Classification of Non-Frozen and Frozen–Thawed Pork with Adaptive Support Vector Machine and Electronic Nose

The quality of raw meat is important for community health as its freshness is crucial to preventing foodborne illnesses. In the United States, the related illness cases were 9.4 million cases with 55,961 hospital admissions and 1351 deaths annually. This underscores the urgent need for improved meat quality monitoring. This study aims to develop an electronic nose (E-nose) that can differentiate between frozen–thawed and fresh pork meat samples, thereby enhancing food safety. We designed the E-nose using MQ series gas sensor array with temperature and humidity sensors, and an Arduino Uno microcontroller. Sensors were calibrated for accurate data collection. An adaptive support vector machine (ASVM) was used for data classification. We evaluated the model’s accuracy using a confusion matrix. The ASVM model exhibited robust performance, achieving an accuracy of 88%. Its performance was evaluated with recall, F1 scores, and precision. To further enhance the model’s performance, future studies are mandated to integrate additional gas sensors, increase sample sizes, advance data preprocessing techniques, and explore different machine learning algorithms or ensemble methods.