Implications of new legislation (US FSMA) and guidelines (EC) on the establishment of management systems for agricultural water

Allende, Ana; Datta, Atin R.; Smith, Woutrina A.; Adonis, R.; Mackay, A. D.; Adell, Aiko D.

doi:10.1016/j.fm.2017.10.002

Cited by 9 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since, in the current study, the relationship between natural cover and Salmonella presence and EHEC marker detection is weak, additional research is needed to fully characterize the role wildlife plays as a source of enteric pathogen contamination for southwestern canals. Given the need for additional research, and the important ecosystem services provided by natural cover (e.g., water filtration) and wildlife (e.g., pest control, pollination), the authors want to emphasize we are not advocating the removal of natural cover or wildlife from growing areas (Aarons and Gourley, 2012;Allende et al, 2018;Navarro-Gonzalez et al, 2020).…”

Section: Natural Cover and The Day Of Year Are Important For Prediction Of Salmonella Presence And Ehec Marker Detectionmentioning

confidence: 99%

“…While some studies have established associations between generic E. coli levels and pathogen presence (Holvoet et al, 2014;Lopez-Galvez et al, 2014;Stea et al, 2015;Bradshaw et al, 2016;Truchado et al, 2018;Weller et al, 2020b), several studies have not shown similar associations (Haley et al, 2009;Shelton et al, 2011;Benjamin et al, 2013;Falardeau et al, 2017). E. coli has also been shown to survive for extended periods of time in the preharvest environment (Lu et al, 2004;Franz et al, 2014;Allende et al, 2018); therefore, a high level of generic E. coli does not necessarily indicate recent fecal contamination. High levels of indicator organisms also do not necessarily indicate the presence of pathogens, and alternatively, the absence or low levels of indicator organisms do not necessarily indicate the absence of pathogens (Haley et al, 2009;Shelton et al, 2011;Benjamin et al, 2013;Stea et al, 2015;Falardeau et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Belias

Brassill²,

Roof³

et al. 2021

Front. Water

View full text Add to dashboard Cite

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).

show abstract

Section: Natural Cover and The Day Of Year Are Important For Prediction Of Salmonella Presence And Ehec Marker Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Belias

Brassill²,

Roof³

et al. 2021

Front. Water

View full text Add to dashboard Cite

show abstract

“…The Food Safety Modernization Act (FSMA) addressed this issue with a series of directives released between 2015 and 2020. In general, the directives were intended to change the U.S. food systems focus from a reactive or responsive safety paradigm to a preventative safety paradigm [14][15][16][17]. The role of sensors/detectors within the modern food supply chain has been reviewed by several groups [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Sense–Analyze–Respond–Actuate (SARA) Paradigm: Proof of Concept System Spanning Nanoscale and Macroscale Actuation for Detection of Escherichia coli in Aqueous Media

et al. 2020

View full text Add to dashboard Cite

Foodborne pathogens are a major concern for public health. We demonstrate for the first time a partially automated sensing system for rapid (~17 min), label-free impedimetric detection of Escherichia coli spp. in food samples (vegetable broth) and hydroponic media (aeroponic lettuce system) based on temperature-responsive poly(N-isopropylacrylamide) (PNIPAAm) nanobrushes. This proof of concept (PoC) for the Sense-Analyze-Respond-Actuate (SARA) paradigm uses a biomimetic nanostructure that is analyzed and actuated with a smartphone. The bio-inspired soft material and sensing mechanism is inspired by binary symbiotic systems found in nature, where low concentrations of bacteria are captured from complex matrices by brush actuation driven by concentration gradients at the tissue surface. To mimic this natural actuation system, carbon-metal nanohybrid sensors were fabricated as the transducer layer, and coated with PNIPAAm nanobrushes. The most effective coating and actuation protocol for E. coli detection at various temperatures above/below the critical solution temperature of PNIPAAm was determined using a series of electrochemical experiments. After analyzing nanobrush actuation in stagnant media, we developed a flow through system using a series of pumps that are triggered by electrochemical events at the surface of the biosensor. SARA PoC may be viewed as a cyber-physical system that actuates nanomaterials using smartphone-based electroanalytical testing of samples. This study demonstrates thermal actuation of polymer nanobrushes to detect (sense) bacteria using a cyber-physical systems (CPS) approach. This PoC may catalyze the development of smart sensors capable of actuation at the nanoscale (stimulus-response polymer) and macroscale (non-microfluidic pumping).

show abstract

“…Recently, EFSA identified the generic E. coli as suitable for a hygiene criterion that could be considered for validation and verification of GAP and good hygiene practices (GHPs); this allows growers to take appropriate corrective actions [24]. The current European legal framework does not include microbiological criteria applicable at the primary production stage [66]. …”

Section: Food Safety Concerns and Standards Are Growing Globallymentioning

confidence: 99%

A Review on the Rising Prevalence of International Standards: Threats or Opportunities for the Agri-Food Produce Sector in Developing Countries, with a Focus on Examples from the MENA Region

Faour-Klingbeil¹,

Todd²

2018

Foods

View full text Add to dashboard Cite

Food safety standards are a necessity to protect consumers’ health in today’s growing global food trade. A number of studies have suggested safety standards can interrupt trade, bringing financial and technical burdens on small as well as large agri-food producers in developing countries. Other examples have shown that economical extension, key intermediaries, and funded initiatives have substantially enhanced the capacities of growers in some countries of the Middle East and North Africa (MENA) region to meet the food safety and quality requirements, and improve their access to international markets. These endeavors often compensate for the weak regulatory framework, but do not offer a sustainable solution. There is a big gap in the food safety level and control systems between countries in the MENA region and those in the developed nations. This certainly has implications for the safety of fresh produce and agricultural practices, which hinders any progress in their international food trade. To overcome the barriers of legal and private standards, food safety should be a national priority for sustainable agricultural development in the MENA countries. Local governments have a primary role in adopting the vision for developing and facilitating the implementation of their national Good Agricultural Practices (GAP) standards that are consistent with the international requirements and adapted to local policies and environment. Together, the public and private sector’s support are instrumental to deliver the skills and infrastructure needed for leveraging the safety and quality level of the agri-food chain.

show abstract

Implications of new legislation (US FSMA) and guidelines (EC) on the establishment of management systems for agricultural water

Cited by 9 publications

References 34 publications

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

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

Sense–Analyze–Respond–Actuate (SARA) Paradigm: Proof of Concept System Spanning Nanoscale and Macroscale Actuation for Detection of Escherichia coli in Aqueous Media

A Review on the Rising Prevalence of International Standards: Threats or Opportunities for the Agri-Food Produce Sector in Developing Countries, with a Focus on Examples from the MENA Region

Contact Info

Product

Resources

About