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 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
- Qin, Jianwei - Tony Qin
- Chao, Kuanglin - Kevin Chao
- Baek, Insuck
- Food Safety, (Animal and Plant Products) (108)

Research Project: **Location:
                            Environmental Microbial & Food Safety Laboratory** Project Number: 8042-42000-021-014-S
                        
                        Project Type: Non-Assistance Cooperative Agreement Start Date: Sep 1, 2022
                        
                        End Date: Aug 31, 2026 **Objective:**
                        
                        ARS is conducting research to develop the next generation of rapid, intelligent, user-friendly sensing technologies for use in food production and food processing. Automated sensing and instrumentation systems require real-time data processing and analysis capabilities to provide nonexpert users with a clear understanding and ability to make decisions based on system output. The objective of this cooperative research is to develop automated optical sensing and instrumentation technologies and analysis methodologies for food safety and food integrity evaluations of food products for identifying adulterants, foreign materials, and microbial contamination as well as for authenticating food ingredients. **Approach:**
                        
                        Various spectroscopic and multimodal spectral imaging technologies have been developed for rapidly evaluating food products for safety and quality attributes during production and post-harvest processing, utilizing Raman chemical imaging and spectroscopy as well as fluorescence and reflectance techniques spanning the visible, near-infrared, and mid-infrared spectral regions. For the next generation of user-friendly sensing and instrumentation technologies, automated real-time processing of a variety of these types of spectral and image data, and analytical algorithms and models such as multivariate and machine learning/artificial intelligence (ML/AI) methods, will be developed and incorporated into systems with graphic user interfaces (GUIs) designed to enable non-expert users to conduct rapid and routine applications. ARS and cooperator will focus on next-generation sensing and instrumentation development that addresses food safety concerns associated with food adulterant detection and food ingredient authentication.


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