An Intelligent Wireless Sensor Network for Real-Time Kimchi Fermentation Monitoring and Early Abnormality Detection

Kimchi fermentation involves dynamic physicochemical and microbial changes; however, conventional monitoring methods are generally dependent on intermittent measurements, resulting in limitations in the real-time detection of abnormal fermentation. In this study, a Wireless Sensor Network (WSN)-based Fermentation Monitoring System (WFMS) and a Long Short-Term Memory (LSTM)-based Anomaly Detection System (LADS) were developed to continuously monitor internal pressure changes during kimchi fermentation. Kimchi samples were prepared under normal fermentation conditions (CON) and glucose-added conditions (GLU-6). Pressure data were collected at 10 min intervals using 15 psi and 30 psi pressure sensors connected to an Arduino Nano 33 IoT board and were transmitted to the ThingSpeak platform. During the fermentation period, pressure data were collected stably, while the external temperature was maintained at approximately 25 °C. Both CON and GLU-6 samples exhibited a rapid increase in internal pressure during the early fermentation stage, followed by a gradual decrease. However, relatively larger pressure fluctuations were observed in the middle and late fermentation stages of the GLU-6 samples. An LSTM autoencoder model trained using CON data established a reconstruction error-based threshold of 0.0025 and successfully detected anomalies in the GLU-6 samples. Anomalies were mainly identified during the initial fermentation stage and between fermentation days 2 and 4. These results demonstrate that pressure-based real-time monitoring combined with LSTM autoencoder analysis can be effectively applied for the non-destructive tracking of kimchi fermentation and the early detection of abnormal fermentation patterns.