Quality Assessment of Beef Based of Computer Vision and Electronic Nose

Current techniques for beef quality evaluations rely on sensory methods. These procedures are subjective, prone to error, and difficult to quantify. Automated evaluation of color and odor is desirable to reduce subjectivity and discrepancies and assist with the creation of standards for inspectors worldwide. The objectives of this study were to develop color machine vision techniques for visual evaluation and to test electronic nose sensors for odor raw and beef. A color machine vision system was developed to analyze the color of beef samples. The system was able to analyze the color of samples with non-uniform color surfaces. An electronic nose sensors was used to measure odors of beef and beef stored at different temperatures, with different levels of spoilage. Discriminant function analysis was used as the pattern recognition technique to differentiate samples based on odors. Results showed that the electronic nose could discriminate differences in odor due to storage time and spoilage levels for beef. Results also showed good correlation of sensor reading with sensory scores overall, the electronic nose showed good sensitivity and accuracy. Results from this work could lead to methodologies that will assist in the objective and repeatable quality evaluation of beef. These methods have potential in industrial and regulatory application where rapid response, no sample preparation, and no need for chemicals are required.