Location: Quality and Safety Assessment Research Unit
Project Number: 6040-30600-001-000-D
Project Type: In-House Appropriated
Start Date: Jun 13, 2025
End Date: Jun 12, 2030
Objective:
Objective 1. Elucidate how inherent muscle chemical and structural traits, physiological mechanisms, and processing methods impact commercially important poultry meat attributes and quality defects.
Sub-objective 1A. Identify and characterize the physiological mechanisms regulating muscle quality traits and myopathies using in vitro and in vivo approaches
Sub-objective 1B. Determine how physical and chemical properties in muscle change and influence meat quality characteristics during heating, freezing/thawing, and marination
Sub-objective 1C. Determine how primary processing steps (bird stunning through carcass chilling) impact the severity of meat quality defects observed in broiler spaghetti meat
Objective 2. Develop multimodal, nondestructive objective methods and sensing systems for measuring commercially important attributes and quality defects in poultry products.
Sub-objective 2A. Develop AI-powered multimodal imaging techniques for automated assessment of poultry meat quality defects
Sub-objective 2B. Develop robot-assisted sensing technologies with tactile sensors and wide-field optical coherence tomographic (OCT) imaging for detecting the presence and severity of poultry breast myopathies
Sub-objective 2C. Develop dielectric-spectroscopy and AI-based sensing system for rapid detection of poultry meat quality defects
Sub-objective 2D. Evaluate a low-field nuclear magnetic resonance (LF-NMR) based technique as a non-destructive, rapid method for predicting sensory attributes of cooked broiler breast meat
Objective 3. Develop multi-parameter microwave sensing technologies for rapid, nondestructive determination of commercially important properties in agriculture commodities such as grain, seed, inshell nuts, and poultry feed.
Sub-objective 3A. Develop low-footprint microwave sensors within a distributed network system for real-time mapping of in-shell moisture content distribution in nut storage facilities and drying trailers
Sub-objective 3B. Develop analytical and AI-based algorithms for determining multiple commercially important properties from dielectric measurements at microwave frequencies
Sub-objective 3C. Develop sensor prototypes for determining multiple commercially important properties of grain, seed, in-shell nuts, and poultry feed
Approach:
Meat quality defects associated with the fast growth and large size of modern broiler chickens are problematic to the poultry industry. Unfortunately, the underlying factors that trigger the development of myopathies leading to defects such as woody breast, white striping, and spaghetti meat are not well understood. This project utilizes in vivo and in vitro approaches to investigate underlying cellular and physiological mechanisms that lead to meat quality defects. Omics-based approaches are used to analyze various tissues from broilers to identify key pathways and systems and histological analysis is used to confirm myopathy phenotypes. Pathways of interest are explored using biochemical, molecular biology, and cell culture techniques to gain a better understanding of factors that trigger myopathy development. Studies using low-field NMR, histology, and muscle ultrastructural measurements investigate how alterations in muscle tissue architecture control meat quality traits such as water-holding and texture in myopathic muscle. Studies also investigate the effects of primary and further processing parameters on the meat quality and functionality characteristics of myopathic breast muscle.
The lack of objective, non-destructive methods for assessing poultry meat quality characteristics and defects has made it difficult for the industry to develop effective myopathy mitigation and product sorting strategies. Research focuses on developing an AI-powered vision-guided robotic technology for on-line meat quality assessment and sorting. A wide-field subsurface imaging technology for detecting the presence and severity of meat quality defects is being developed using optical coherence tomography, hyperspectral, and 3D imaging. Research is being conducted on a dielectric-spectroscopy based sensing system using AI for rapid detection of poultry meat defects and a low-field NMR method to predict sensory attributes in poultry meat.
In grain, seed, nuts, and feed, multiple pieces of equipment based on different technologies are often used to determine quality attributes. Using multiple sensors or destructive methods is time consuming, costly, and often not practical for implementation in dynamic situations or characterizing large volumes of materials such as in trailers or storage facilities. Research focuses on developing microwave sensors to simultaneously measure multiple quality attributes (bulk density, moisture content, water activity, meat content, and foreign material content) in grain, seed, in-shell nuts, and feed. Analytical and AI-based algorithms for determining quality attributes from dielectric properties are being developed and different sensor designs investigated. Research also focuses on developing low-footprint microwave sensors within a distributed network system for real-time mapping of in-shell moisture content distribution in nut storage facilities and drying trailers. Appropriate microwave circuitry, antennas, and communication protocols are being tested. Correlations between dielectric properties and kernel moisture content will be determined and used to establish calibrations for real-time sensing.
