Skip to main content
ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality and Safety Assessment Research Unit » Research » Publications at this Location » Publication #406929
Research Project: Smart Optical Sensing of Food Hazards and Elimination of Non-Nitrofurazone Semicarbazide in Poultry

Location: Quality and Safety Assessment Research Unit

Title: Hyperspectral microscope imaging applications for food safety and quality

Author
item Park, Bosoon

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/9/2023
Publication Date: 8/3/2023
Citation: Park, B. 2023. Hyperspectral microscope imaging applications for food safety and quality. Meeting Abstract. https://www.optica.org/events/congress/optical_sensors_and_sensing_congress/.

Interpretive Summary:

Technical Abstract: Since the hyperspectral imaging (HSI) technology has been introduced in food and agriculture, research has been conducted successfully for food safety and quality assessment, mostly with a prism-grating-prism line scan platform. Furthermore, HSI technology has been employed in micro-scale image acquisition called hyperspectral microscope imaging (HMI) for foodborne pathogen detection specifically foodborne bacterial detection at the cellular level without a label such as fluorescent dyes. Consequently, we took advantage of imaging spectroscopy as a tool of food safety and quality evaluation with micro-scale as well as macro-scale image acquisition. In this presentation, two different HMI platforms, acousto-optic tunable filter (AOTF) and Fabry-Perot interferometer (FPI), are employed for food quality and safety evaluation. Especially, performance of each HMI platform is compared for foodborne pathogen detection. Also, efficient sample preparation methods are presented for acquiring high quality HMI hypercubes. Additionally, modeling method with artificial intelligence (AI) algorithms including deep learning is discussed for classification of agricultural commodity for safety and quality evaluation. Finally, several case studies including foodborne pathogenic bacterial classification with machine learning, chicken breast meat myopathy detection, green tea quality evaluation, and blueberry firmness measurement at the cellular level using AOTF-HMI and FPI-HMI are demonstrated. This presentation will expand current hyperspectral imaging technology with integration of microscopy and deep learning methods to evaluate food and agricultural commodity for better understanding at cellular level to maintain food quality and safety.