Progress 01/01/24 to 12/31/24
Outputs Target Audience:Cross-contamination in food manufacturing plants is a significant challenge, particularly for ready-to-eat (RTE) foods. The salience of this topic can be seen in the incidence of outbreaks due to post-processing contamination; the application of molecular subtyping, which has revealed the persistence of pathogens in the manufacturing environment; and the introduction of sanitation as a preventive control within the Human Food Rule of FSMA (21 CFR 117.135). But while advancements in detection and subtyping methods have further elucidated the issue, evidence-based interventions remain underdeveloped.In processing facilities where product is not contained within packaging following the kill-step, or where no kill-step is applied, the risk of cross-contamination from persistent colonizers of the processing environment is a significant threat to food safety.This challenge cuts across food sectors and includes dairy (e.g., cheeses, ice cream), meat (e.g., deli products), fresh produce (e.g., leafy greens, fresh-cut melon), and recipe meals (e.g., frozen dinners, assembly meals, RTE sandwiches and salads).In this project, we selected dairy processing plants as model food processing environments because of their highly industrialized processing equipment and many niches, consistent processing and sanitation cycles, and well characterized product microbiological standards.However, we want to also acknowledge that many other food sectors are also served by addressing this challenge. In addition to the dairy industry, the fresh produce industry represents other important stakeholders for this work.We do not envision that the outcomes from this project nor the conceptual framework of the project are limited only to dairy processing environments. While further future work would be needed to define the identity of members of the microbiome within other operation types, we believe that integrating leaders from both the dairy and the fresh produce industry within our discussions can help drive the expansion of this work across product sector categories. Therefore, we have identified industry advisors from both fields as well as a technology company to support technology transfer to diverse audiences. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Collaborative Opportunities This project has provided a unique opportunity for collaboration in our research group.We have a team consisting of two postdocs, three Ph.D. students, and a master's student that have assisted in sample collection, processing, and analysis.Each member of our team has different expertise and background which has provided in-depth insight into the various aspects of project design, execution, and analysis.Bi-weekly meetings have been held since the beginning of the project in which issues, strategies, and suggestions for sample collection have been discussed.Meetings also include the development of individual research projects which will use this data set.We have also collaborated with a faculty member from Pennsylvania State University who will be a coauthor on a manuscript. Industry engagement for trainees A team including the PI, two PH.D. students and a postdoc have attended the sample collection trips.During these trips, everyone has had the opportunity to engage with the industry professionals at the facilities to learn about facility management, operation, and challenges. Publication Opportunities The dataset from this project will be used in at least 4 different research papers which will be published in peer-reviewed journals and presented at research conferences.We have also had the opportunity to present our work at weekly lab-group meetings in the Department of Food Science at Cornell. Mentorship This project has provided several opportunities for teaching and mentorship for two postdocs.Postdocs has taken on a significant role mentoring a master's student who is analyzing the data from this project for their thesis as well as leading and organizing biweekly meetings for the research team. How have the results been disseminated to communities of interest? The code from this project has been made publicly available on Github. We've shared updates about our microbiology results with our industry partners. Findings have been shared at meetings with the industry and scientists. The first manuscripts from this project have been published, and two more are under review. The 16S sequences collected from our project have been made publicly available on NCBI Project NumberPRJNA1075764https://www.ncbi.nlm.nih.gov/sra/PRJNA1075764 The ITS sequences collected from our project have been made publicly available on NCBI Project NumberPRJNA1123599athttps://www.ncbi.nlm.nih.gov/sra/PRJNA1123599 What do you plan to do during the next reporting period to accomplish the goals? We are completing our analysis of swab sample collection and DNA extraction methods which we anticipate submitting for publication in the next few months. We are currently working on a project that will investigate how food surface microbiomes (bacterial and fungal) interact with one another.This project will illuminate how the processing environment can influence the taxonomic and ecological characteristics of food surface microbiomes. We plan to initiate a project in the next year or so that will address the third objective from the proposal and use computational fluid dynamics to model sanitation fluid flow through the environmental niches. The first study related to this objective should be submitted for publication in the next few months.
Impacts What was accomplished under these goals?
In our previous progress report, we identified progress related to field sampling, sample processing, and DNA sequencing. We have successfully published a study comparing the bioinformatic pipelines for evaluating this data. In this progress report, we provide updates related to two analyses: (1) evaluation of ITS sequencing results and (2) evaluation of sanitation efficacy at reducing environmental pathogen, Listeria monocytogenes. The summary of findings from these two analyses are provided below: ITS Sequence Analysis The processing environment is a source of fungi that can cause spoilage in cultured dairy products.However, fungi within the built environment of dairy facilities have not been well characterized. This study explored fluctuations of fungal identity and diversity (composition and proportion) on built environment surfaces in three dairy processing facilities.We used ITS sequencing to characterize fungi from 119 environmental surfaces of three cultured dairy facilities, and from 154 spoiled cultured dairy products manufactured in those facilities. Notably,PenicilliumandMucorwere frequently found in both spoiled products and environmental surface samples across all three facilities.Other fungal genera found in both spoiled products and surfaces includedAureobasidium,Candida, Cladosporium,andYarrowia.The diversity of fungi collected fromenvironmental surfaces varied broadly (0-3.98 Shannon diversity, 0-176 Chao1 diversity) and were significantly (P< 0.01) different among Production Area (Warehouse, Raw, Filling, and Post-Pasteurization) and Season.Yeast and mold (YM) counts also varied substantially (0-8.27log10CFU/sponge) and were significantly different (P < 0.001) among Object Types (Doors, Fans/Vents, Floor Objects, and Walls/Ceilings).Furthermore, the abundance of several fungal genera related to dairy product spoilage were significantly different (P< 0.05) among Production Area, Season, Object Type, and Facility.Among some objects of the same type, the abundance of certain fungal genera varied considerably, indicating that no individualfactor could predict potential fungal resovoirs.Overall, our results suggest thatseasonal and spatial differences (i.e.,location) significantly influencethe ecology of fungal reservoirs within built environments of cultured dairy facilities.Spoilage of cultured dairy products can result in food waste and economic loss.Investigating fungal ecology within the built environment can provide insight into potential sources of product contamination. Spoilage fungi spread either directly from surfaces or when airborne spores settle in product or on surfaces.Diffusion via air is typically the predominant route for mold contamination of product while yeasts are more commonly spread from surfaces. Thus, surface swabbing may be an effective method to simultaneously sample fungi that either persist on surfaces or transiently settle on surfaces from the air.For example, we identified overlap among fungi found on environmental surfaces and spoiled products was observed, suggesting a relationship between environmental presence and product contamination.By elucidating how spatial and temporal factors influence fungal composition and distribution, better interventions against yeast and mold spoilage can be developed. Sanitation to Control Listeria monocytogenes Pathogen cross-contamination during food production is mitigated by cleaning and sanitizing surfaces. Sanitizers are often applied through spray applicators by facility personnel. However, sanitizer efficacy is often studied in bench-scale experiments (eg., submerged coupons in static or stirred sanitizer) which poorly approximate the impact of fluid dynamics and operator variability. This limits our understanding of how effective sanitization is in commercial application. To examine these dynamic factors, we evaluated the reduction of a 3-strain cocktail ofListeria innocuaspot-inoculated on stainless steel surfaces achieved using a commercially available 100 ppm hypochlorite sanitizer. Reductions on stainless steel surfaces were quantified under three conditions: 10x10 cm2coupon submersion in static sanitizer; bench-scale spray application on 30 x 30 cm2surfaces; and pilot-scale manual spraying of120×70 cm2surfaces.For the bench-scale spray chamber experiments, computational fluid dynamics modeling was used to estimate shear stress at three inoculated locations on the coupon during sanitizer spray application: the "impingement" point directly across from the spray nozzle, an "impingement-adjacent" location, 5.1 cm to the left of spray impingement, and a "fluid-film" location 7.6 cm below the spray impingement where sanitizer flowed down the surface. Static coupon submersion in sanitizer for 3 s resulted in a 2.3 ± 0.1 log reduction inL. innocua. For the bench-scale trials, locations with greater shear stresses had greaterL. innocuareductions after a 3 s exposure time (p < 0.05). The "impingement" location had a reduction of 7.5 ± 0.5 log CFU which was greater than the log-reduction from static application (p < 0.05). The "impingement-adjacent" location had a reduction of 6.4 ± 0.7 log CFU which was not significantly different than at the "impingement" location (p > 0.05), nor from static application (p < 0.05). The "fluid-film" location which had the lowest shear stress had a 0.4 ± 0.1 log CFU reduction which was significantly different than the "impingement," "impingement-adjacent," and static log-reductions (p < 0.05).Bench-scale spray application of water without sanitizer yielded reductions of 3.4 ± 0.6, 3.2 ± 0.5, and 0.4 ± 0.8 log CFU/surface at the "impingement," "impingement-adjacent," and "fluid-film" locations, respectively. Differences in log-reductions elicited by water spray between sites were not statistically different from one another (p < 0.05), nor were they statistically different from static application (p < 0.05). Additionally, the reductions at the "impingement-adjacent" and "fluid-film" locations induced by water application were not significantly different from those observed at those locations in the bench-scale sanitizer trial (p > 0.05). At the pilot scale, sanitizer spray resulted in a 2.5 ± 0.4 log CFU/surface reduction which was statistically different from the reductions induced by 3s sanitizer application at the "impingement" and "fluid-film" positions (p < 0.05), but not from static sanitizer application (p > 0.05) nor the "impingement-adjacent" position (p > 0.05). Additionally, pilot scale sanitization efficacy varied significantly between the three operators (p < 0.05). These differences in efficacy between pilot scale spray, bench-scale spray, and coupon submersion highlight the challenges of laboratory sanitizer research and underscore the importance of validating sanitizer efficacy under realistic conditions.
Publications
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Daly, S.E., Feng, J., Daeschel, D., Kovac, J., Snyder, A.B. 2024. The choice of 16S rRNA gene sequence analysis impacted characterization of highly variable surface microbiota in dairy processing environments. mSystems. E00620-24.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Yang, J., Baker, J., & Snyder, A. B. (2024). Shear stress and sanitizer chemistry act synergistically to remove Listeria from surfaces during environmental sanitation using a fan-shaped spray. IAFP Annual Conference, Long Beach, CA, United States.
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Progress 01/01/23 to 12/31/23
Outputs Target Audience:Cross-contamination in food manufacturing plants is a significant challenge, particularly for ready-to-eat (RTE) foods. The salience of this topic can be seen in the incidence of outbreaks due to post-processing contamination; the application of molecular subtyping, which has revealed the persistence of pathogens in the manufacturing environment; and the introduction of sanitation as a preventive control within the Human Food Rule of FSMA (21 CFR 117.135). But while advancements in detection and subtyping methods have further elucidated the issue, evidence-based interventions remain underdeveloped.In processing facilities where product is not contained within packaging following the kill-step, or where no kill-step is applied, the risk of cross-contamination from persistent colonizers of the processing environment is a significant threat to food safety.This challenge cuts across food sectors and includes dairy (e.g., cheeses, ice cream), meat (e.g., deli products), fresh produce (e.g., leafy greens, fresh-cut melon), and recipe meals (e.g., frozen dinners, assembly meals, RTE sandwiches and salads).In this project, we selected dairy processing plants as model food processing environments because of their highly industrialized processing equipment and many niches, consistent processing and sanitation cycles, and well characterized product microbiological standards.However, we want to also acknowledge that many other food sectors are also served by addressing this challenge. In addition to the dairy industry, the fresh produce industry represents other important stakeholders for this work.We do not envision that the outcomes from this project nor the conceptual framework of the project are limited only to dairy processing environments. While further future work would be needed to define the identity of members of the microbiome within other operation types, we believe that integrating leaders from both the dairy and the fresh produce industry within our discussions can help drive the expansion of this work across product sector categories. Therefore, we have identified industry advisors from both fields as well as a technology company to support technology transfer to diverse audiences. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Collaborative Opportunities This project has provided a unique opportunity for collaboration in our research group.We have a team consisting of two postdocs, three Ph.D. students,a master's student, and an undergraduate researcher that have assisted in sample collection, processing, and analysis.Each member of our team has different expertise and background which has provided in-depth insight into the various aspects of project design, execution, and analysis.Bi-weekly meetings have been held since the beginning of the project in which issues, strategies, and suggestions for sample collection have been discussed.Meetings also include the development of individual research projects which will use this data set.We have also collaborated with a faculty members from other institutions. Industry engagement for trainees A team including the PI, two Ph.D. students and a postdoc have attended the sample collection trips.During these trips, everyone has had the opportunity to engage with the industry professionals at the facilities to learn about facility management, operation, and challenges. Publication Opportunities The dataset from this project will be used in at least 4 different research papers which will be published in peer-reviewed journals and presented at research conferences.We have also had the opportunity to present our work at weekly lab-group meetings in the Department of Food Science at Cornell. Mentorship This project has provided several opportunities for teaching and mentorship for a postdoc and graduate students.The postdoc has taken on a significant role mentoring a master's student who is analyzing the data from this project for her thesis as well as leading and organizing biweekly meetings for the research team. How have the results been disseminated to communities of interest? The code from this project will be publicly available on Github. We've shared updates about our microbiology results with our industry partners. The first manuscript from our paper will be submitted Spring 2024. The 16S sequences collected from our project have been made publicly available on NCBI Project NumberPRJNA1075764https://www.ncbi.nlm.nih.gov/sra/PRJNA1075764 What do you plan to do during the next reporting period to accomplish the goals? We are currently working on two projects that will investigate how food surface microbiomes (bacterial and fungal) are different between facility environments and seasons.This project will illuminate how the processing environment can influence the taxonomic and ecological characteristics of food surface microbiomes.We plan to collect another batch of environmental samples from a facility of interest to investigate for this project. We plan to develop a project to use the metadata we collected to predict individual taxa on different food surfaces. We plan to develop guidelines for effective feature selection of the data attributes to better characterize and predict environmental surface microbiota. Another project will address the third objective from the proposal and use computational fluid dynamics to model sanitation fluid flow through the environmental niches.
Impacts What was accomplished under these goals?
Sample collection An additional 163 samples were collected in Spring 2024 from three different facilities. We collected samples from over 25 different environmental niches. We gathered information on the spatial dimensions of the niches.These samples were processed using classical microbiology techniques to identifyListeria, spore formers, yeast & molds, Enterobacteriaceae, and Gram-negative bacteria. Sequencing We finished the ITS and 16S sequencing data from all of our samples.The Qiagen DNeasy PowerSoil DNA extraction kit was used to extract the DNA from the samples.A portion of the samples had too low of a bioload for DNA extraction and were removed from analysis.The ITS2 region and the 16S V4 regions were selected for sequencing and sent to Novogene.The results were processed in QIIME 2.A protocol for the appropriate analysis of these sequences was selected based on the attributes of the samples.Then, the taxonomy was assigned using the GreenGenes database for 16S sequences and the UNITE database for the ITS sequences.The alpha and beta diversity were calculated.Visualizations of phylogenic trees, relative abundance, microbial network maps, and diversity were developed in Rstudio. Manuscript Preparation A manuscript lead by a postdoc to assess the discrepancies among diversity and taxonomic characterizations generated from eight different bioinformatic pipelines analyzing the 16S RNA sequences collected from the dairy processing facility surfaces has been developed.The manuscript will be submitted in Spring 2024. Summary of Findings: Accurate knowledge of the microbiota on food processing surfaces is important for food quality and safety.However, little has been done to assess the impact of bioinformatic pipeline selection on the accuracy of food processing surface microbiota characterizations.Our research assessed discrepancies among diversity and taxonomic characterizations generated from eight different bioinformatic pipelines analyzing 16S RNA sequences from dairy processing facility surfaces.We found that characterization of low abundance genera (i.e.,below 1% relative abundance) and diversity outcomes were significantly impacted by bioinformatic pipeline selection.Specifically, the choice of sequence aggregation and normalization methods had the most influence on these outcomes.Pipelines using centered log-ratio (CLR)-transformation inflated diversity metrics compared to pipelines using rarefaction, suggesting that rarefaction is preferable for retaining accurate characterization of uneven microbiomes.Furthermore, there were significant discrepancies in diversity values and genera characterizations between the amplicon sequence variant (ASV) and operational taxonomic unit (OTU) approaches.Pipeline selection requires close consideration of microbiota structure to minimize bias.Increased investigation of bioinformatic algorithms are required for accurate ecological interpretations from 16S rRNA sequences.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Chen, H, Moraru, C (2023). Exposure to 222 nm far UV-C effectively inactivates planktonic foodborne pathogens and inhibits biofilm formation. Innovative Food Science & Emerging Technologies. 103411.
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Progress 01/01/22 to 12/31/22
Outputs Target Audience:Cross-contamination in food manufacturing plants is a significant challenge, particularly for ready-to-eat (RTE) foods. The salience of this topic can be seen in the incidence of outbreaks due to post-processing contamination; the application of molecular subtyping, which has revealed the persistence of pathogens in the manufacturing environment; and the introduction of sanitation as a preventive control within the Human Food Rule of FSMA (21 CFR 117.135). But while advancements in detection and subtyping methods have further elucidated the issue, evidence-based interventions remain underdeveloped.In processing facilities where product is not contained within packaging following the kill-step, or where no kill-step is applied, the risk of cross-contamination from persistent colonizers of the processing environment is a significant threat to food safety.This challenge cuts across food sectors and includes dairy (e.g., cheeses, ice cream), meat (e.g., deli products), fresh produce (e.g., leafy greens, fresh-cut melon), and recipe meals (e.g., frozen dinners, assembly meals, RTE sandwiches and salads).In this project, we selected dairy processing plants as model food processing environments because of their highly industrialized processing equipment and many niches, consistent processing and sanitation cycles, and well characterized product microbiological standards.However, we want to also acknowledge that many other food sectors are also served by addressing this challenge. In addition to the dairy industry, the fresh produce industry represents other important stakeholders for this work.We do not envision that the outcomes from this project nor the conceptual framework of the project are limited only to dairy processing environments. While further future work would be needed to define the identity of members of the microbiome within other operation types, we believe that integrating leaders from both the dairy and the fresh produce industry within our discussions can help drive the expansion of this work across product sector categories. Therefore, we have identified industry advisors from both fields as well as a technology company to support technology transfer to diverse audiences. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Bi-weekly team meetings This project has provided a unique opportunity for collaboration in our research group.We have a team consisting of two postdocs, three Ph.D. students, a professional master's student, and several undergraduates that have assisted in sample collection, processing, and analysis.Each member of our team has different expertise and background which has provided in-depth insight into the various aspects of project design, execution, and analysis.Bi-weekly meetings have been held since the beginning of the project in which issues, strategies, and suggestions for sample collection have been discussed.Meetings also include the development of individual research projects which will use this data set. Industry engagement for trainees A team including the PI, two PH.D. students and a postdoc have attended the sample collection trips.During these trips, everyone has had the opportunity to engage with the industry professionals at the facilities to learn about facility management, operation, and challenges. PD opportunities with teaching and presenting The dataset from this project will be used in at least 4 different research papers which will be published in peer-reviewed journals and presented at research conferences.We have also had the opportunity to present our work at weekly lab-group meetings in the Department of Food Science at Cornell. How have the results been disseminated to communities of interest?To date, results have primarily been shared through (1) results reporting to collaborating industry stakeholders and (2) through initial publication of research papers. In the future, as we generate additional results and more complete analyses, we anticipate sharing more broadly through communities of interest. Currently, we have provided our collaborating sites with their results forListeriatesting and with a summary of yeast and mold findings. We have published one paper based on our preliminary work establishing a metadata scheme, which was used by collaborators at FDA in the development of a metadata standard currently available through NCBI. Our team also has a second manuscript under review, led by co-PI Moraru. What do you plan to do during the next reporting period to accomplish the goals?We have one more sampling time point among the three sites.All the data from all sampling seasons will then be combined, processed, and analyze.Specifically, we will be conducting an in-depth analysis examining the effect of the bioinformatic pipeline on amplicon sequencing results as one of our first products from this data set.The samples we collected ranged from low-density, low-diversity samples to high-density, high-diversity samples and we hypothesize that there will be significant differences between diversity results based on step selection within the pipeline.Our team is also examining methods to improve DNA extraction from spore formers.Lysing spores can be inefficient or incomplete during DNA extraction.Since our classical microbiology results have detected spore formers in many samples, this project is crucial for ensuring our results sufficiently encompass the diversity of our samples.Both projects will have significant implications for environmental sampling and characterization.
Impacts What was accomplished under these goals?
Sampling exercises have been completed for ¾ of a year We have collected474environmental samples from three different dairy plants in New York State.Each dairy plant has been visited3 times(Summer 2022, Fall, 2022, and Winter 2023).One more set of visits is planned for Spring 2023 where we plan to collect an additional175 samples.We obtained samples fromover25different environmental niches.Furthermore, these samples were processed using classical microbiology techniques to identifyListeriaand quantify spore formers, yeast & molds, Enterobacteriaceae, and Gram-negative bacteria. Sequencing results The Qiagen DNeasy PowerSoil DNA extraction kit was used to extract the DNA from the Summer 2022 environmental samples.A portion of the samples had too low of a bioload for DNA extraction and were removed from analysis.The ITS2 region and the 16S V4 regions were selected for sequencing.The results were processed in QIIME 2.A protocol for the appropriate analysis of these sequences was selected based on the attributes of the samples.Then, the taxonomy was assigned using the GreenGenes database for 16S sequences and the UNITE database for the ITS sequences.The alpha and beta diversity were calculated.Visualizations of phylogenic trees, relative abundance, microbial network maps, and diversity were developed in Rstudio. Metadata collection method is publicly available We developed a detailed metadata standard which we used to characterize swab site descriptions during collection, which we published as part of the preliminary work for this project:https://journals.asm.org/doi/abs/10.1128/msystems.01284-22. This schema was used in the collection of sample metadata for the dairy samples collected in this study. We collected information of niche-specific attributes with15 features collected(ATP, surface roughness, moisture, temperature, humidity, object type, structure, material, angle, risk zone, location in facility, shape, surface continuity, sanitation regime, and soil).After every sampling trip the metadata was cleaned and attached to sample identifiers in Microsoft Excel for further analysis.The metadata files are available to all members of our research team.
Publications
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Chen, H, Moraru, C (2023). Exposure to 222 nm far UV-C effectively inactivates planktonic foodborne pathogens and inhibits biofilm formation. In Review.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
J. Feng, D. Daeschel, D. Dooley, E. Griffiths, M. Allard, R. Timme, J.B. Pettengill, Y. Chen, A. B. Snyder. 2023. A schema for digitized surface swab site metadata in open-source DNA sequence databases. mSystems. E01284-22.
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