scholarly journals Extending the Holding Time for Agricultural Water Testing EPA Method 1603 for Produce Growers

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2020
Author(s):  
Manreet Singh Bhullar ◽  
Angela Shaw ◽  
Joseph Hannan ◽  
Smaranda Andrews
Keyword(s):  
Surface Water ◽  
Test Time ◽  
Time Interval ◽  
Agricultural Water ◽  
E Coli ◽  
Produce Safety ◽  
Water Testing ◽  
Sample Test ◽  
Safety Rule ◽  
Us Epa

Agricultural water is a known vector for the transfer of foodborne pathogens onto fresh produce. Development of pre-harvest and post-harvest microbial profiles of agricultural water used by fresh produce growers, processors, and holdings is a requirement under the Food Safety Modernization Act Produce Safety Rule. One of the United States Environmental Protection Agency (US EPA) approved agricultural water testing methods is US EPA Method 1603, which requires no greater than a 6-h time frame between the collection of the water sample and initiation of analysis. This 6-h timeframe is unrealistic for many produce growers due to there being few laboratories certified to conduct testing and the geographic location of the farms. Agricultural water samples (n = 101) from well water and surface water were collected from 60 different farms to determine if holding samples for 24 h yielded significantly more generic Escherichia coli (E.coli) than 6 h using EPA 1603 method. A total of 32 samples were found contaminated with generic E. coli. Of these positive samples, surface water accounted for 87.5% of the samples (n = 28). There was no significant disparity between populations of generic E. coli at 6- and 24-h sample-test time interval (p > 0.05). These results provide evidence that the sample-test time interval can be extended to 24-h time, which makes quantitative generic E. coli testing for agricultural water as mandated by the FSMA Produce Safety Rule more accessible to growers.

scholarly journals Evaluating the U.S. Food Safety Modernization Act Produce Safety Rule Standard for Microbial Quality of Agricultural Water for Growing Produce

2017 ◽  
Vol 80 (11) ◽  
pp. 1832-1841 ◽  
Author(s):  
Arie H. Havelaar ◽  
Kathleen M. Vazquez ◽  
Zeynal Topalcengiz ◽  
Rafael Muñoz-Carpena ◽  
MICHELLE D. DANYLUK
Keyword(s):  
Water Quality ◽  
Microbial Quality ◽  
Agricultural Water ◽  
Lognormal Distributions ◽  
E Coli ◽  
Produce Safety ◽  
Quality Profile ◽  
Safety Rule ◽  
The U.S

ABSTRACT The U.S. Food and Drug Administration (FDA) has defined standards for the microbial quality of agricultural surface water used for irrigation. According to the FDA produce safety rule (PSR), a microbial water quality profile requires analysis of a minimum of 20 samples for Escherichia coli over 2 to 4 years. The geometric mean (GM) level of E. coli should not exceed 126 CFU/100 mL, and the statistical threshold value (STV) should not exceed 410 CFU/100 mL. The water quality profile should be updated by analysis of a minimum of five samples per year. We used an extensive set of data on levels of E. coli and other fecal indicator organisms, the presence or absence of Salmonella, and physicochemical parameters in six agricultural irrigation ponds in West Central Florida to evaluate the empirical and theoretical basis of this PSR. We found highly variable log-transformed E. coli levels, with standard deviations exceeding those assumed in the PSR by up to threefold. Lognormal distributions provided an acceptable fit to the data in most cases but may underestimate extreme levels. Replacing censored data with the detection limit of the microbial tests underestimated the true variability, leading to biased estimates of GM and STV. Maximum likelihood estimation using truncated lognormal distributions is recommended. Twenty samples are not sufficient to characterize the bacteriological quality of irrigation ponds, and a rolling data set of five samples per year used to update GM and STV values results in highly uncertain results and delays in detecting a shift in water quality. In these ponds, E. coli was an adequate predictor of the presence of Salmonella in 150-mL samples, and turbidity was a second significant variable. The variability in levels of E. coli in agricultural water was higher than that anticipated when the PSR was finalized, and more detailed information based on mechanistic modeling is necessary to develop targeted risk management strategies.

scholarly journals Cross-Validation Indicates Predictive Models May Provide an Alternative to Indicator Organism Monitoring for Evaluating Pathogen Presence in Southwestern US Agricultural Water

2021 ◽  
Vol 3 ◽  
Author(s):  
Alexandra Belias ◽  
Natalie Brassill ◽  
Sherry Roof ◽  
Channah Rock ◽  
Martin Wiedmann ◽  
...  
Keyword(s):  
Surface Water ◽  
Sampling Site ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Indicator Organism ◽  
Agricultural Water ◽  
Canal Water ◽  
E Coli ◽  
Forest Models ◽  
Water Canals

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).

scholarly journals Fate and Biofilm Formation of Wild-Type and Pressure-Stressed Pathogens of Public Health Concern in Surface Water and on Abiotic Surfaces

2020 ◽  
Vol 8 (3) ◽  
pp. 408 ◽  
Author(s):  
Md Niamul Kabir ◽  
Sadiye Aras ◽  
Sabrina Wadood ◽  
Shahid Chowdhury ◽  
Aliyar Cyrus Fouladkhah
Keyword(s):  
Public Health ◽  
Surface Water ◽  
Biofilm Formation ◽  
Health Concern ◽  
Wild Type ◽  
Produce Safety ◽  
Abiotic Surfaces ◽  
Safety Rule ◽  
Public Health Challenge ◽  
Salmonella Serovars

Since the historic outbreak near Broad Street in London, which serves as cornerstone of modern epidemiology, infectious diseases spread in surface and sub-surface water has been a persisting public health challenge. The current study investigated persistence of wild-type and pressure-stressed Listeria monocytogenes, Escherichia coli O157:H7, and non-typhoidal Salmonella enterica serovars in surface water stored aerobically for up to 28 days at 5, 25, and 37 °C. Additionally, biofilm formation of wild-type and pressure-stressed non-typhoidal Salmonella serovars were monitored on surface of stainless steel and rubber coupons for 28 days at 25 and 37 °C. While L. monocytogenes exhibited a lower (p < 0.05) survival rate at 5 °C, relative to the two Gram-negative pathogens, at higher temperatures of 25 and 37 °C, all three pathogens exhibited similar (p ≥ 0.05) trends for survival in surface water. Both wild-type and pressure-stressed Salmonella serovars in the vast majority of tested times, temperatures, and surfaces exhibited comparable (p ≥ 0.05) persistence and biofilm formation capability. Our study thus indicates the occurrence of contamination could lead to prolonged survival of these microorganisms in low-nutrient environments and highlights the need for preventive measures such as those articulated under Produce Safety Rule of the U.S. Food Safety Modernization Act.

Evaluation of Microbiological Quality of Agricultural Water and Effect of Water Source and Holding Temperature on the Stability of Indicator Organisms' Levels by Seven U.S. Environmental Protection Agency–Approved Methods

2020 ◽  
Vol 83 (2) ◽  
pp. 249-255
Author(s):  
ACHYUT ADHIKARI ◽  
VIJAY SINGH CHHETRI ◽  
ANDREA CAMAS
Keyword(s):  
Surface Water ◽  
Environmental Protection ◽  
Environmental Protection Agency ◽  
Water Source ◽  
Indicator Organisms ◽  
Agricultural Water ◽  
Protection Agency ◽  
E Coli ◽  
Holding Temperature

ABSTRACT The Food Safety Modernization Act Produce Safety Rule requires covered produce growers to monitor the quality of their agricultural water on a regular basis by some U.S. Environmental Protection Agency (EPA)–approved methods recognized by the U.S. Food and Drug Administration. In this study, we evaluated the changes in the population of indicator organisms in surface water up to 6 months, and the effects of water source and holding temperature on the survival of indicator organisms by seven EPA-approved methods (five methods for Escherichia coli and two methods for Enterococcus). The levels of E. coli and Enterococcus in the surface water were variable with sampling month, ranging from 1.61 ± 0.04 to 2.68 ± 0.15 log most probable number (MPN)/100 mL and from undetectable level to 1.19 ± 0.29 log MPN/100 mL, respectively. At 25°C (holding temperature), there were significant reductions (P &lt; 0.05) in E. coli and Enterococcus populations in surface water after 48 and 24 h, respectively, whereas at 4°C, no significant changes in the bacterial populations were observed up to 48 h. Methods 1603, 1604, 1103.1, 10029, and Colilert showed a comparable sensitivity in quantifying E. coli, whereas method 1600 and Enterolert showed a variable sensitivity with the type of water. The results indicated that regular monitoring of agricultural water is essential to examine whether the microbial quality of water is appropriate for its intended use. Water samples should be maintained at 4°C to minimize the changes in microbial populations between sampling and testing. The comparison of the sensitivity of EPA methods for quantifying indicator organisms could provide growers with useful information for choosing the method for their water quality analysis. HIGHLIGHTS

Microbial Quality of Agricultural Water Used in Produce Preharvest Production on the Eastern Shore of Virginia

2018 ◽  
Vol 81 (10) ◽  
pp. 1661-1672 ◽  
Author(s):  
LAURA N. TRUITT ◽  
KATHLEEN M. VAZQUEZ ◽  
RACHEL C. PFUNTNER ◽  
STEVEN L. RIDEOUT ◽  
ARIE H. HAVELAAR ◽  
...  
Keyword(s):  
Food Safety ◽  
Water Samples ◽  
Aerobic Bacteria ◽  
Microbial Quality ◽  
Agricultural Water ◽  
Eastern Shore ◽  
Probable Number ◽  
Produce Safety ◽  
Safety Rule

ABSTRACT Several produce-borne outbreaks have been associated with the use of contaminated water during preharvest applications. Salmonella has been implicated in a number of these outbreaks. The purpose of this study was to evaluate the microbial quality of agricultural surface water used in preharvest production on the Eastern Shore of Virginia in accordance with the Food Safety Modernization Act's Produce Safety Rule water standards. The study also examined the prevalence, concentration, and diversity of Salmonella in those water sources. Water samples (1 L) from 20 agricultural ponds were collected during the 2015 and 2016 growing seasons (n = 400). Total aerobic bacteria, total coliforms, and Escherichia coli were enumerated for each sample. Population levels of each microorganism were calculated per 100-mL sample and log transformed, when necessary. Samples (250 mL) were also enriched for Salmonella. Presumptive Salmonella isolates were confirmed by PCR (invA gene) and were serotyped. In 2016, the concentration of Salmonella in each sample was also estimated by most probable number (MPN). Indicator bacteria and environmental and meteorological factors were analyzed for their association with the detection of a Salmonella-positive water sample by using logistic regression analysis. Seventeen of the 20 ponds met the Food Safety Modernization Act's Produce Safety Rule standards for production agricultural water. Three ponds did not meet the standards because the statistical threshold value exceeded the limit. Salmonella was detected in 19% of water samples in each year (38 of 200 in 2015 and 38 of 200 in 2016). Of the 118 Salmonella isolates serotyped, 14 serotypes were identified with the most prevalent being Salmonella Newport. E. coli concentration, farm, and total aerobic bacteria concentration were significantly associated with the likelihood of detecting a Salmonella-positive sample The average concentration of Salmonella in all samples was 4.44 MPN/100 mL, with the limit of detection being 3.00 MPN/100 mL. The highest concentration of Salmonella was 93.0 MPN/100 mL. These data will assist in a better understanding of the risks that production water poses to produce contamination events.

Food Safety on the Farm: Good Agricultural Practices and Good Handling Practices – Packing Operation Sanitation

EDIS ◽  
2017 ◽  
Vol 2017 (6) ◽  
Author(s):  
Jesscia A. Lepper ◽  
Aswathy Sreedharan ◽  
Renée Goodrich Schneider ◽  
Keith R. Schneider
Keyword(s):  
Food Safety ◽  
Fruits And Vegetables ◽  
Agricultural Practices ◽  
Good Agricultural Practices ◽  
Produce Safety ◽  
Foreign Countries ◽  
Safety Rule ◽  
Handling Practices ◽  
Voluntary Program ◽  
Federal Guidelines

Good agricultural practices (GAPs) and good handling practices (GHPs) encompass the general procedures that growers, packers and processors of fresh fruits and vegetables should follow to ensure the safety of their product. GAPs usually deal with preharvest practices (i.e., in the field), while GHPs cover postharvest practices, including packing, storage and shipping. This factsheet covers GAPs relating to packing operation sanitation. There are seven other Florida Cooperative Extension factsheets in the ‘Food Safety on the Farm’ series that focus on specific aspects of the GAPs program and how they relate to Florida crops and practices. Under the new Food Safety Modernization Act (FSMA), GAPs are a foundation of the Produce Safety Rule (PSR). Other than for round tomatoes in Florida (T-GAPs regulation), GAPs have mainly been a voluntary program. Additionally the PSR mandates all non-exempt operations to follow these new FSMA federal guidelines (6), but all exempt commodities and for those producers exporting to foreign countries, GAPs may still be required. Both the mandatory PSR and GAPs aim to reduce the foodborne illness burden associated with produce.

scholarly journals Correlation of head-up tilt table test time interval with risk of recurrent syncope

2015 ◽  
Vol 67 ◽  
pp. S77
Author(s):  
R.K. Gokhroo ◽  
Kumari Priti ◽  
A. Avinash ◽  
Bhanwar Lal Ranwa ◽  
Kamal Kishor ◽  
...  
Keyword(s):  
Test Time ◽  
Time Interval ◽  
Tilt Table Test ◽  
Tilt Table ◽  
Head Up Tilt

Survival of cryptosporidium parvum, escherichia coli, faecal enterococci and clostridium perfringens in river water: influence of temperature and autochthonous microorganisms

1997 ◽  
Vol 35 (11-12) ◽  
pp. 249-252 ◽  
Author(s):  
G. J. Medema ◽  
M. Bahar ◽  
F. M. Schets
Keyword(s):  
Surface Water ◽  
River Water ◽  
Cryptosporidium Parvum ◽  
Health Hazard ◽  
Influence Of Temperature ◽  
E Coli ◽  
Untreated Water ◽  
Time Required ◽  
Micro Organisms

Oocysts of Cryptosporidium parvum can survive for several months in surface water, one of the main factors determining their success in environmental transmission and thus their health hazard via water. Several factors in the environment, e.g. temperature, presence of predators and exo-enzymes will probably influence oocyst survival. The high persistence of oocysts may also limit the value of traditional faecal indicator bacteria. The aim of this study was to determine the rate at which C parvum oocysts, E coli, faecal enterococci and C perfringens spores die in surface water and the influence of temperature and the presence of autochthonous (micro)organisms on the die-off rate. Microcosms with autoclaved river water were inoculated with the organisms. Microcosms with untreated river water were inoculated with concentrated primary effluent containing the bacteria and with C parvum oocysts. Microcosms were incubated at 5°C or 15°C at 100rpm. Viability of oocysts was monitored by in vitro excystation and dye-exclusion; viability of the bacteria was determined on appropriate selective media. When pseudo first-order die-off kinetics were assumed, the die-off rate of oocysts at 5°C was 0.010 log10/d and at 15°C, 0.006–0.024 log10/d. These rates underestimate die-off since oocyst disintegration was not accounted for. Incubation in autoclaved or untreated water did influence the die-off rate of oocysts at 15°C but not at 5°C. The die-off rate of E coli and enterococci was faster in the non-sterile river water than in autoclaved water at both temperatures. At 15°C, E coli (and possibly E faecium) even multiplied in autoclaved water. In untreated river water, the die-off of E coli and enterococci was approximately 10x faster than die-off of oocysts but die-off rates of C perfringens were lower than those of oocysts. As for oocysts, die-off of the bacteria and spores was faster at 15°C than at 5°C. Oocysts are very persistent in river water: the time required for a 10x reduction in viability being 40–160d at 15°C and 100d at 5°C. Biological/biochemical activity influenced oocyst survival at 15°C and survival of both vegetative bacteria at 5 and 15°C. The rapid die-off of E coli and enterococci makes them less suitable as indicators of oocyst presence in water. As C perfringens survived longer in untreated river water than oocysts, it may prove useful as an indicator of the presence of C parvum.

scholarly journals Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water

2021 ◽  
Vol 9 ◽  
Author(s):  
Daniel Lowell Weller ◽  
Tanzy M. T. Love ◽  
Martin Wiedmann
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Predictive Models ◽  
Training Data ◽  
Agricultural Water ◽  
Learning Models ◽  
Safety Hazards ◽  
E Coli ◽  
Resampling Method ◽  
Machine Learning Models

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.

scholarly journals Holocene climate variability in the Winter Rainfall Zone of South Africa

2013 ◽  
Vol 9 (3) ◽  
pp. 2309-2356 ◽  
Author(s):  
S. Weldeab ◽  
J.-B. W. Stuut ◽  
R. R. Schneider ◽  
W. Siebel
Keyword(s):  
South Africa ◽  
Surface Water ◽  
Thermal State ◽  
Causal Link ◽  
Major Elements ◽  
Ice Age ◽  
Time Interval ◽  
Dust Deposition ◽  
Winter Rainfall ◽  
Phytoplankton Productivity

Abstract. We established a multi-proxy time series comprising analyses of major elements in bulk sediments, Sr and Nd isotopes and grain size of terrigenous fraction, and δ18O and δ13C in tests of Neogloboquadrina pachyderma (sinistral) from a marine sediment sequence recovered off the Orange River. The records reveal coherent patterns of variability that reflect changes in wind strength, precipitation over the river catchments, and upwelling of cold and nutrient-rich coastal waters off western South Africa. The wettest episode of the Holocene in the Winter Rainfall Zone (WRZ) of South Africa occurred during the "Little Ice Age" (700–100 yr BP). Wet phases were accompanied by strengthened coastal water upwellings, a decrease of Agulhas water leakage into the southern Atlantic, and a reduced dust incursion over Antarctica. A continuous aridification trend in the WRZ and a weakening of the southern Benguela Upwelling System (BUS) between 9000 and 5500 yr BP parallel with increase of dust deposition over Antarctica and an enhanced leakage of warm Agulhas water into the southeastern Atlantic. The temporal relationship between precipitation changes in the WRZ, the thermal state of the coastal surface water, and leakage of warm water in southern Atlantic, and variation of dust incursion over Antarctica suggests a causal link that most likely was related to latitudinal shifts of the Southern Hemisphere westerlies. Our results of the mid-Holocene time interval may serve as an analogue to a possible long-term consequence of the current and future southward shift of the westerlies that may result in a decline of rainfall over southwest Africa and a weakened upwelling with implication for phytoplankton productivity and fish stocks. Furthermore, warming of the coastal surface water as a result of warm Agulhas water incursion into the southern BUS may affect coastal fog formation that is critical as moisture source for the endemic flora of the Namaqualand.

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