IoT-Based Real-Time Food Safety and Quality Monitoring System Using Gas Sensors and Machine Learning for Perishable Food Detection

Abstract

Food safety and quality monitoring are significant issues for ensuring human safety, especially for perishable goods and cooked foods such as rice, fish, or meat. Conventional inspection methods are manual, labour-intensive, and error-prone, thus re-establishing the need for an automated, novel solution. This paper proposes a real-time food safety and quality monitoring system based on three gas sensors (MQ3, MQ4, and MQ135) interfaced with an ESP32 microcontroller core to obtain readings, which are displayed on an OLED display. By detecting alcohol vapors, the MQ3 sensor can identify meat rot, both of which are important parameters for detecting spoiled food. The methane gas is detected using the MQ4 sensor, and air quality indicators such as carbon dioxide and ammonia are detected using the MQ135 sensor. The V. Logenthira: Three specimens were prepared, i.e., food- containing substrate (FCS), study shows that FCS is degraded more than twice in anaerobic conditions with [24] delignified straw-peat at Fig.