Real-Time Food Spoilage Detection Using Multi-Sensor Gas Analytics and IoT-Based Monitoring

This study introduced an IoT-enabled real-time food rotting detection architecture powered by dynamic edge computing and multi-sensor gas analytics. The system effectively adjusted its categorization limits (Td) to real-time microclimate fluctuations by merging MQ-series gas data into a single index (ΦGas) and using a continuous multivariate contextual compensation function. The system successfully neutralizes baseline drift noise caused by environmental humidity and temperature fluctuations while achieving an absolute classification accuracy of 100% across transitional decomposition phases, according to experimental validation using telemetry streams.