Progress 09/01/19 to 08/31/24
Outputs
Target Audience:Goal 1.The key beneficiaries of this research are fresh produce growers and processors, who will benefit from the findings of the conducted quantitative microbial risk assessments of food safety risks of E. coli O157:H7 in fresh-cut lettuce and L. monocytogenes in fresh-cut cantaloupe, and effective interventions to reduce the risks. Additionally, this research will help industry navigate among different food safety interventions based on improved understanding of cost-effective, environmentally friendly and socially acceptable interventions in reducing public health risks. Such efforts will enhance thesafety and sustainability of the fresh produce supply, whichwill benefit consumers and society as a whole. Goal 2.This objective goal will directly benefit produce growers/farms by reducing the risk of field contamination with pathogens through agricultural water and soil. This goal will also indirectly benefit produce packers and processors, as fewer pathogens will be introduced into packing/processing facilities through the raw materials. Academia, government, and industry professionals will also all benefit from the massive publicly available database that was generated from this effort, which can be found here: https://github.com/wellerd2/Weller-et-al-2024-AEM-Datasets/tree/main. Anyone can use this for future projects, or to develop or enhance GIS-based predictive models. Goal 3. The key beneficiaries of this research will be produce packers, processors, and retailers; these groups are the intended users of the agent-based models developed for this goal. Food safety managers can use the developed agent-based models to evaluate different sampling plans and interventions to facilitate the decision-making process. Goal 4. The key beneficiaries of the systematic scoping review will be the industry professionals and researchers, who will benefit from the comprehensive search and synthesis of literature in digital food safety. Academia and industry will also benefit from our finding that active learning methods are an effective alternative to laborious manual literature searches, which is crucial for staying up-to-date with the rapidly growing body of digital food safety literature.Finally, the virtually library website will benefit any internet user who is interested in digital food safety, as it allows foreasy access to existing food safety models. Goal 5. The key beneficiaries and target audiences for the produce safety certificate program arestakeholder groups across the fresh produce production, processing, and distribution continuum. This includes(i) growers, (ii) packers and processors, (iii) retailers, (iv) consumer educators, as well as (v)developers of modeling technologies for use in produce food safety. Goal 6. Academic educators, as well as undergraduate and graduate students with an interest/focus in produce safety and digital produce safety will directly benefit from the trainingmodules developed for this objective goal. Indirectly, the fresh produce industry will also benefit, as they will be able to hire new personnel that are better trained on how to (i) tackle real world issues in produce safety and (ii) use modeling tools to enhance produce safety. Changes/Problems:Goal 1. Nothing to report. Goal 2. Nothing to report. Goal 3. Nothing to report. Goal 4. Nothing to report. Goal 5.The pandemic introduced unforeseen hurdles that included retirements and position changes of senior industry representatives that initially agreed to participate in the development of some of the certificate program training materials.Additionally, due to the 2024 change in agricultural water requirements, the Control of Produce Contamination in the Field course is being updated to reflect the current changes in regulatory requirements. Goal 6. Nothing to report. What opportunities for training and professional development has the project provided?Goal 1.A Research Associate (Dr. Ece Bulut) and a Postdoctoral Researcher (Dr. Sarah I. Murphy) were provided training in quantitative microbial risk assessment models. Additionally, aPhD student in Food Science and Technology (Linda Kalunga) and a PhD student in Hotel Administration(Jieyu Hao) received training in applied economics and sustainability analysis. Bulut, Murphy, Hao, and Kalunga were provided with experience in public speaking at research conferences. Goal 2.This project goal provided training opportunities in ecology, microbiology, GIS and R based modeling tools for a PhD student (ClaireMurphy) and two MS students (ClaraDiekman, CamrynCook) who were supported on this grant. AlexisHamilton, a Postdoctoral Researcher, assisted in data cleaning, and was provided training on tidy data, and data structure. Murphy, Cook, Diekman and Hamilton were provided with experience in public speaking at research conferences (IAFP). Goal 3.Three PhD students (Genevieve Sullivan, Luke [Chenhao] Qian, YeonJin Jung) and one MS student (Cecil Barnett-Neefs) were provided training on how to (i) develop agent-based models using NetLogo as a programming language and (ii) analyze outcomes of agent-based models using statistical methods. Qian, Jung, and Barnett-Neefs were provided with experience in public speaking at research conferences (IAFP, AIFS). Goal 4.A high school student (Lily Grower), an undergraduate student (Sophia Ruser), and two PhD students (Linda Kalunga, Luke [Chenhao]Qian) received training in conducting systematic reviews. Ruser also received training in website development and in writing a successful research proposal for a summer research grant from the Cornell Institute for Digital Agriculture that complemented research in this project. An undergraduate student (Tyler Wu) received training in using active learning classifiers for screening of articles in literature reviews. Kalunga, Ruser, and Wu were provided with experience in public speaking at research conferences. Goal 5. Graduate students (8) and Postdoctoral Researchers (2) received trainingin curriculum development, skill-building in the various courses for industry-focused educational materials, andnetworking with industry connections. Additionally, an Extension Support Specialist (Louise Felker) and an Extension Aide (Maria Witlox) have been actively engaged in thedevelopment and distribution of select courses developed for the produce safety certificate program. Goal 6.A Senior Research Associate (Dr. Renato Orsi)and aTechnician (Zoe Wasserlauf)were trained in online surveydevelopment. Three PhD students (Luke [Chenhao] Qian, Samantha Bolten, YeonJin Jung), one MS student (Cecil Barnett-Neefs), and two Postdoctoral Researchers (Dr. Ece Bulut, Dr. Sophia Harrand) received training in the development of video-based education materials and knowledge assessments. Additionally, four PhD students (Alexandra Belias, Claire Murphy, Jonathan Sogin, Yadwinder Singh Rana) supported the development of video-based education materials by participating in active discussions during lecture recordings. How have the results been disseminated to communities of interest?Goal 1. Results were shared among the members of the external advisory council (comprised of the representatives of the produce industry - growers, processors and distributors, trade and research organizations, and government agencies). Additionally, results were shared among researchers, students, and industry professionals at food safety conferences, including the International Association for Food Protection (IAFP) Annual Meeting and Society for Risk Analysis (SRA) Annual Meeting, andThe National Academies of Sciences, Engineering, and Medicine: Food and Nutrition Board Meeting. Goal 2. Results were presented at professional food safety conferences (e.g., IAFP Annual Meeting, local affiliate chapters of IAFP). IAFP is made up of university academics, extension specialists, government officials and researchers, industry professionals and other food safety experts. Results and bigger key findings were also shared with members of our external advisory council, which represents members from produce companies like growers, packers, processors, shippers, retailers, trade associations, among others. Lastly, all the work from this objective has now been published in open access peer-reviewed journals (Journal of Food Protection, Frontiers, Applied and Environmental Microbiology, and Spectrum). Goal 3.Results have been shared in threemanuscripts published in open access peer-reviewed journals (Applied and Environmental Microbiology, Plos one, Journal of Food Protection), as well as in presentations at professional food safetyconferences (IAFP Annual Meetings). Additionally, two educational videos that provide basic information on how to developand interpretagent-based models for food safety wereshared on the Artificial Intelligence Institute forFood Systems website (https://aifs.ucdavis.edu/education-and-outreach/educational-modules) anddisseminated to selectindustry professionals. Furthermore, avideolecture covering agent-based modeling principles(developed as part of the advanced modeling module under Goal 6)has also been publicly sharedon our website(https://cals.cornell.edu/produce-safety-coe/training/teaching-modules)to target more technical audiences. Goal 4. The developed virtual library website (https://cals.cornell.edu/institute-for-food-safety/resources/model-virtual-library)is openly accessibleto any internet user;main beneficiaries are industry food safety professionals, and academic and industry software developers. Additionally, research was shared by undergraduate students in different research venues, including at the Symposium on Artificial Intelligence in Veterinary Medicine (SAVY), the Ecology & Evolutionary Biology Graduate Student Symposium, and the Sigma Xi Undergraduate Research Symposium. Goal 5. Learning objectives and course outcomes were shared with our external advisory council and collaborators for input and feedback.Broader dissemination of outcomes will occur with tested modules via project websites and industry partnerships. Goal 6.All developed modules have been disseminated to a variety of student communities of interest. For example, (i) the basic produce safety principles module was piloted to undergraduate students as part of an experiential learning program at Virginia Polytechnic Institute and State University in 2021; responses from pre- and post-knowledge assessments revealed that the students were typically able to achieve the expected learning outcomes from the module. In addition, both produce safety and modeling modules have been shared with (ii) graduate student interns at iFood Decision Sciences, and(iii) members of our external advisory council for input and feedback. All recorded lectures have also been shared on our YouTube channel (https://www.youtube.com/@producesafetycenterofexcel8306) and have garnered a combined total of over 500 views. Finally, all lecture materials (i.e., recorded lecture videos, slide sets, knowledge assessments) developed for eachmodule are freely available and can be accessed on our public website (https://cals.cornell.edu/produce-safety-coe/training/teaching-modules) to provide further opportunities to train undergraduate and graduate students on digital produce safety applications outside of traditional class room settings. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Goal 1.We conducted quantitative microbial risk assessments (QMRA) for (i)E. coliO157:H7 (ECO157) in fresh-cut lettuce (Bulut et al., submitted), and (ii)L. monocytogenes(LM) in fresh-cut cantaloupe (Murphy & Bulut et al., submitted). For the fresh-cut lettuce QMRA, irrigation was the most important source of ECO157 contamination in the preharvest environment; risks could be reduced through irrigation water treatments or by switching from overhead spray to furrow or drip irrigation. For the fresh-cut cantaloupe QMRA, time/temperature conditions post-packaging had the greatest impact on LM per contaminated serving and annual number of illnesses; risks could be mitigated by reducing temperature and/or time conditions post-packaging. Key interventions identified in QMRAs were assessed experimentally (Usaga et al., 2022) and/or evaluated in a cost-benefit sustainability analysis (Hao and Kalunga et al., in writing), including irrigation-based interventions for controlling ECO157 in fresh-cut lettuce (e.g., surface water treatment using chlorine, peracetic acid, or UV light) and time/temperature control interventions for controlling LM in fresh-cut cantaloupe (e.g., open vs. closed refrigeration storage at retail). This comprehensive approach allowedus to determine the most effective interventions for reducing foodborne pathogen contamination while balancing economic costs, public health, and ecological considerations. Goal 2.Field studies conducted in Florida and Virginia revealed regional and site-specific variations in soil microbial ecology (Cook et al., 2023; Diekman et al., 2024). In Florida, Salmonella prevalence was minimal (0.4%), compared to generic E. coli (11.3%); soil properties and farm-specific factors influenced microbial prevalence. In Virginia, Salmonella prevalence was higher (4.2%), with detections concentrated on a single farm, emphasizing localized and regional differences. L. monocytogenes was detected in 2.5% of samples, with soil pH positively associated with its presence. These results provide data for developing geographic information systems (GIS)-based models to predict pathogen hotspots and inform site-specific interventions. Variance partitioning and condition forest analysis performed on microbial water quality (MWQ) data for >2.4 million samples from >500 studies revealed that methodological variability was a major barrier to generating standardized insights into pathogen ecology (Murphy et al., 2022, 2023, 2024; Weller et al., 2022, 2024). For example, for Salmonella and other pathogens, only 13% of variance in detection likelihood was uniquely attributed to non-methodological factors, underscoring the need for standardized MWQ testing methods to improve data reliability and GIS tool accuracy. To address this, we created a factsheet on recommended practices for collecting, recording, and reporting of MWQ research data and attributes (see products-factsheet). Overall, our findings demonstrate how leveraging field data with GIS-based analyses can enhance preharvest produce safety strategies (e.g., guide risk assessments, prioritizeinterventions). Goal 3. We developed agent-based models (ABM) tosimulateListeriatransmission in 2 fresh-cut produce processing facilities (Sullivan et al., 2021), 2 produce packinghouses (Barnett-Neefs et al., 2022), and the produce section of 1 retail store (Jung et al., 2024). All ABMs were parameterized using observations, values from published literature, and expert input, and were validated using historical environmental sampling data. Eachmodel's baseline conditions were investigated and used to test the effectiveness of various corrective actions/interventions with regard to reducing Listeria contamination. For fresh-cut facility/packinghouse ABM, corrective actions focused on (i) implementing risk-based cleaning and sanitation, (ii) modifyingequipment connectivity, and (iii) reducing Listeria introduction via raw materials were all effective at reducingListeria prevalence, indicating that well-designed cleaning and sanitation schedules and good manufacturing practices can be effective in controlling contamination, even if incomingListeriaon raw materials cannot be fully controlled. Meanwhile, for the retail store ABM, interventions focused on reducing initial Listeria concentration on incoming produce (e.g., through more stringent supplier qualification) were most effective at reducing Listeria contamination in the retail environment. Overall, these findings demonstrate the value of using ABM to improve environmental Listeria control in produce-associated built environments. Goal 4.We conducted a systematic scoping review of digital food safety models (Kalunga et al, in writing). Primary searches runacross6 databases (e.g., PubMed, Web of Science) from (i) 2012-2022 and updated for (ii) 2022-2024identified 7,181 articles.After deduplication and manual title and abstract screening, 377 articles were selected for full-text review, with 86 articles ultimately meeting inclusion criteria. These 86 articles cover use of risk assessments, machine learning, and other computational and statistical models to enhance food safety. Additionally, we re-screened deduplicated articles identified from 2012-2022 using semi-automated active learning models to assess whether active learning approaches can replicate manual selection. Here, 3 active learning models were tested in 2 scenarios: screening an unlabeled dataset and screening a labeled benchmarked dataset. Results showed that active learning methods significantly outperformed manual screening across all models (Wu et al., submitted). Finally, we developed a virtual library of food safety models identified in this scoping review (hosted on the Cornell Institute for Food Safety webpage) to offer stakeholders centralized access to digital-support tools for improving food safety (Ruser et al., 2024). Goal 5.A national produce safety certificate program was developed that provides training and certification to individuals across the produce industry through a mixed-model of online and in-person courses. Courses represent 4 specialized certificate tracks specific to produce Growers, Packers & Processors, Retailers, and Consumer Educators. All participants take three core produce safety courses: Introduction to Food Microbiology, Systems Approach to Produce Safety, and Produce Traceability. Each track has 1-3 specialized courses, including (i) GAPs and Control of Produce Contamination in the Field for the Growers track; (ii) GMPs, PCQI or Packinghouse HACCP, and Environmental Monitoring Programs for the Packers & Processors track; (iii) Food Code and Strategies for Retailers to Prevent Contamination for the Retailers track; and (iv) Consumer Produce Safety for Educators for the Consumer Educators track. Advanced certification requires the completion of 1 of 3 advanced courses: Produce Safety Train-the-Trainer, Modeling for Produce Safety, GIS tools for Produce Safety. Goal 6.We developed 3 asynchronous teaching modules to train undergraduate and graduate students on fundamental produce safety principles and how to apply computational modeling tools towards produce safety. Each module includes 2-3 recorded lecture videos (30-60 min each) that cover topics related to (1) basic produce safety principles (e.g., how to identify and manage produce safety risks along the farm-to-fork continuum), (2) basic modeling principles (e.g., introduction to modeling concepts and evaluation methods), and (3) advanced modeling principles (e.g., use of GIS and ABM to address produce safety challenges). Pre- and post-knowledge assessments were also developed for each lecture to evaluate whether defined learning outcomes were met.
Publications
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2022
Citation:
Usaga, J., Beauvais, W., Englishbey, A.K., Marchesan Marconi, C., Cholula, U., Belias, A.M., Wemette, M., Churey, J.J., Worobo, R.W., Enciso, J., Anciso, J.R., Nightingale, K., Ivanek, R. Inactivation of Salmonella and E. coli in surface agricultural water using a commercial UV processing unit. Food Protection Trends. 2022. 42(5): 377-382
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Rosenthal, H., Beauvais, W., Zoellner, C., Greiner Safi, A., Mathios, A., Ivanek, R. Knowledge, health, and social drivers of frozen vegetable consumption practices relevant to listeriosis in women of childbearing age. Journal of Food Protection, 2024. Volume 87, Issue 8
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2024
Citation:
Wu, T., Ruser, S., Ivanek , R. Active learning models to screen articles for a systematic review of literature on food safety. Symposium on Artificial Intelligence in Veterinary Medicine (SAVY), Cornell University, Ithaca, NY, April 19-21, 2024. Poster Presentation.
- Type:
Other
Status:
Published
Year Published:
2024
Citation:
Murphy, C.M., D.L. Weller, M.D. Danyluk, and L.K. Strawn. 2024. Water Clarity: The Need for Standardized Data Collection and Methods for Assessing and Managing Water Quality. Food Safety Resource Clearinghouse. Accessed here: https://foodsafetyclearinghouse.org/resources/water-clarity-need-standardized-data-collection-and-methods-assessing-and-managing-water
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Belias, A., Bolten, S., Orsi, R. H., Wiedmann, M. 2024. Application of environmental monitoring programs and root cause analysis to identify and implement interventions to reduce or eliminate Listeria populations in apple packinghouses. Journal of Food Protection, 87(8) 100324.
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Belias, A., Bolten, S., & Wiedmann, M. (2024). Challenges and opportunities for risk- and systems-based control of Listeria monocytogenes transmission through food. Comprehensive Reviews in Food Science and Food Safety, 23(6), e70071.
- Type:
Websites
Status:
Published
Year Published:
2024
Citation:
Ruser, S., Kalunga, L., Luongo, N., and Ivanek, R. Institute for Food Safety at Cornell (2024). Food Safety Model Virtual Library. Virtual library of digital food safety tools. https://cals.cornell.edu/institute-for-food-safety/resources/model-virtual-library
- Type:
Peer Reviewed Journal Articles
Status:
Accepted
Year Published:
2025
Citation:
Murphy, C.M., D.L. Weller, T.M.T. Love, M.D. Danyluk, and L.K. Strawn. 2024. The Probability of Detecting Host-Specific Microbial Source Tracking Markers in Surface Waters Was Strongly Associated with Method and Season. Spectrum.
|
Progress 09/01/22 to 08/31/23
Outputs
Target Audience:The key beneficiaries and target audiences for this project will be fresh produce growers and processors, developers of modeling technologies for use in produce food safety. The project will provide data to help industry to implement sustainable mitigation strategies that are cost-effective, environmentally friendly, and socially acceptable in reducing public health risks, which will benefit the consumers as well. This project also specifically targets produce food safety and quality assurance professionals in industry, academia, and government. Goal 5 focuses on development of comprehensive produce safety extension programs and, in addition, targets retailers and educators. Finally, Goal 6 ("Develop graduate and undergraduate teaching modules to train students to use and develop computational and modelling tools for produce safety") also targets students interested in digital produce food safety. Changes/Problems:Goal 1. We have modified the methodology for the sustainability analysis. We initially considered using a semi-quantitative method, namely expert elicitation, to conduct the sustainability analysis, where the opinions of food industry experts would be quantified. However, we have opted for a fully quantitative, and thus, more rigorous approach. In this new methodology, we are estimating the sustainability impact of each intervention in terms of US dollars based on available literature data. We have identified specific factors that describe the sustainability impacts of each intervention, enabling us to estimate the impacts in US dollars. Any factors that cannot be quantified will be recognized as limitations in our future reports and publications. Goal 2. Nothing to report. Goal 3. Nothing to report. Goal 4. Nothing to report. Goal 5. Nothing to report. Goal 6. Nothing to report. What opportunities for training and professional development has the project provided?Goal 1. Two postdocs, Drs. Ece Bulut and Sarah Murphy have continued training to develop quantitative microbial risk assessment models. A Master of Public Health (MPH) student (Linda Kalunga, Cornell) and a PhD student in Applied Economics and Management (Jieyu Hao, Cornell) received training in sustainability analysis. Goal 2. This project goal has provided training opportunities in GIS and R based modeling tools for a PhD student (Claire Murphy, Virginia Tech) and MS students (Clara Diekman, Camryn Cook, Virginia Tech). Additionally, both students have received basic training in bacterial enumertion and enrichment techniques. Goal 3. Two PhD students (Luke Qian and YeonJin Jung, both at Cornell, are currently being trained on modeling as part of this project component. Goal 4. Two undergraduate students have received training (Sophia Ruser in scoping review and website development and Tyler Wu in using active learning classifiers for screening of articles in literature reviews; both students at Cornell)). Additionally, PhD student (Linda Kalunga, Cornell) received training in economic/sustainability analysis of preharvest produce safety control strategies. Goal 5. A Postdoctoral Research Associate (Dr Magdalena Pajor, Cornell) -, and an Extension Support Specialist (Louise Felker, Cornell), have been actively engaged in the ongoing development and distribution of industry-specific courses. This presents an excellent chance for the Postdoctoral Researcher to not only contribute to the creation of scientific content for professionals beyond academia but also to establish valuable connections in the industry and actively participate in the creation of educational materials. Goal 6. This goal was finalized in the previous reporting period (2021-2022). How have the results been disseminated to communities of interest?For all goals, preliminary results have been shared among the members of the external Advisory Council (comprised of representatives of the produce industry - growers, processors and distributors, trade and research organizations, and government agencies). In addition, a number of preliminary findings have been communicated through abstracts and talks presented at scientific meetings (see below for a complete listing). In 2023, this project also has contributed 5 peer-reviewed publications. In addition, developed videos have been shared on YouTube (combined about 200 views thus far) and with members of the members of the Artificial Intelligence Institute for Next Generation Food Systems. Teaching modules and a virtual library of food safety models and computational decision-support tools, including apps, are shared on the project www site. What do you plan to do during the next reporting period to accomplish the goals?Goal 1. We will complete the manuscripts for the QMRA models and continue to evaluate the sustainability impacts for the identified intervention strategies. Goal 2. Over the next reporting period, we will work to finalize manuscripts associated with Obj 2 outputs including the water quality meta-data method variation analyses (submitted, under review Applied and Environmental Microbiology), and soil surveys (FL submitted Journal of Food Protection, VA accepted, Frontiers). We will also work to publish the meta data associated with our water quality work via a GitHub site (which includes over 100 cleaned and tidied datasets). Goal 3. Finalize manuscripts for publication. Goal 4. We will complete the synthesis of papers on models in food safety identified through a scoping review. We will also complete the website "Virtual Library of Food Safety Models". Goal 5. A dedicated postdoctoral researcher has been hired to this project as previously planned and will be responsible for (i) completing prioritized modules and (ii) beta testing the modules in cooperation with produce safety experts from industry and extension. Through cooperation with the CALS Extension Support developed courses/ teaching modules will be shared on the project www site. Goal 6. This goal was finalized in the previous reporting period (2021-2022).
Impacts
What was accomplished under these goals?
Goal 1. We are finalizing two papers on quantitative microbial risk assessments (QMRA) for two produce-pathogen pairs: (i) Escherichia coli O157:H7 and fresh-cut lettuce (EC) and (ii) Listeria monocytogenes and fresh-cut cantaloupe (LM). For the EC QMRA, our findings highlight the significance of initial field contamination, particularly through the irrigation water source (well or surface) and the irrigation system (overhead spray, drip, or furrow), in determining health risks. In the LM QMRA, our model highlights the impact of time and temperature conditions during post-processing stages, including distribution, retail, and home storage. Based on the outcomes of our QMRA models, the project team has identified potential interventions for mitigating health risks and is currently evaluating their sustainability impacts for the fresh-cut lettuce and fresh-cut cantaloupe industry. For fresh-cut lettuce, these interventions involve (i) surface water treatment (using Chlorine, Peracetic acid or UV treatment) for overhead spray irrigation and (ii) transitioning from furrow/overhead spray irrigation to drip irrigation. For fresh-cut cantaloupe, the interventions include (i) moving fresh-cut cantaloupe packages from open refrigeration to closed refrigeration in retail stores and (ii) improving refrigeration during retail (reduced temperature). We are conducting a detailed evaluation of the sustainability implications across three key dimensions: economic, ecological, and social. In each dimension, we are quantifying specific factors (in US dollars) directly affected by each intervention, including private and public costs and benefits for the US fresh-cut lettuce and fresh-cut cantaloupe industry. These factors include the annual cost of water usage (drip irrigation compared to other systems; used in fresh-cut lettuce, intervention (i)) or the annual cost of electricity use (closed versus open refrigeration; used in fresh-cut cantaloupe, intervention (i)), among others. Goal 2. Differences in methodology (e.g., sample volume, detection method) have been shown to affect microbial water quality results, but research is needed to determine how these differences impact the comparability of findings to generate best management practices, and the ability to perform meta-analyses. Here, we address this knowledge gap by compiling and analyzing a dataset representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence, E. coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quantified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic E. coli, Salmonella, and Listeria, suggesting that our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological differences between studies (e.g., only 13% of the total variance in the likelihood of Salmonella detection was uniquely attributed to non-methodological factors), highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies. In this goal, we also evaluated the prevalence of Salmonella and L. monocytogenes (Lm) and the concentration of indicator bacteria (total coliforms, generic E. coli) in agricultural soils, and characterized associations between soil properties and each microbial target in Virginia. Three produce farms, representing different regions and soil types, were sampled four times over one year (October 2021-November 2022). For each individual farm visit, composite soil samples were collected from 20 sample sites per farm per visit for microbial and nutrient analysis (n=240). Samples (25g) were processed for Listeria spp. and Salmonella using a modified FDA BAM method; samples (5g) were enumerated for generic E. coli and total coliforms using Petrifilm. Presumptive Listeria spp. and Salmonella isolates were confirmed by PCR. Soil nutrients from each sample were tested and evaluated for their association with each microbial target. Salmonella, generic E. coli, Listeria spp., and Lm prevalence was 4.2% (10/240), 44.6% (107/240), 10% (24/240) and 2.5% (6/240), respectively. The average concentration of generic E. coli in positive samples was 1.5±0.8 log10 CFU/g. Soil pH was positively associated with Lm [Odds Ratio (OR)=5.5] and generic E. coli (OR=4.9) prevalence; there was no association between Salmonella prevalence and any evaluated factor. Results show that pathogen prevalence was relatively low in unamended soils, and that factors influencing prevalence and concentration varied by microbial target and farm. We also evaluated the prevalence ofSalmonellaand the prevalence and concentration of genericE. coliin Florida's agricultural soils was evaluated to understand the potential risk of microbial contamination at the pre-harvest level. A longitudinal field study was performed in three agricultural areas across Florida. At each location, 20 unique 5x5 m field sampling sites were selected, soil was collected and evaluated forSalmonellapresence (25g) andE. coliand coliform concentrations (5g). Complementary data including weather, adjacent land-use, soil properties, and field management practices were collected from October 2021 to April 2022. The overallSalmonellaand genericE. coliprevalence was 0.4% (1/239) and 11.3% (27/239), respectively; with meanE. coliconcentrations in positive samples of 1.56 log CFU/g. A significant relationship (p<0.05) was observed between genericE. coliand coliforms, and farm and sampling trip. GenericE. coliwas detected in Florida soils throughout the duration of the growing season meaning activities that limit contact between soil and horticultural crops should continue to be emphasized. Goal 3. An agent-based model for the produce section of the retail store has been completed, and the predicted outputs were evaluated using different analyses such as (i) sensitivity analysis, (ii) cluster analysis, and (iii) scenario analysis. Goal 4. To ensure that existing models for inclusion in the virtual library are identified, we first conducted a rapid scoping review of peer-reviewed articles published over the last 10 years by searching the databases PubMed, Web of Science, AGRICOLA, CAB Abstracts, Medline, and FSTA. The search resulted in 6,110 distinct peer-reviewed articles. After title/abstract screening, 215 articles remained. A full-text screening reduced the number of relevant papers to 52. The most common reason for excluding articles was a lack of access to code or a link to the model. For the identified 52 articles, we designed a website to serve as a virtual library of food safety models and computational decision-support tools, including apps. The website is hosted on the www page of the Cornell Institute for Food Safety (https://cals.cornell.edu/institute-for-food-safety). The preliminary virtual library website is organized into multiple tabs based on the microorganism for which the model was intended. Goal 5. The "Introduction to Food Microbiology", "Food Code" and "Additional Strategies to Prevent Contamination" courses have been completed, and await feedback from the Advisory Council (AC). The "Root Cause Analysis" course content has been developed and is approximately 60% complete; it needs to be completed with specific produce specific examples after receiving feedback from the AC. Goal 6. This goal was finalized in the previous reporting period (2021-2022).
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Cook, C., D.L. Weller, C.M. Murphy, C. Diekman, A.M. Hamilton, M. Ponder, R.R. Boyer, S.L. Rideout, R.O. Maguire, M.D. Danyluk, and L.K. Strawn. 2023. Factors Associated with Foodborne Pathogens and Fecal Indicator Organisms in Virginia Agricultural Soils. Frontiers in Sustainable Food Systems.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Murphy, C.M., D.L. Weller, R. Ovissipour, R. Boyer, and L.K. Strawn. 2023. Spatial Versus Non-Spatial Variance in Fecal Indicator Bacteria Differs Within and Between Ponds. Journal of Food Protection. 86 (3), 100045.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Murphy, C.M., Weller, D.L. & Strawn, L.K., 2023. Scale and detection method impacted Salmonella prevalence and diversity in ponds. Science of The Total Environment, p.167812.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Weller, D.L., Love, T.M., Weller, D.E., Murphy, C.M., & Strawn, L.K. 2023. Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data. Journal of Applied Microbiology. 134(10).
- Type:
Journal Articles
Status:
Submitted
Year Published:
2024
Citation:
Weller, D.L., C.M. Murphy, T.M.T. Love, M.D. Danyluk, and L.K. Strawn. Methodological Differences Between Studies Confound One-Size Fits All Approaches to Managing Surface Waterways for Food and Water Safety. Applied and Environmental Microbiology.
- Type:
Journal Articles
Status:
Submitted
Year Published:
2024
Citation:
Diekman, C.M., C. Cook, R.W. Worobo, L.K. Strawn, and M.D. Danyluk. Factors Associated with the Prevalence of Salmonella, Generic Escherichia coli, and Coliforms in Floridas Agricultural Soils. Journal of Food Protection.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Ruser, S. Scoping Review to Assess the Ecology of Foodborne Infectious Agents and the State of the Digitally Enabled Food Safety. Ecology & Evolutionary Biology Graduate Student Symposium, Cornell University, Ithaca, NY. December 8 - 9, 2022. Oral presentation.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Ruser, S., Kalunga, L., Grover, L., Ivanek, R. Scoping Review to Assess the State of the Digitally Enabled Food Safety. Sigma Xi Undergraduate Research Symposium, Virtual. April 14-30, 2023. Virtual Slide Show and Personal Video.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
4. Ivanek, R. The Role of Modeling and Risk Assessment in Controlling Outbreaks The National Academies of Sciences, Engineering, and Medicine; Food and Nutrition Board Summer Meeting: Open Session on New and Emerging Technologies in Food Safety , June 23, 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Diekman, C., and M.D. Danyluk. 2023. Investigating the prevalence of Salmonella and E. coli in Floridas soil and identifying key environmental factors. 11th Annual UF/IFAS CREC Research Symposium. P. 19.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Diekman, C., and M.D. Danyluk. 2023. Investigating the prevalence of Salmonella and E. coli in Floridas soil and identifying key environmental factors. 11th Annual UF/IFAS CREC Research Symposium.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Weller, D.L., C.M. Murphy, T. Love, M.D. Danyluk, and L.K. Strawn. 2023. Methodological differences cofound one-size fits all approaches to agricultural water management. International Association of Food Protection Annual Meeting, Toronto, ON. Canada.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Murphy, C. C., Weller, D. L. and Strawn, L. K. 2023. Not All Ponds are Created Equal: Factors Associated with Salmonella Contamination Varies by Pond and Detection Method. International Association for Food Protection 2023 Annual Conference.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Cook, C., Murphy, C.M, Weller, D. L, Ponder, M., Boyer, R. R., Rideout, S., Maguire, R. O., Strawn, L. K. 2023. Soil Nutrient Levels Associated with Salmonella Prevalence and Escherichia coli and Total Coliform Concentrations on Produce Farms. International Association for Food Protection 2023 Annual Conference.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Y. Jung, C. Qian, C. Barnett-Neefs, R. Ivanek, and M. Wiedmann. Validating Agent-Based Model (ABM) that Predicts Listeria spp. Prevalence on Environmental Surfaces in a Retail Store. 2023 International Association for Food Protection Annual Meeting. July 16-19, 2023, Toronto, Ontario, Canada.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Y. Jung, C. Qian, C. Barnett-Neefs, R. Ivanek, and M. Wiedmann. Validating an Agent-Based Model (ABM) that Predicts Listeria spp. Prevalence in a Retail Store. 2023. AI Institute for Next Generation Food Systems open house. Sept 12-13, 2023, Davis, CA (poster).
- Type:
Websites
Status:
Under Review
Year Published:
2023
Citation:
The website Virtual Library of Food Safety Models , hosted within the www page of the Cornell Institute for Food Safety ( https://cals.cornell.edu/institute-for-food-safety , is currently open for internal use by the project team)
|
Progress 09/01/21 to 08/31/22
Outputs
Target Audience:The key beneficiaries and target audiences for this project are fresh produce growers and processors, and developers of modeling technologies for use in produce food safety. The project will provide data to help industry to implement sustainable mitigation strategies that are cost-effective, environmentally friendly, and socially acceptable in reducing public health risks, which will benefit the consumers as well. This project also specifically targets produce food safety and quality assurance professionals in industry, academia, and government. Goal 5, which focuses on development of comprehensive produce safety extension programs in addition targets retailers and educators. Finally, Goal 6 ("Develop graduate and undergraduate teaching modules to train students to use and develop computational and modelling tools for produce safety") also targets students interested in digital produce food safety. Changes/Problems:For Goal 2, the number of Salmonella and Listeria identified in soils was significantly lower than anticipated. This will limit the amount of model work that can be completed. While no major overall changes have been made, initial project delays due to COVID have carried over into subsequent years, which will mean that we will likely need to request a no-cost extension. What opportunities for training and professional development has the project provided?Goal 1. Two postdocs, Drs. Ece Bulut and Sarah I. Murphy have continued training to develop quantitative microbial risk assessment models. Master of Public Health (MPH) student (Linda Kalunga) received training in agent-based modeling applied to food safety. ?Goal 2. This project goal has provided training opportunities in GIS and R based modeling tools for a PhD student (C. Murphy) and MS students (C. Diekman, C. Cook) who were supported on this grant. A. Hamilton, a postdoc, assisted in data cleaning, and was provided training on tidy data, and data structure. Additionally, both students have received basic training in bacterial enumeration and enrichment techniques. Goal 3. This goal has trained two researchers (Genevieve Sullivan and Cecil Barnett-Neefs), which have both graduated (with a PhD and MS, respectively); two more PhD students (Luke Qian and YeonJin Jung are currently being trained on modeling as part of this project component. Goal 4. An undergraduate student (Sophia J. Ruser) and a high school student (Lily Grover) have been trained in food safety and in conducting a scoping review. Goal 5. Two graduate students, Samantha Bolten and Jonathan Sogin, continued training to develop courses for industry personnel and continued to develop connections with industry leaders available to them via the Advisory Council. Goal 6. Two PhD students at Cornell (Luke Qian and Samantha Bolten) were actively involved in module development. In addition, a researcher (Cecil Barnett-Neefs) has been trained in the development of video-based educational materials. Finally, one student at Virginia Tech (C. Murphy) helped in one of the undergraduate-level produce food safety module discussion sessions. How have the results been disseminated to communities of interest?For all goals, preliminary results have been shared among the members of the external Advisory Council (comprised of representatives of the produce industry - growers, processors and distributors, trade and research organizations, and government agencies). In addition, a number of preliminary findings have been communicated through abstracts and talk presented at scientific meetings (see below for a complete listing). In 2022, this project also has contributed four peer-reviewed publications. In addition, developed videos have been shared on YouTube (combined about 100 views thus far) and with members of the members of the Artificial Intelligence Institute for Next Generation Food Systems and teaching modules are shared on the project www site. What do you plan to do during the next reporting period to accomplish the goals?Goal 1. Over the next reporting period, we will finalize manuscripts for the developed QMRA models for EC and LM. Next, we will use predictions from the QMRA models about the risk reduction associated with certain mitigation strategies, as well as information from literature and expert opinion elicitation, to evaluate the indicators of food safety sustainability (social, economic, and environmental) for those mitigation strategies. Goal 2. Over the next reporting period, we will submit manuscripts on the water quality meta-data effort for each microbial target, and method variation analyses for water, and for soil surveys in VA and FL. We will also develop predictive models with different levels of complexity (allowing for use by stakeholders with different levels of expertise); these efforts will initially focus on Listeria predictive models (as methodological signals are not confounding environmental/other factors). Goal 3. We plan to complete the Agent-based model for produce section of retail environments and evaluate it for prediction of the effectiveness of different Listeria control strategies. ?Goal 4. We will complete the scoping review of digitally enabled models and modeling tools. We also expect to create and apply a web-based survey to assess produce industry practices regarding the use of modeling tools and barriers to using them. Goal 5. We plan to hire a postdoctoral researcher that will be dedicated to this project, the modules will be completed and beta tested with produce safety experts from industry and extension. Goal 6. We plan to complete the graduate students modules for produce safety and digital produce safety and subsequently launch a communication campaign to encourage adoption and use of the teaching modules developed as part of this project.
Impacts
What was accomplished under these goals?
Goal 1.We are finalizing papers describing quantitative microbial risk assessments (QMRA) for two produce-pathogen pairs: (i) Escherichia coli O157:H7 and fresh-cut lettuce (EC) and (ii) Listeria monocytogenes and fresh-cut cantaloupe (LM). Both models are aimed to evaluate the human health risk from consumption of contaminated lettuce/cantaloupe. QMRA for EC: We revised the pre-harvest module of the QMRA to better represent the field contamination of lettuce via four contamination sources (i.e., contamination via irrigation water, animal feces from field application of manure, wildlife intrusion and runoff from adjacent cattle farm). This included changes in the conceptual model, repurposing parameters/variables used in the model and revalidating the pre-harvest module. In addition, we included parameters and variables to develop alternative scenarios to the baseline model, including variables/parameters related to furrow and drip irrigation and cattle vaccination for pre-harvest module, as well as chlorine wash alternatives and consumer wash in post-harvest module. Preliminary results indicate the importance of the irrigation system used (spray, drip or furrow) in determining the food safety risk associated with consumption of lettuce. QMRA for LM: We expanded the QMRA model to include (i) option of cantaloupe following either a scheme of field to packinghouse to fresh-cut facility OR field to cooling facility to fresh-cut facility, (ii) option of equipment cleaning and/or sanitation at the packinghouse and fresh-cut facility, and (iii) cross-contamination at the packinghouse and fresh-cut facility. We extracted data from the literature for model validation and prepared a set of scenarios for assessment based on input from the project advisory council as well as literature review. Preliminary sensitivity analysis identified that time and temperature conditions during distribution, retail, and home storage, and the initial number of LM cells on the total cantaloupe surface area in a bin had the greatest impacts on LM per contaminated fresh-cut cantaloupe serving. Scenario analysis showed that the relative degree of clustering affects the risk of listeriosis per serving and the predicted number of illnesses and deaths. Assessment of interventions demonstrated that reducing temperature and/or time conditions post-processing can be an effective strategy for reducing LM risk. Goal 2 includes efforts related to both water and soil related pathogen sources. For water related pathogen sources, a 2,429,990 sample dataset representing >500 studies was compiled; each sample had data on at least one microbial water quality (MWQ) target (e.g., Salmonella presence, fecal indicator bacteria [FIB] levels). Variance partitioning analysis showed that environmental and methodological signals could not be disentangled for E. coli (EC), Salmonella, and Listeria, limiting our current understanding of and ability to base guidance on foodborne pathogen ecology in freshwater systems. For example, 18% of variance in Salmonella detection was jointly attributable to methodological and non-methodological factors. Despite this, opportunities for site or waterway-specific management exist because substantial variance in MWQ was still uniquely attributable to environmental factors (e.g., 13% for Salmonella). In contrast, FIB levels were more strongly associated with environmental, as opposed to methodological factors by condition forest. Our findings suggest that metadata collection and management should be standardized across studies (min set of attributes), and comparability data is needed to develop water quality risk assessments. With regard to soil related pathogen sources, the prevalence of EC,Salmonella and Listeria spp., and the concentration of EC and total coliform bacteria in Florida and Virginia agricultural soils was evaluated to understand the risk of microbial contamination at the pre-harvest level. A longitudinal field study was performed in each state consisting of three geographically distributed agricultural areas across each state. At each location, 20 unique 5x5 m field blocks were selected, soil was collected and evaluated for Salmonella and Listeria spp. presence (25g) and ECand total coliform bacteria enumeration (5g). Complementary data included weather, adjacent land-use, soil micro-nutrients, and field management practices from October 2021 to April 2022 in Florida and October 2021 to October 2022 in Virginia. Analyses are ongoing in Virginia, as sampling just concluded. In Florida, the overall prevalence of Salmonella and EC were 0.418% (1/239) and 11.3% (27/239), respectively; mean EC population levels in positive samples were 1.56 log CFU/g. Investigation of the relationship between farm and sampling trip showed that both farm and sampling trip were significant (p<0.05) for ECand coliforms and between farm and sampling trip significant (p<0.05) for coliforms only. For example, in Florida, variation in the prevalence of ECand changes in coliform populations between farms suggests the factors influencing the soil environment at the three farms are different. While Salmonella was only identified once, ECwas detected in Florida soils throughout the duration of the growing season meaning activities that limit contact between soil and crops should continue to be emphasized. Goal 3.We published two papers in 2022, which detail development and use of Agent-Based models (ABMs) to characterize Listeria transmission and control strategies in packing houses. In addition, we have developed an agent-based model on Listeria transmission in retail environments; we currently finalizing the parameter estimates for this model. Initial model runs suggested that limiting the contamination from the incoming supply and improving the cleaning schedule can be effective in reducing Listeria contamination in retail environments. Goal 4.We are conducting a scoping review to identify, summarize and assess digitally enabled models and modeling tools applicable to food safety. We developed a literature search strategy to identify the peer-reviewed articles describing food safety modeling tools by searching across six databases (PubMed, Medline, Web of Science, FSTA, CAB Abstracts and AGRICOLA). To be eligible, a study needs to be published in a peer-reviewed journal within the last 10 years, describe primary research, be written in English, and be about reusable modeling tools developed for various foodborne pathogens or viruses found in food/beverage or agricultural matrices. We first conducted a pilot scoping review that identified a total of 687 articles, screening identified 34 studies eligible for data extraction. The results of the pilot in terms of the identified and missed studies were used to improve the search algorithm and re-run the search. This resulted in the identification of 6,110 studies for screening. A total of 2,358 duplicates were removed, and the remaining articles are currently being screened by title and abstract against the eligibility criteria. Goal 5. The prerequisite courses (Introduction to Food Microbiology, Systems Approach to Produce Safety, Produce Traceability, and Root Cause Analysis Methods) have been prioritized for completion.Introduction to Food Microbiology content is complete but must be finalized with produce specific visual aids and produce specific examples.Produce Traceability and Systems Approach to Produce Safety are approximately 50% of the content developed.Feedback from the Advisory Council was incorporated into the materials that were generated up to this point. Goal 6.We have modules on produce safety (3 lectures) and digital produce safety (2 lectures) on our project www site (https://cals.cornell.edu/produce-safety-coe/training/teaching-modules). We have also developed a lecture and module on food systems digital twins for graduate and undergraduate students as well as industry, which have been posted on YouTube.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Barnett-Neefs, C.W., Sullivan, G., Zoellner, C., Wiedmann, M., Ivanek, R. Using Agent-Based Modeling to compare corrective actions for Listeria contamination in produce packinghouses. PLOS ONE, 2022. https://doi.org/10.1371/journal.pone.0265251
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Barnett-Neefs, C., Wiedmann, M., Ivanek, R. Examining Patterns of Persistent Listeria Contamination in Packinghouses using Agent-Based Models. Journal of Food Protection 2022 Dec 1;85(12):1824-1841, doi: 10.4315/JFP-22-119. https://pubmed.ncbi.nlm.nih.gov/36041081/
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Ivanek R., Acharya J., Wiedmann M., Zhao Q., Zohdi T., Nitin N., Adalja A., Smith A., Kalunga L. Digital twin models and integrated AI for food safety across the supply chain. AIFS site visit, UC Davis, Sep 20, 2022. Poster presentation
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Ivanek, R. In Silico Models for Design and Optimization of Science-Based Listeria Environmental Monitoring Programs in Fresh-Cut Produce Facilities. Invited talk at ASM Microbe 2022, June 11, 2022
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Bulut E, Murphy S.I., Strawn L.K., Danyluk M., Wiedmann M. and Ivanek R. Identifying preharvest risk reduction strategies for Escherichia coli O157:H7 on fresh-cut lettuce in the US industry through application of QMRA. Society for Risk Analysis (SRA) 2021 Annual Meeting, Dec 5-9, 2021, Virtual:SRA.org. Poster presentation
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Murphy S.I., Bulut E., Strawn L.K., Danyluk M., Wiedmann M. and Ivanek R. Utilizing QMRA to identify postharvest risk reduction strategies for Listeria monocytogenes on fresh-cut cantaloupe in the US industry. Society for Risk Analysis (SRA) 2021 Annual Meeting, Dec 5-9, 2021, Virtual:SRA.org. Poster presentation
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Ruser, S. Scoping Review to Assess the Ecology of Foodborne Infectious Agents and the State of the Digitally Enabled Food Safety. Ecology & Evolutionary Biology Graduate Student Symposium, Cornell University, Ithaca, NY. Oral presentation.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Diekman, C., and M.D. Danyluk. 2022. Investigating the Prevalence of Salmonella and E. coli in Floridas Soil and Identifying Key Environmental Factors. International Association for Food Protection Annual Meeting, Pittsburgh, PA. P1-131
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Cook, C., M.A. Ponder, C.M. Murphy, A. Hamilton, R.R. Boyer, S. Rideout, R. Maguire, and L.K. Strawn. 2022. Associations between Soil Nutrient Levels with Escherichia coli and Total Coliform Concentrations and Listeria and Salmonella Prevalence. International Association for Food Protection Annual Meeting, Pittsburgh, PA. P1-61
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Murphy, C., D. Weller, L.K. Strawn. 2022. Spatial Versus Non-Spatial Variance in E. coli Levels Differs by Scale of Analysis in Virginia Ponds. International Association for Food Protection Annual Meeting, Pittsburgh, PA. P1-63.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Jung, Y., Qian, C., Barnett-Neefs, C., and Wiedmann, M. 2022. Developing an Agent-Based Model to Assess Listeria Control Strategies in Retail Stores. International Association for Food Protection Annual Meeting, Pittsburgh, PA. P3-136
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Weller, D.W., C. Murphy, D. Danyluk, L.K. Strawn. 2022. Agricultural Water Quality for Produce: Recent Advances, Current Challenges, and Future Opportunities. International Association for Food Protection Annual Meeting, Pittsburgh, PA. S29.
|
Progress 09/01/20 to 08/31/21
Outputs
Target Audience:Goal 1. The key beneficiaries will be fresh produce growers and processors, who will be better able to mitigate E. coli O157:H7 or L. monocytogenes contamination of fresh produce. This project specifically provide data for identification of potential contamination pathways for pathogens entering farm-to-fork food production chain. The project also provide data to help industry to implement sustainable mitigation strategies that are cost-effective, environmentally friendly and socially acceptable in reducing public health risks. Thus, the sustainable and safe produce supply will benefit the consumers as well as the society as a whole. Goal 2. This goal will directly benefit produce growers by reducing the risk of field contamination by pathogens through agriculture water. In addition, this goal will also indirectly benefit processing facilities as fewer pathogens will be introduced into these facilities through the raw material, and consumers, who will benefit from having safer produce. Goal 3. Target audiences will be processors, packing houses, and retail food safety managers; these groups are intended users of the agent-based models developed as part of this goal. Goal 4. Target audiences will be produce food safety and QA professionals in industry, academia, and government. Goal 5. No change from the prior reporting period. The beneficiaries of the extension and outreach programs are stakeholder groups across the produce production, processing, and distribution continuum, including (i) growers, (ii) processors and packing house operators, (iii) retailers, and (iv) educators. Students that participate in the project are additional beneficiaries. Goal 6. Academic educators, undergraduate and graduate students focusing on produce food safety will directly benefit from the products of this goal as we will develop modules that educators can use to teach undergraduate and graduate students advanced topics related to produce safety and statistical modelling in the context of produce safety. Indirectly, the fresh produce industry will also benefit as they will be able to hire new personnel that are better trained to tackle real world issues in produce safety and using modeling tools to enhance produce safety. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Goal 1. Two postdocs, Drs. Ece Bulut and Sarah Murphy have been trained in setup of a framework for sustainability assessment, framing of the risk questions and the development of farm-to-fork conceptual models. Additionally, an undergraduate student (Hannah Rosenthal) has been trained in produce food safety research, focusing on the consumer perceptions, beliefs and behaviors. Goal 2. This project goal has provided training opportunities in GIS and R based modeling tools for a PhD student (C. Murphy) and MS student (C. Diekman) who were supported on this grant. Additionally, both students have received basic training in bacterial enumeration and enrichment techniques. C. Murphy helped in one of the undergraduate-level produce food safety module discussion sessions. Goal 3. As part of this goal, we trained 3 graduate students as well as one undergraduate student (all at Cornell) in development of agent-based models. Goal 4. Postdoc, Dr. Sarah Murphy and PhD student, Luke Qian, have received training in features of different types of models and the basics of web design. Goal 5. Three graduate students, Samantha Bolten, Yadwinder Singh Rana, and Jonathan Sogin, continued training to develop courses for industry personnel and continued to develop connections with industry leaders available to them via the Advisory Council. Goal 6. One graduate student, Samantha Bolten, was trained in developing and finalizing the three lectures in the undergraduate-level produce safety module. Several graduate students participated in the discussion-like recordings of these three lectures, including: Jonathan Sogin, Yadwinder Singh Rana, Samantha Bolten, Alexandra Belias, Luke Qian, and Claire Murphy. One postdoctoral fellow, Sophia Harrand, and one graduate student, Samantha Bolten, were responsible for creating the student knowledge assessments and the module evaluation survey for the undergraduate-level produce safety module. How have the results been disseminated to communities of interest?Overall, results to date have been communicated with the project Advisory Council, which includes 20 members; so far this group has met 3 times. Details on specific communication that occurred for the different goals are provided below. Goal 1. Preliminary results have been shared among the members of the external Advisory Council (comprised of representatives of the produce industry - growers, processors and distributors, trade and research organizations, and government agencies). Additionally, preliminary results have been shared among researchers and students in the academic circles and at a conference (Annual Meeting of the International Association of Food Protection, 2021). Goal 2. Preliminary results have been communicated to the Advisory Council, and project collaborators. Two publications were submitted and accepted in 2021 based on project efforts. Future communications and presentations are planned for International Association of Food Protection (IAFP) 2022, including submission of preliminary findings from the soil/micronutrient sampling effort. Goal 3. Results have been shared through a peer-reviewed manuscript: Sullivan G, Zoellner C, Wiedmann M, Ivanek R. 2021. In silico models for design and optimization of science-based Listeria environmental monitoring programs in freshcut produce facilities. Appl Environ Microbiol 87:e00799-21. https://doi.org/10.1128/AEM.00799-21. Goal 4. Nothing to report. Goal 5. Learning objectives and course outcomes have been communicated to the Advisory Council and project collaborators. Preliminary courses have been distributed to specific members of the Advisory Council, project collaborators, and other industry personnel. Goal 6. All virtual lectures in the undergraduate-level produce safety module were distributed to the Advisory Council to obtain feedback, and this feedback has been logged and will be used to aid in the development of future modules. The undergraduate-level produce safety module (https://foodsafety.foodscience.cornell.edu/produce-safety-coe/teaching-modules/) was provided to summer graduate student interns at iFood Decisions and piloted in an experiential learning program at Virginia Polytechnic Institute and State University in June-August of 2021. Lectures were also made available through YouTube (https://www.youtube.com/channel/UCCmWOQGV7hh6OwjoDJ4GtDw/videos) on the Produce Safety Center of Excellence channel. What do you plan to do during the next reporting period to accomplish the goals?Goal 1. Over the next reporting period, we will complete the quantitative microbial risk assessment (QMRA) models for the E. coli O157:H7-lettuce and L. monocytogenes-cantaloupe commodity-pathogen pairs to identify promising mitigation strategies that reduce the human health risk and start evaluating the indicators of food safety sustainability (social, economic and environmental) for those mitigation strategies. Goal 2. Over the next reporting period, we will continue our analyses and draft manuscripts of the water quality meta-data effort for each microbial target, and method variation analyses. We will also continue to develop models based on different levels of complexity to the produce industry; including, first level: temperature, (air/water), rainfall, other characteristics (e.g., water type); second level: first level plus physiochemical parameters (e.g., dissolved oxygen); third level: first and second level plus microbial test data; four level: first, second and third levels plus land-use data. These models range in the levels of difficulty for the stakeholders, for example, it is fairly easy to obtain weather data from public weather stations (first level), to increasing difficulty to monitor and obtain land-use data from GIS software. We will also continue the soil sampling/micronutrients efforts. Additionally, progress has been made on our goal of investigating pathogen/indicator organism associations with soil micro-nutrients. Sampling efforts are underway on three produce farms in each state, Florida and Virginia. Within each produce farm, twenty 5 m by 5 m plots are randomly selected and five soil samples collected per plot (n=100 soil samples). Soil will be tested using standard methods for E. coli and total coliform concentration, and Salmonella and Listeria presence (only Salmonella in FL); as well as, >20 different micronutrients (similar to Liao et al., 2021; Nature Microbiology). The study will run for one year with longitudinal sampling performed during the produce production season at four different times. Goal 3. We will continue with development of an ABM for retail produce sections in order to model Listeria transmission and develop optimal Listeria sampling strategies and intervention plans. Goal 4. We will start collating existing models and modelling tools in the area of food safety. This may involve some form of a systematic/scoping literature review but we also anticipate that literature identified in Goal 1 will be valuable for Goal 4 as well. We also expect to create and apply a web-based survey to assess produce industry practices regarding the use of modeling tools and barriers to using them. Goal 5. Over the next reporting period, we will finalize the courses that have been started already and will begin to develop those that have not been developed yet. The basic introductory produce safety courses that all participants take will be prioritized for completion first. The subsequent specialized courses for the 4 tracks will be developed next, and finally the advanced courses. The courses will be shared with members of the advisory council, project collaborators and small test groups of industry personnel to solicit feedback. The finalized courses will be made available to the public through the established Produce Safety Center of Excellence https://foodsafety.foodscience.cornell.edu/produce-safety-coe/. Goal 6. During the next reporting period, we will finalize the graduate-level produce safety module, and plan to pilot this module in academic institutions as we did with the undergraduate-level produce safety module. In addition, we expect to have the undergraduate-level statistical modeling module finalized by the next reporting period.
Impacts
What was accomplished under these goals?
Goal 1. We have continued the work on the quantitative microbial risk assessments (QMRA) for two produce-pathogen pairs: (i) Escherichia coli O157:H7 and fresh-cut lettuce (EC) and (ii) Listeria monocytogenes and fresh-cut cantaloupe (LM). The exposure assessment evaluated the distribution of the pathogens along the whole supply chain, from pre-harvest to storage at home. Then, dose response models were applied to evaluate the human health risk from consumption of contaminated lettuce/cantaloupe. The models were developed in @Risk software and were parameterized based on published literature and expert opinion. QMRA for EC: We evaluated introduction of contamination via animal feces (from field application of manure, wildlife intrusion, and runoff from adjacent cattle farms) and irrigation at the pre-harvest. Accordingly, the model evaluated several pre-harvest interventions: (i) growing the produce in non-amended soils, (ii) eliminating runoff from adjacent dairy farms, (iii) keeping wildlife off farms and (iv) cattle vaccination. EC concentration in a single serving of fresh-cut lettuce (100 grams) was used to evaluate the impact of each intervention on the risk of human illness in the US annually. Preventing wildlife intrusion, vaccinating cattle and eliminating runoff were identified as the most effective strategies for reducing human illness cases. QMRA for LM: This model accounts for the effects of conditions during transportation and storage as well as fresh-cut preparation (chlorine washing and cutting). The probability of listeriosis was estimated from consumption of a LM-contaminated fresh-cut cantaloupe serving (134 g). The preliminary QMRA model estimated a median of 3.03 log10 CFU of LM (5th, 95th, 99th percentiles: 0.90, 7.18, 9.91) per contaminated serving of fresh-cut cantaloupe at consumption. Notably, the probability of ≥1 death annually attributed to fresh-cut cantaloupe was 12.1%. Sensitivity analysis identified that time and temperature after packaging (retail, distribution, home storage), and the initial concentration of LM on whole cantaloupes at point-of-harvest had the greatest impacts on occurrence of ≥1 death annually attributed to fresh-cut cantaloupe. Goal 2. We conducted a search of available datasets (both published and un-published) on the concentration of indicator organisms and presence of pathogens in surface water surfaces in the United States, and North America, to characterize and identify spatio-temporal factors (e.g., soil properties, meteorological events, and adjacent land-use), and build models to predict pathogen presence (e.g., Listeria monocytogenes, Salmonella), and generic Escherichia coli levels. Raw data was collected from 96 candidate datasets and represented all US states, 3 Canadian provinces, and an additional dataset from Mexico yielding approximately 300,000 individual data points. All data was organized in Excel for importation into the statistical database R (to allow ease of storage, analysis and visualization). Additionally, R code was developed to mine adjacent land-use buffered at 122, 366, and 1098 meters from the sampled GPS location using the National Land Cover Database (NLCD). This was exacted for all data with GPS points (approximately 1/3). Historical weather data was also pulled from the nearest weather station using the date the sample data point was collected. Two publications have results from efforts. Goal 3. For this goal, we have completed Agent-based models (ABMs) for fresh cut facilities, which has been published. More specifically, two ABMs of fresh-cut produce facilities were developed as a way to simulate the dynamics of Listeria by modeling the different surfaces of equipment and employees in a facility as agents. We also developed ABMs of Listeria contamination dynamics for two produce packinghouse facilities; models were parameterized using observations, values from published literature and expert opinion. Once validated with historical data from Listeria environmental sampling, each model's baseline conditions were investigated and used to determine the effectiveness of corrective actions in reducing prevalence of agents contaminated with Listeria and concentration of Listeria on contaminated agents. Corrective actions targeting Listeria introduced in the facility with raw materials, implementing risk-based cleaning and sanitation, and modifying equipment connectivity were shown to be most effective in reducing Listeria contamination prevalence. Overall, our results suggest that a well-designed cleaning and sanitation schedule, coupled with good manufacturing practices can be effective in controlling contamination, even if incoming Listeria spp. on raw materials cannot be reduced. We also initiated development of an ABM for retail produce sections in order to model Listeria transmission and develop optimal Listeria sampling strategies and intervention plans. Goal 4. Initial steps were made on (i) preparation of the library of models and modelling tools and (ii) design of the model library website. Briefly, a preliminary list of existing models and an initial design for the model library website were prepared. Feedback was obtained from the project Advisory Council. Goal 5. Work continued on the development of a national produce safety certificate program that provides training and certification to individuals associated with all segments of the produce industry. The previous reporting period established a preliminary curriculum consisting of 4 specialized certificate tracks; these tracks remain unchanged. After several discussions with the Advisory Council and project collaborators, changes to the preliminary curriculum were made. An additional course, Root Cause Analysis Methods, was added to the basic produce safety courses. The Produce Safety Alliance Grower Training was added as an alternative to GAPs training for the produce grower tract. Food Code training was combined with Additional Strategies to Prevent Contamination for the produce retailer tract. Two additional courses, Product Sampling Strategies, and Food Safety Communication and Enterprise Risk Managements, were added to the Advanced course offerings. In addition to curriculum development, learning objectives and course outcomes were established for each of the courses in the curriculum. Significant progress on the development of four courses, Systems Approach to Produce Safety, Produce Traceability, Food Code and Additional Strategies to Prevent Contamination (retail tract), and Environmental Monitoring, was made. Feedback from individuals of the Advisory Council and project collaborators was solicited and integrated for these preliminary-stage courses. Goal 6. Progress was made on development of the produce safety and modeling modules proposed under goal 6. First, the undergraduate-level produce safety module (which includes three ~45-minute lectures recorded using a discussion-like format) has been completed. The undergraduate-level produce safety module was piloted in an experiential learning program at Virginia Polytechnic Institute and State University in June-August of 2021. For knowledge assessment of learning outcomes for the undergraduate-level produce safety module, pre- and post-assessments were created and provided to students who participated in the experiential learning program. We also obtained student feedback by having students fill out evaluation surveys after completion of the module. The surveys indicated that students enjoyed the use of real-life examples of outbreaks to help teach principles of produce safety and outbreak investigation. In addition, learning outcomes were defined for the undergraduate-level and graduate-level modules focused on teaching principles of modeling in the context of produce safety. The first framework of the undergraduate-level modeling module is currently in preparation.
Publications
- Type:
Journal Articles
Status:
Accepted
Year Published:
2021
Citation:
D.L. Weller, C. Murphy, S. Johnson, H. Green, E. Michalenko, T. Love, and L.K. Strawn. 2021. Land-use, Weather, and Water Quality Factors are Associated with Fecal Contamination of Northeastern Streams that Span an Urban-Rural Gradient. Frontiers in Water. Accepted November 15, 2021.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Sullivan G, Zoellner C, Wiedmann M, Ivanek R. 2021. In silico models for design and optimization of science-based Listeria environmental monitoring programs in freshcut produce facilities. Appl Environ Microbiol 87:e00799-21. https://doi.org/10.1128/AEM.00799-21.
- Type:
Websites
Status:
Published
Year Published:
2021
Citation:
The model algorithm (code) for the packing house agent-based models was written in the open source program NetLogo 6.1.1 and is available on GitHub (https://github.com/IvanekLab/CPS_2019_OpenData).
- Type:
Websites
Status:
Published
Year Published:
2021
Citation:
Undergraduate-level produce food safety module: https://foodsafety.foodscience.cornell.edu/produce-safety-coe/teaching-modules/
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2021
Citation:
Oral presentation by Dr. Bulut: E Bulut, S I Murphy, L K Strawn, M Danyluk, M Wiedmann and R Ivanek. A farm-to-fork quantitative microbial risk assessment model for Escherichia coli O157:H7 on fresh-cut lettuce. International Association for Food Protection (IAFP) Annual Meeting, Jul 18-21, 2021, Phoenix AZ.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2021
Citation:
Oral presentation by Dr. Murphy: S I Murphy, E Bulut, L K Strawn, M Danyluk, M Wiedmann and R Ivanek. Farm-to-consumer quantitative risk assessment model for Listeria monocytogenes on fresh-cut cantaloupe. International Association for Food Protection (IAFP) Annual Meeting, Jul 18-21, 2021, Phoenix AZ.
- Type:
Journal Articles
Status:
Accepted
Year Published:
2021
Citation:
Murphy, C.M., L.K. Strawn, T.K. Chapin, R. McEgan, S. Reddy, L. Friedrich, L. Goodridge, D.L. Weller, K. Schneider, and M.D. Danyluk. 2021. Factors associated with E. coli levels in and Salmonella contamination of agricultural water differed between North and South Florida waterways. Frontiers in Water. Accepted November 16, 2021.
|
Progress 09/01/19 to 08/31/20
Outputs
Target Audience:Goal 1. The key beneficiaries will be fresh produce growers and processors, who will be better able to mitigate E. coli O157:H7 or L. monocytogenes contamination of fresh produce. This project specifically provide data for identification of potential contamination pathways for pathogens entering farm-to-fork food production chain. The project also provide data to help industry to implement sustainable mitigation strategies that are cost-effective, environmentally friendly and socially acceptable in reducing public health risks. Thus, the sustainable and safe produce supply will benefit the consumers as well as the society as a whole. Goal 2. This goal will directly benefit produce growers by reducing the risk of field contamination by pathogens through agriculture water. In addition, this goal will also indirectly benefit processing facilities as fewer pathogens will be introduced into these facilities through the raw material, and consumers, who will benefit from having safer produce. Goal 3. Nothing to report. Goal 4. Nothing to report. Goal 5. The beneficiaries of the extension and outreach programs are stakeholder groups across the produce production, processing, and distribution continuum, including (i) growers, (ii) processors and packing house operators, and (iii) retailers as well as consumer outreach efforts that will also include students that participate in the project. Goal 6. Academic educators, undergraduate and graduate students will directly benefit from the products of this goal as we will develop modules that educators can use to teach undergraduate and graduate students on advanced topics related to produce food safety and statistical modelling. Indirectly, the produce food industry will also benefit as they will be able to hire new personal that are better trained in produce food safety and statistical modelling. Changes/Problems:Goal 1. Nothing to report. Goal 2. We have experienced a delay in the activities planning for this Goal due to the universities' lockdown in response to the COVID-19 pandemic. However, we do not foresee this delay to extend through the next reporting period and no change will be required in our timeline or scope. Goal 3. Nothing to report. Goal 4. Nothing to report. Goal 5. Nothing to report. Goal 6. Nothing to report. What opportunities for training and professional development has the project provided?Goal 1. Two postdocs, Drs. Ece Bulut and Sarah Murphy have been trained in setup of a framework for sustainability assessment, framing of the risk questions and the development of farm-to-fork conceptual models. Additionally, an undergraduate student (Hannah Rosenthal) has been trained in produce food safety research, focusing on the consumer perceptions, beliefs and behaviors. Goal 2. This project goal has provided training opportunities in GIS and R based modeling tools for a post-doctoral researcher, PhD student and MS student who were supported on this grant. The post-doctoral researcher was offered a position at University of Florida, which is evidence of an increased skill set, and food safety training. Goal 3. Nothing to report. Goal 4. Nothing to report Goal 5. This project has already involved two PhD graduate students in the development of the produce safety certificate program course framework. The graduate students are integrally involved in the development of the 4 specialized produce safety tracks framework and content, as well as the advanced course requirements and content. The graduate students will continue to be integrally involved in the development of the course content and materials in the future years of the grant. Goal 6. One Research Associate Sr., Dr. Renato Orsi, and one technician, Zoe Wasserlauf, were trained in developing online surveys and were responsible for creating the first framework of the undergraduate-level produce food safety module. Two graduate students, Samantha Bolten and Alexandra Belias, were trained in developing the undergraduate-level produce food safety module slides and participated in the discussion-like recording of the first lecture in this module. How have the results been disseminated to communities of interest?Overall, results to date have been communicated with the project Advisory Council, which includes 20 members; so far this group has met 3 times. Details on specific communication that occurred for the different goals are provided below. Goal 1. Preliminary results have been shared among the members of the external advisory council (comprised of representatives of the produce industry - growers, processors and distributors, trade and research organizations, and government agencies). Additionally, preliminary results have been shared among researchers and students in the academic circles. Goal 2. Preliminary results have been communicated to the Advisory Council, and project collaborators. Future communications and presentations are planned for 2021, including submission of abstracts for the International Association of Food Protection, and draft manuscript for Virginia and Florida specific models. Goal 3. Nothing to report. Goal 4. Nothing to report. Goal 5. The extension and outreach certificate program plans have been shared with the Advisory Council. Goal 6. The first lecture of the undergraduate-level produce food safety module was shared among the other PIs and PDs as well as among all members of the Advisory Council. What do you plan to do during the next reporting period to accomplish the goals?Goal 1. Over the next reporting period, we will complete farm-to-fork conceptual models for E. coli O157:H7-lettuce and L. monocytogenes-cantaloupe commodity-pathogen pairs. We also plan to start building the quantitative microbial risk assessment (QMRA) models based on the elements of the completed conceptual models for each pathogen-food pair and start evaluating the other indicators of food safety sustainability (social, economic and environmental). Goal 2. Over the next reporting period, we will continue our analyses and draft manuscripts of the Virginia and Florida datasets, and other states/regions. We will also continue to develop models based on different levels of complexity to the produce industry; including, first level: temperature, (air/water), rainfall, other characteristics (e.g., water type); second level: first level plus physiochemical parameters (e.g., dissolved oxygen); third level: first and second level plus microbial test data; four level: first, second and third levels plus land-use data. These models range in the levels of difficulty for the stakeholders, for example, it is fairly easy to obtain weather data from public weather stations (first level), to increasing difficulty to monitor and obtain land-use data from GIS software. However, we hope to provide tutorials in the outreach objective. Goal 3. Four produce processing facilities with distinct layouts and geographic locations will be selected for the development of facility-specific Agent-Based Models (ABM). Facilities already participating on other projects involving the PDs and PIs may be selected. New facilities (i.e., facilities not involved in current parallel projects) will be visited by key member on this project to ensure they are appropriate for this goal. Longitudinal microbial data will be obtained from previous sampling efforts or new longitudinal samplings will be carried out if needed. ABM will start to be developed soon after the facilities are selected and the longitudinal data are obtained. Goal 4. Identification of existing models and modelling tools will be carried out in this next reporting period. We anticipate that a high proportion of the literature being used for Goal 1 will be valuable for Goal 4 as well. We also expect to create and apply a web-based survey to assess produce industry practices regarding the use of modeling tools and barriers to using them. Goal 5. During the next reporting period, the course content for the 4 specialized produce safety tracks will be generated. The basic introductory produce safety courses that all participants will be prioritized to be completed first. The subsequent specialized courses for the 4 tracks will be developed next, and finally the advanced courses. Goal 6. Over the next reporting period, we will finalize the undergraduate-level produce food safety module and develop the other three modules planned for this goal. We expect that both the undergraduate-level and graduate level produce food-safety modules will be finalized by the end of the next period while the statistical modelling modules should be initiated by then.
Impacts
What was accomplished under these goals?
Goal 1. Progress was made on the assessment of sustainability of fresh produce food safety, specifically the assessment of the public health indicator of sustainability. Towards that goal, we are using the Modular Process Risk Model (MPRM) framework to model produce contamination prevalence and levels along the farm-to-table production chain. The models are being developed for two produce-pathogen pairs: (i) Escherichia coli O157:H7-lettuce (cut) and (ii) Listeria monocytogenes-cantaloupe (whole and cut), which were selected based on feedback from members of our external advisory council. A preliminary conceptual model was developed that outlines the risk pathway from farm-to-fork contamination chain for the E. coli O157:H7-lettuce commodity-pathogen pair. The E. coli O157:H7-lettuce model has been designed to estimate the number of E. coli O157:H7 cells per serving of chopped romaine lettuce and includes four consecutive modules: (i) growing, (ii) harvest/field packing, (iii) processing/distribution and (iv) consumer. A preliminary version of the E. coli O157:H7-lettuce conceptual model was presented to the project team and the external advisory council, and then updated according to their feedback. A sub-group was identified within the advisory council to further seek input on model design and parameterization (e.g., manure or treated biological soil amendment practices that are of specific interest to the produce industry and stakeholders). Accordingly, the main processes currently considered in the MPRM include (i) lettuce growth in the field, (ii) holding time, (iii) harvest/field packing, (iv) washing and chopping (post-harvest processing), (v) distribution, (vi) retail storage and display, (vii) transportation from retail to home, (viii) home storage, and (ix) consumer washing and preparation. Major considered sources of E.coli O157:H7 contamination in the lettuce MPRM are (i) irrigation water, (ii) soil and/or manure contamination, (iii) harvesting blades, (iv) hands of the workers and/or harvesting belts, (v) wash water (cross-contamination) and cutting equipment at post-harvest processing, (vi) and consumer washing (cross contamination) and preparation. Preliminary works has also started on the development of the conceptual model for the L. monocytogenes-cantaloupe MPRM. Goal 2. Due to COVID-19 restrictions, no sampling efforts were performed during this reporting period. To accomplish our target goal of using GIS-based modeling tools to reduce produce microbial food safety hazards in water, and environmental sources; we conducted a search of available datasets (both published and un-published) on the concentration of indicator organisms and presence of pathogens in surface water surfaces in the United States, and North America, to characterize and identify spatio-temporal factors (e.g., soil properties, meteorological events, and adjacent land-use), and build models to predict pathogen presence (e.g., Listeria monocytogenes, Salmonella), and generic Escherichia coli levels. Raw data was collected from 50 datasets and represented 22 states, 3 Canadian provinces, and an additional dataset from Mexico yielding 112,710 individual data points. All data was organized in Excel for importation into the statistical database R (to allow ease of storage, analysis and visualization). Additionally, R code was developed to mine adjacent land-use buffered at 122, 366, and 1098 meters from the sampled GPS location using the National Land Cover Database (NLCD). This was exacted for all data with GPS points (approximately 1/3). Historical weather data was also pulled from the nearest weather station using the date the sample data point was collected. For example, in Florida, 4 different datasets were used for mined, two of which are unpublished, and we performed analyses to determine associations between Salmonella presence, and indicator organisms (e.g., generic E. coli, fecal coliforms), and proximity to specific land-use categories (e.g., wetlands, cropland, forest, urban), meteorological variables (e.g., air temperature, rainfall), and physiochemical parameters (e.g., turbidity, conductivity). Similar analyses are underway for Virginia, and other states. Analyses are in progress. Goal 5. The development of a national produce safety certificate program that provides training and certification to individuals associated with all segments of the produce industry has been initiated. The courses that will make up the content for the 4 specialized certificate tracks, and the advanced certificate programs have been identified. The 4 specialized certificate tracks are Produce Growers, Produce Packing Houses & Produce Processors, Retail, and Consumer Educators. The basic produce safety courses that will be taken by all tracks include: Introduction to Food Microbiology, Systems Approach to Produce Safety, and Traceability in Produce Systems. For the Produce Growers specialized certificate, in addition to the basic produce safety courses, they will take courses that include: GAPs, and Control of Produce Contamination in the Field. For the Produce Packing Houses & Produce Processors, the courses identified for this specialized certificate will be: GMPs 21 CFR 117, PCQI or HACCP for Packing Houses, and Hygiene and Environmental monitoring. The courses for the specialized certificate for Retail will include: Food Code, and Additional Strategies for Retailers to Prevent Contamination. The Consumer Educators specialized certificate will include: Consumer Produce Safety for Educators. The advanced certificates for the 4 specialized tracks will require one advanced course after completing the course requirements for the respective specialized tracks. In addition, advanced courses for Modeling of Produce Safety and GIS Tools for Produce Safety will be developed as the respective objectives for modeling and GIS tools have been completed. Originally, the certificate program was going to be mixed model of on-line and in-person, but due to the pandemic, the certificate program will be on-line for the first 1-2 years, and may change to in-person and on-line when the pandemic subsides. As part of the industry training, we have also offered one industry webinar on WGS (live on November 24th; 50 registrants; 25 attendees), which specifically addressed the WGS data for the E. coli outbreaks and recalls that occurred during the fall of 2020. Goal 6. Progress was made on (i) assessing the needs for and (ii) initial development of the modules proposed under Goal 6. Briefly, a draft survey on needs assessment related to undergraduate and graduate-level modules to teach (i) produce food safety and (ii) statistical modelling was created and sent to three academic professors who teach food safety at three different US universities. Based on the feedback received from these three educators, a final survey was developed. The final survey had questions on format of the modules (e.g., number of lectures per module, length of each module) and as well as interest of using the modules. Educators responsible for teaching produce-related food safety lectures at 53 academic institutions across the United States were identified, contacted, and asked to complete the survey. Twenty out of the 57 educators contacted completed the survey after 22 days. We also obtained feedback from the Advisory Council during our second quarterly Advisory Council meeting, chiefly on the content of the modules. The surveys indicated that modules containing three 20-40 minutes lectures each would be ideal and most educators that responded the survey showed high interest in using the modules in their own classes, both as a slide set for in-person instruction as well as a recorded lecture for distance learning. We are currently developing of the undergraduate-level produce food-safety module. The first of the three lectures have been finalized and recorded using a discussion-like format (link).
Publications
|
|