Abstract

Fast and accurate detection of meat freshness is very important in the food industry because it directly affects the quality and safety of consumption. Traditional sensory assessments are often inefficient and subjective, so more reliable technology is needed. This research develops a system to detect beef freshness using the E-Nose tool and the K-Nearest Neighbor (KNN) algorithm with accuracy, precision, and recall. E-Nose detects volatile compounds from beef, while KNN classifies sensor data. The research  results show that this tool can identify the freshness level of beef with high accuracy so that it can be used to ensure meat quality in the food industry. The combination of E-Nose and KNN shows promising results in detecting beef freshness by identifying freshness biomarkers through aroma analysis. The KNN model trained with data from the E-Nose sensor can classify fresh, slightly fresh, and non-fresh meat with good precision, achieving an accuracy of 83%. Implementing this technology in the food industry can improve meat products' safety and quality standards, ensuring that consumers get safe products. Overall, E-Nose technology with the KNN method is an effective, efficient, and reliable tool for detecting the freshness of beef, providing significant benefits for the food industry in maintaining product quality and safety.