daniel Weller; Jasna Kovac; David J. Kent; Sherry Roof; Jeffrey I. Tokman; Erika Mudrak; Barbara Kowalcyk; David Oryang; Anna Aceituno; Martin Wiedmann. Escherichia coli transfer from simulated wildlife feces to lettuce during foliar irrigation: A field study in the Northeastern United States. Food Microbiology 2017, 68, 24 -33.

AMA Style

Daniel Weller, Jasna Kovac, David J. Kent, Sherry Roof, Jeffrey I. Tokman, Erika Mudrak, Barbara Kowalcyk, David Oryang, Anna Aceituno, Martin Wiedmann. Escherichia coli transfer from simulated wildlife feces to lettuce during foliar irrigation: A field study in the Northeastern United States. Food Microbiology. 2017; 68 ():24-33.

Chicago/Turabian Style

Daniel Weller; Jasna Kovac; David J. Kent; Sherry Roof; Jeffrey I. Tokman; Erika Mudrak; Barbara Kowalcyk; David Oryang; Anna Aceituno; Martin Wiedmann. 2017. "Escherichia coli transfer from simulated wildlife feces to lettuce during foliar irrigation: A field study in the Northeastern United States." Food Microbiology 68, no. : 24-33.

Journal article
Published: 20 June 2017 in Journal of Food Protection
Reads 0
Downloads 0

Although wildlife intrusion and untreated manure have been associated with microbial contamination of produce, relatively few studies have examined the survival of Escherichia coli on produce under field conditions following contamination (e.g., via splash from wildlife feces). This experimental study was performed to estimate the die-off rate of E. coli on preharvest lettuce following contamination with a fecal slurry. During August 2015, field-grown lettuce was inoculated via pipette with a fecal slurry that was spiked with a three-strain cocktail of rifampin-resistant nonpathogenic E. coli. Ten lettuce heads were harvested at each of 13 time points following inoculation (0, 2.5, 5, and 24 h after inoculation and every 24 h thereafter until day 10). The most probable number (MPN) of E. coli on each lettuce head was determined, and die-off rates were estimated. The relationship between sample time and the log MPN of E. coli per head was modeled using a segmented linear model. This model had a breakpoint at 106 h (95% confidence interval = 69, 142 h) after inoculation, with a daily decrease of 0.70 and 0.19 log MPN for 0 to 106 h and 106 to 240 h following inoculation, respectively. These findings are consistent with die-off rates obtained in similar studies that assessed E. coli survival on produce following irrigation. Overall, these findings provide die-off rates for E. coli on lettuce that can be used in future quantitative risk assessments.

ACS Style

Daniel Weller; Jasna Kovac; Sherry Roof; David J. Kent; Jeffrey I. Tokman; Barbara Kowalcyk; David Oryang; Renata Ivanek; Anna Aceituno; Christopher Sroka; Martin Wiedmann. Survival of Escherichia coli on Lettuce under Field Conditions Encountered in the Northeastern United States. Journal of Food Protection 2017, 80, 1214 -1221.

AMA Style

Daniel Weller, Jasna Kovac, Sherry Roof, David J. Kent, Jeffrey I. Tokman, Barbara Kowalcyk, David Oryang, Renata Ivanek, Anna Aceituno, Christopher Sroka, Martin Wiedmann. Survival of Escherichia coli on Lettuce under Field Conditions Encountered in the Northeastern United States. Journal of Food Protection. 2017; 80 (7):1214-1221.

Chicago/Turabian Style

Daniel Weller; Jasna Kovac; Sherry Roof; David J. Kent; Jeffrey I. Tokman; Barbara Kowalcyk; David Oryang; Renata Ivanek; Anna Aceituno; Christopher Sroka; Martin Wiedmann. 2017. "Survival of Escherichia coli on Lettuce under Field Conditions Encountered in the Northeastern United States." Journal of Food Protection 80, no. 7: 1214-1221.

Journal article
Published: 01 November 2016 in Trends in Food Science & Technology
Reads 0
Downloads 0
ACS Style

Siyun Wang; Daniel Weller; Justin Falardeau; Laura Strawn; Fernando Mardones; Aiko D. Adell; Andrea I. Moreno Switt. Food safety trends: From globalization of whole genome sequencing to application of new tools to prevent foodborne diseases. Trends in Food Science & Technology 2016, 57, 188 -198.

AMA Style

Siyun Wang, Daniel Weller, Justin Falardeau, Laura Strawn, Fernando Mardones, Aiko D. Adell, Andrea I. Moreno Switt. Food safety trends: From globalization of whole genome sequencing to application of new tools to prevent foodborne diseases. Trends in Food Science & Technology. 2016; 57 ():188-198.

Chicago/Turabian Style

Siyun Wang; Daniel Weller; Justin Falardeau; Laura Strawn; Fernando Mardones; Aiko D. Adell; Andrea I. Moreno Switt. 2016. "Food safety trends: From globalization of whole genome sequencing to application of new tools to prevent foodborne diseases." Trends in Food Science & Technology 57, no. : 188-198.

Validation study
Published: 01 February 2016 in Applied and Environmental Microbiology
Reads 0
Downloads 0

Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs ( n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes , validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce.

ACS Style

Daniel Weller; Suvash Shiwakoti; Peter Bergholz; Yrjo Grohn; Martin Wiedmann; Laura K. Strawn. Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields. Applied and Environmental Microbiology 2016, 82, 797 -807.

AMA Style

Daniel Weller, Suvash Shiwakoti, Peter Bergholz, Yrjo Grohn, Martin Wiedmann, Laura K. Strawn. Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields. Applied and Environmental Microbiology. 2016; 82 (3):797-807.

Chicago/Turabian Style

Daniel Weller; Suvash Shiwakoti; Peter Bergholz; Yrjo Grohn; Martin Wiedmann; Laura K. Strawn. 2016. "Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields." Applied and Environmental Microbiology 82, no. 3: 797-807.

Journal article
Published: 01 September 2015 in Applied and Environmental Microbiology
Reads 0
Downloads 0

While rain and irrigation events have been associated with an increased prevalence of foodborne pathogens in produce production environments, quantitative data are needed to determine the effects of various spatial and temporal factors on the risk of produce contamination following these events. This study was performed to quantify these effects and to determine the impact of rain and irrigation events on the detection frequency and diversity of Listeria species (including L. monocytogenes ) and L. monocytogenes in produce fields. Two spinach fields, with high and low predicted risks of L. monocytogenes isolation, were sampled 24, 48, 72, and 144 to 192 h following irrigation and rain events. Predicted risk was a function of the field's proximity to water and roads. Factors were evaluated for their association with Listeria species and L. monocytogenes isolation by using generalized linear mixed models (GLMMs). In total, 1,492 (1,092 soil, 334 leaf, 14 fecal, and 52 water) samples were collected. According to the GLMM, the likelihood of Listeria species and L. monocytogenes isolation from soil samples was highest during the 24 h immediately following an event (odds ratios [ORs] of 7.7 and 25, respectively). Additionally, Listeria species and L. monocytogenes isolates associated with irrigation events showed significantly lower sigB allele type diversity than did isolates associated with precipitation events ( P = <0.001), suggesting that irrigation water may be a point source of L. monocytogenes contamination. Small changes in management practices (e.g., not irrigating fields before harvest) may therefore reduce the risk of L. monocytogenes contamination of fresh produce.

ACS Style

Daniel Weller; Martin Wiedmann; Laura K. Strawn. Spatial and Temporal Factors Associated with an Increased Prevalence of Listeria monocytogenes in Spinach Fields in New York State. Applied and Environmental Microbiology 2015, 81, 6059 -6069.

AMA Style

Daniel Weller, Martin Wiedmann, Laura K. Strawn. Spatial and Temporal Factors Associated with an Increased Prevalence of Listeria monocytogenes in Spinach Fields in New York State. Applied and Environmental Microbiology. 2015; 81 (17):6059-6069.

Chicago/Turabian Style

Daniel Weller; Martin Wiedmann; Laura K. Strawn. 2015. "Spatial and Temporal Factors Associated with an Increased Prevalence of Listeria monocytogenes in Spinach Fields in New York State." Applied and Environmental Microbiology 81, no. 17: 6059-6069.