Real-time monitoring system for quality monitoring of jujube slice during drying process

Abstract

The real-time monitoring and prediction system for quality attributes of jujube slices during the drying process was designed to solve the problem of destructive and inconvenient of the traditional quality detection method and realize quality online monitoring. Firstly, machine vision and automatic weighing were employed to monitor the color and moisture content changes of jujube slices in real-time. Secondly, correlation models between color parameter (a* value) and nutritional quality attributes (vitamin C, reducing sugar) were established to predict vitamin C and reducing sugar content of jujube slices during the drying process. Finally, the upper computer monitoring software was integrated and designed based on LABVIEW virtual instrument, and the real-time monitoring system was tested and validated. Results showed that: the changing trends of color (L*, a*, and b* values) monitored by the system were basically the same as the results detected by the color difference meter, and the average errors of L*, a*, and b* values were 0.93, 0.52, and 0.73, respectively. The average relative error of moisture content between the system monitoring and manual static detection was 0.18%. The average error of vitamin C and reducing sugar content between the system prediction and manual detection were 50 mg/100 g on dry basis and 0.71g/100 g on dry basis, respectively. The current work can provide a useful reference for real-time monitoring of quality attributes of fruits and vegetables during the drying process.

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“…Their content significantly impacts the taste and ripeness of jujube fruit, serving as a key quality indicator [3]. Moisture content directly affects the taste and storage period of jujube fruit and is also one of the main indicators of jujube fruit quality [4]. Therefore, it is essential to accurately and efficiently measure these parameters.…”

Cloud-Enabled Real-Time Monitoring and Alert System for Primary Network Resource Scheduling and Large-Scale Users

“…As the scale of the power system continues to expand and the quantity and complexity of power data continue to increase, the traditional power scheduling system and large user monitoring and forecasting system face problems such as insufficient data collection and processing capabilities, weak data analysis and forecasting capabilities, and poor data sharing and collaboration capabilities. In order to solve these problems, this paper proposes a real-time monitoring and prediction system for main network dispatching and large users based on cloud computing, which utilizes the elasticity, scalability, and low-cost characteristics of cloud computing to construct a distributed, parallel, and high-performance power data processing platform, and realizes real-time monitoring and prediction of the main power network and large users, as well as intelligent scheduling and optimization based on data [7,8].…”