Mississippi State University
Sukumaran, Anuraj T.
Other Advisors or Committee Members
Lowe, John Wes
Date of Degree
Graduate Thesis - Open Access
Master of Science (M.S.)
James Worth Bagley College of Engineering
Department of Agricultural and Biological Engineering
Manual inspection is a prevailing practice for quality assessment of poultry meat, but it is labor-intensive, tedious, and subjective. This thesis aims to assess the efficacy of an emerging structured illumination reflectance imaging (SIRI) technique with machine learning approaches for assessing WS and microbial spoilage in broiler breast meat. Broiler breast meat samples were imaged by an in house-assembled SIRI platform under sinusoidal illumination. In first experiment, handcrafted texture features were extracted from direct component (DC, corresponding to conventional uniform illumination) and amplitude component (AC, unique to the use of sinusoidal illumination) images retrieved from raw SIRI pattern images build linear discriminant analysis (LDA) models for classifying normal and defective samples. A further validation experiment was performed using deep learning as a feature extractor followed by LDA. The third experiment was on microbial spoilage assessment of broiler meat, deep learning models were used to extract features from DC and AC images builds on classifiers. Overall, this research has demonstrated consistent improvements of AC over DC images in assessing WS and spoilage of broiler meat and that SIRI is a promising tool for poultry meat quality detection.
U.S. Department of Agriculture, National Institute of Food and Agriculture [Grant No. 2022-67018-36625] and the Mississippi Agricultural and Forestry Experimentation Special Research Initiative
Olaniyi, Ebenezer O., "Non-destructive evaluation of white striping and microbial spoilage of Broiler Breast Meat using structured-illumination reflectance imaging" (2023). Theses and Dissertations. 5891.