Beef quality identification using color analysis and k-nearest neighbor classification

Beef is one of the many produce prone to contamination by microorganism. Water and nutrition contents make an ideal medium for the growth and proliferation of microorganism. Contaminated beef will degrade and has less storage duration. Beef is valued by two factors; its price and its quality. The quality itself is measured using four characteristics; marbling, color of meat, color of fat, and meat density. Specifically, marbling is the dominant parameter that determines meat's quality. Determination of meat quality is conducted visually by comparing the actual meat and reference pictures of each meat class. This process is very subjective in nature. Therefore, this research aims to develop an automated system to determine meat by adopting the Indonesian National Standard requirement on the quality of carcass and beef (SNI 3932:2008) using the image processing technique. Image segmentation is carried out using the thresholding method and classification is conducted using the k-nearest neighbor algorithm. The features used to differentiate beef quality are marbling score, color of meat, and color of fat. Results indicate that the system developed is able to acquire images and identify beef quality as required in the Indonesian National Standard.