Enhancing microbial food safety of fresh leafy greens by technology innovations and AI tools

Fresh produce is a critical component of healthy diets that provide essential nutrients to the human body. However, consuming fresh fruits and vegetables has long been the primary source of foodborne illness outbreaks. Moreover, leafy greens alone have caused 74 foodborne outbreaks in the ten-year period of 2009-2018, which is over half of the total outbreaks caused by the consumption of fresh vegetables. Current approaches to improve fresh produce microbial food safety include risk management and hazard control at the pre-harvest level and sanitization and cross-contamination control during post-harvest processing and handling. The recurrence of fresh produce outbreaks calls for new strategies and insights to ensure food safety for this category of fresh foods. Thus, the overall goal of this work was to understand the food safety risks associated with fresh produce and develop preventive measures to control food safety hazards at pre- and post-harvest stages. Food safety issues in controlled environment agriculture (CEA) settings like hydroponic and aquaponic cropping systems were the target of investigation. Hence, we first investigated the food safety risks and potential hazards of hydroponic/aquaponic farming practices via a food safety survey and next-generation 16S-ITS-23S rRNA microbiome sequencing. Based on the risk assessment, we developed ultrasound-assisted seed sanitation/priming treatments to control E. coli O157:H7 growth and improve growth outcomes at the pre-harvest stage. At the post-harvest level, we examined the susceptibility of different leafy vegetables to bacterial attachment, survival, and growth. We further redesigned double-sided artificial phylloplane surfaces for seven varieties of leafy greens as a reproducible and controllable platform for studying the interactions between bacteria and leafy green phylloplane. In the last part, we developed a data-driven strategy to optimize the fresh produce sanitation process, using an ultrasound-assisted washing process as a model system. We applied machine learning algorithms to optimize the post-harvest sanitation process for enhancing the sanitation efficiency of fresh-cut leafy greens. Overall, this work has provided new insights into the development of food safety preventive measures in CEA farming and fresh produce processing systems and contributed to enhancing the microbial food safety of fresh produce. By integrating bioinformatics and machine learning tools with data produced from wet lab practices, this study has taken a step forward into food manufacturing system innovations towards Industry 4.0.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Food Science & Human Nutrition

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Dong, Mengyi

Contributors dc:contributor

• Feng, Hao
• Miller, Michael J.
• Banerjee, Pratik
• Stasiewicz, Matthew J.

• Hydroponics
• Microgreens
• Microbiome
• Bioinformatics
• Lettuce
• Leafy Green Sanitation
• Artificial Intelligence
• Machine Learning
• Pdms
• Microfabrication

Statement dc:rights

• © 2022 Mengyi Dong

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/117573

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F117573