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After completing the production of preserved eggs, traditionally, the degree of gelling is judged by allowing workers to tap the preserved eggs with their fingers and sense the resulting oscillations. The amount of oscillation is used for the quality classification. This traditional method produces varying results owing to the differences in the sensitivity of the individual workers, who are not objective. In this study, dielectric detection technology was used to classify the preserved eggs nondestructively. The impedance in the frequency range of 2–300 kHz was resolved into resistance and reactance, and was plotted on a Nyquist diagram. Next, the diagram curve was fitted in order to obtain the equivalent circuit, and the difference in the compositions of the equivalent circuits corresponding to gelled and non-gelled preserved eggs was analyzed. A preserved egg can be considered an RLC series circuit, and its decay rate is consistent with the decay rate given by mechanical vibration theory. The Nyquist diagrams for the resistance and reactance of preserved eggs clearly showed that the resistance and reactance of gelled and non-gelled eggs were quite different, and the classification of the eggs was performed using Bayesian network (BN). The results showed that a BN classifier with two variables, i.e., resistance and reactance, can be used to classify preserved eggs as gelled or non-gelled, with an accuracy of 81.0% and a kappa value of 0.62. Thus, a BN classifier based on resistance and reactance demonstrates the ability to classify the quality of preserved egg gel. This research provides a nondestructive method for the inspection of the quality of preserved egg gel, and provides a theoretical basis for the development of an automated preserved egg inspection system that can be used as the scientific basis for the determination of the quality of preserved eggs.

Today, the most serious threat to global health is the continuous outbreak of respiratory diseases, which is called Coronavirus Disease 2019 (COVID-19). The outbreak of COVID-19 has brought severe challenges to public health and has attracted great attention from the research and medical communities. Most patients infected with COVID-19 will have fever. Therefore, the monitoring of body temperature has become one of the most important basis for pandemic prevention and testing. Among them, the measurement of body temperature is the most direct through the Forehead Thermometer, but the measurement speed is relatively slow. The cost of fast-checking body temperature measurement equipment, such as infrared body temperature detection and face recognition temperature machine, is too high, and it is difficult to build Disease Surveillance System (DSS). To solve the above-mentioned problems, the Intelligent pandemic prevention Temperature Measurement System (ITMS) and Pandemic Prevention situation Analysis System (PPAS) are proposed in this study. ITMS is used to detect body temperature. However, PPAS uses big data analysis techniques to prevent pandemics. In this study, the campus field is used as an example, in which ITMS and PPAS are used. In the research, Proof of Concept (PoC), Proof of Service (PoS), and Proof of Business (PoB) were carried out for the use of ITMS and PPAS in the campus area. From the verification, it can be seen that ITMS and PPAS can be successfully used in campus fields and are widely recognized by users. Through the verification of this research, it can be determined that ITMS and PPAS are indeed feasible and capable of dissemination. The ITMS and PPAS are expected to give full play to their functions during the spread of pandemics. All in all, the results of this research will provide a wide range of applied thinking for people who are committed to the development of science and technology.

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