{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124257"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124257","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Developing sampling simulation and quantitative microbial risk assessment to improve food safety management systems","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Kim, Minho"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Food Science & Human Nutrition","degree_department":null,"school":null,"contributors":["Stasiewicz, Matthew J","Miller, Michael J","Banerjee, Pratik","Ozturk, Oguz"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Salmonella","Qmra","Serotype"],"languages":["en","eng"],"rights":["Copyright 2024 Minho Kim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124257","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Stasiewicz, Matthew J","Miller, Michael J","Banerjee, Pratik","Ozturk, Oguz"]},{"key":"dc:creator","label":"Author","values":["Kim, Minho"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-23"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Food Science & Human Nutrition"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Salmonella","Qmra","Serotype"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Minho Kim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124257"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Minho Kim, accepted the attached license on 2024-04-22 at 10:39.","The student, Minho Kim, submitted this Dissertation for approval on 2024-04-22 at 10:47.","This Dissertation was approved for publication on 2024-04-23 at 13:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20363 on 2024-09-16 at 00:33:59","Challenges in a modern food system can be defined with large-scale production and hazards that are rare and not evenly distributed. With these characteristics, traditional experimental research design with smaller sample size can be limited to answer current issues in food safety dealt with in this study such as (1) How powerful is given sampling plans in powdered food product production? (2) Can targeting certain levels or/and serotypes of Salmonella in raw poultry products help reducing the public health risk? Tools like simulation, modeling, Quantitative Microbial Risk Assessment (QMRA) can be helpful in answering these questions combined with numerous data collected from government and industry. These tools can provide the ability to develop science-based food safety management strategies that are specific to different processing environments using collected data. This research focused on answering rising questions in the current food safety issues using combined data analytics. The objectives of this PhD research were (1) Adapt previously developed 2-dimensional and 3-dimensional sampling tool for 1-dimensional sampling tool specific for powdered food products. (2) Examine the power of representative sampling plans with realistic contamination profiles in recalled and non-recalled PIF batches from a published paper using developed 1D sampling simulation. (3) Develop a QMRA for assessing risk in certain levels and serotypes of raw poultry products. (4) Assess public health risks in raw chicken parts contaminated with different levels of all Salmonella serotypes and high-and low-virulent serotypes using QMRA. To achieve the first two objectives, we adapted a previously published sampling simulation to PIF sampling and benchmarked industry-relevant sampling plans across different numbers of grabs, total sample mass, and sampling patterns. Overall, (1) systematic or stratified random sampling patterns are equal to or more powerful than random sampling of the same sample size and total sampled mass, and, (2) taking more samples, even if smaller, can increase the power to detect contamination. The rest of the research objectives were addressed by developing QMRA. Our risk assessment suggests public health risk in chicken parts is concentrated in the small proportion of finished products contaminated with high-levels and specifically high-levels of high-virulent serotypes. Low-virulent serotypes, such as Kentucky, are predicted to contribute to extremely few human cases. In conclusion, data analytics tools used in this research such as simulation, modeling and QMRA combined with numerous industry and government testing data, showed that they can help develop better food safety management strategies to deal with current issues in the food industry. We are now living in the era of big data, and the ability to explore massive data will guide us to develop proper strategies to deal with evolving foodborne pathogens."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Developing sampling simulation and quantitative microbial risk assessment to improve food safety management systems"]}]}],"canonical_facts":{"dc:contributor":["Stasiewicz, Matthew J","Miller, Michael J","Banerjee, Pratik","Ozturk, Oguz"],"dc:creator":["Kim, Minho"],"dc:date":["2024-05","2024-04-23"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Minho Kim, accepted the attached license on 2024-04-22 at 10:39.","The student, Minho Kim, submitted this Dissertation for approval on 2024-04-22 at 10:47.","This Dissertation was approved for publication on 2024-04-23 at 13:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20363 on 2024-09-16 at 00:33:59","Challenges in a modern food system can be defined with large-scale production and hazards that are rare and not evenly distributed. With these characteristics, traditional experimental research design with smaller sample size can be limited to answer current issues in food safety dealt with in this study such as (1) How powerful is given sampling plans in powdered food product production? (2) Can targeting certain levels or/and serotypes of Salmonella in raw poultry products help reducing the public health risk? Tools like simulation, modeling, Quantitative Microbial Risk Assessment (QMRA) can be helpful in answering these questions combined with numerous data collected from government and industry. These tools can provide the ability to develop science-based food safety management strategies that are specific to different processing environments using collected data. This research focused on answering rising questions in the current food safety issues using combined data analytics. The objectives of this PhD research were (1) Adapt previously developed 2-dimensional and 3-dimensional sampling tool for 1-dimensional sampling tool specific for powdered food products. (2) Examine the power of representative sampling plans with realistic contamination profiles in recalled and non-recalled PIF batches from a published paper using developed 1D sampling simulation. (3) Develop a QMRA for assessing risk in certain levels and serotypes of raw poultry products. (4) Assess public health risks in raw chicken parts contaminated with different levels of all Salmonella serotypes and high-and low-virulent serotypes using QMRA. To achieve the first two objectives, we adapted a previously published sampling simulation to PIF sampling and benchmarked industry-relevant sampling plans across different numbers of grabs, total sample mass, and sampling patterns. Overall, (1) systematic or stratified random sampling patterns are equal to or more powerful than random sampling of the same sample size and total sampled mass, and, (2) taking more samples, even if smaller, can increase the power to detect contamination. The rest of the research objectives were addressed by developing QMRA. Our risk assessment suggests public health risk in chicken parts is concentrated in the small proportion of finished products contaminated with high-levels and specifically high-levels of high-virulent serotypes. Low-virulent serotypes, such as Kentucky, are predicted to contribute to extremely few human cases. In conclusion, data analytics tools used in this research such as simulation, modeling and QMRA combined with numerous industry and government testing data, showed that they can help develop better food safety management strategies to deal with current issues in the food industry. We are now living in the era of big data, and the ability to explore massive data will guide us to develop proper strategies to deal with evolving foodborne pathogens."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124257"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Minho Kim"],"dc:subject":["Salmonella","Qmra","Serotype"],"dc:title":["Developing sampling simulation and quantitative microbial risk assessment to improve food safety management systems"],"dc:type":["text"],"thesis:degree_discipline":["Food Science & Human Nutrition"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}