Olfactory imaging technology and detection platform for detecting pork meat freshness based on IoT

To achieve portable and intelligent pork meat freshness detection, this study combined olfactory imaging technology with the Internet of Things (IoT). By conducting reaction experiments and screening color-sensitive materials, a sensor array was developed by leveraging RGB differences, deviations, and principal component analysis (PCA). Physicochemical indicators such as spatial image data and total volatile basic nitrogen (TVB-N) content were also utilized. A predictive model was constructed using the partial least squares (PLS) algorithm (RC = 0.9846, RP = 0.9835, RMSEC = 0.4662, and RMSEP = 0.4988). The olfactory imaging device was transformed into an edge device using Raspberry Pi to further enhance its functionality. Additionally, user-friendly visualization software and an edge-cloud platform were created for algorithm sharing and cloud computing. Extensive testing confirmed the method's intelligence and effectiveness, offering a smart solution for pork meat freshness detection with potential for further development.