Abstract
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.
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