A portable, low-power and low-cost Electronic Nose for Meat Freshness Assessment

Ensuring food safety through real-time freshness assessment is a critical challenge in food monitoring. This study presents a portable and modular electronic system, for electrochemical measurements, exploited for meat spoilage detection, utilizing a low-power Solid Polymer Electrolyte Gas Sensor (SPEGS) to measure volatile organic compounds (VOCs). Unlike conventional Metal Oxide Semiconductor (MOS)-based e-nose systems, which require heating elements, our system operates passively, significantly reducing power consumption. The aim is to provide a tool that can be used for monitoring during transport, at the point of sale or at user’s homes. To enhance classification accuracy, different machine learning (ML) models were trained on VOC data. Our results show that a Neural Network (NN) model achieved 87.7% accuracy, closely matching previous approaches in the literature. The proposed system demonstrates a scalable, lowpower alternative for real-time, non-invasive food quality monitoring.