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                    U.S. DEPARTMENT OF AGRICULTURE
 Environmental Microbial & Food Safety Laboratory: Beltsville, MD
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ARS Home Northeast Area Beltsville, Maryland (BARC) Beltsville Agricultural Research Center Environmental Microbial & Food Safety Laboratory Research





- Kim, Moon
- Chao, Kuanglin - Kevin Chao
- Qin, Jianwei - Tony Qin
- Baek, Insuck
- 2023
- 2022
- Publications
- Food Safety, (Animal and Plant Products) (108)

Research Project: **Location:
                            Environmental Microbial & Food Safety Laboratory** Project Number: 8042-42000-021-000-D
                        
                        Project Type: In-House Appropriated Start Date: Mar 30, 2021
                        
                        End Date: Mar 29, 2026 **Objective:**
                        
                        Objective 1: Develop and validate an autonomous unmanned aerial vehicle with multimode imaging technologies for preharvest inspection of produce fields for animal intrusion and fecal contamination, and for irrigation water quality monitoring.
Objective 2: Advance the development of customized compact spectral sensing technologies for food inspection and sanitation assessment in food processing, and for controlled-environment produce production, with embedded automated detection results for non-expert end users.
Sub-objective 2.A: Develop a handheld line-scan hyperspectral imaging device with enhanced capabilities for contamination and sanitation inspection in food processing environments.
Sub-objective 2.B: Develop a compact automated hyperspectral imaging platform for food safety and plant health monitoring for controlled-environment produce production in NASA space missions.
Objective 3: Develop innovative spectroscopic and optical methods to characterize food composition and nondestructively detect adulterants and contaminants, for screening and inspecting agricultural commodities and commercially prepared food materials. 
Sub-objective 3.A: Develop a transportable multimodal optical sensing system for rapid, automated, and intelligent biological and chemical food safety inspection.
Sub-objective 3.B: Develop a novel apparatus enabling dual-modality concomitant detection, along with associated methods and procedures, for assuring food integrity. **Approach:**
                        
                        The overall goal of this project is to develop and validate automated sensing tools and techniques to reduce food safety risks in food production and processing environments. Engineering-driven research will develop the next generation of rapid, intelligent, user-friendly sensing technologies for use in food production, processing, and other supply chain operations. Feedback from industrial and regulatory end users, and from stakeholders throughout the food supply chain, indicates that effective automated sensing and instrumentation systems require real-time data processing to provide non-expert users with a clear understanding and ability to make decisions based on the system output. Towards this end, we will develop unmanned aerial vehicles with multimodal remote sensing platforms and on-board data-processing capability to provide real-time detection and classification of animal intrusion and fecal contamination in farm fields and of irrigation water microbial quality. We will upgrade our existing handheld imaging device for contamination and sanitation inspection with multispectral imaging and embedded computing and artificial intelligence. We are also partnering with the NASA Kennedy Space Center to develop a novel, compact, automated hyperspectral platform for monitoring food safety and plant health of space crop production systems. Food safety and integrity requires identifying adulterants, foreign materials, and microbial contamination as well as authenticating ingredients. We will develop innovative multimodal optical sensing systems utilizing dual-band laser Raman, and Raman plus infrared, for simultaneous detection on a single sampling site. Spectroscopic and spectral imaging-based methodologies will be developed to enhance detection efficacy for liquid or powder samples. These systems will be supported with intuitive, intelligent sample-evaluation software and procedures for both biological and chemical contaminants.


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