Traditional plating methods for bacterial enumeration can be limited, but the development of high-throughput DNA sequencing, such as Oxford Nanopore Technologies (ONT), can provide rapid and highly specific alternative for species-level identification. In this study, ONT amplicon sequencing was applied to fresh broiler breast meat to identify their bacterial composition and monitor their dynamic changes. The sequencing data were complemented by sensory panels, physicochemical analysis, and traditional plating methods. Over time, the bacterial diversity decreased within and across samples. By the end of shelf-life, Pseudomonas fragi, Pseudomonas lundesis, and Brochothrix thermosphacta became the most prevalent species. These bacteria were associated with spoilage attributes that were reported in the sensory panels. This study demonstrated the effectiveness of Nanopore sequencing in determining the spoilage associated bacteria in chicken meat. Future research may focus on developing targeted interventions to mitigate the impact of these spoilage bacteria and extend the shelf life of chicken meat.

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.