Automatic Classification of Time-Temperature Data for Aquatic Production in the Cold Chain

Temperature monitoring data plays a vital role in identifying aquatic product quality risks in the cold chain. Collection and description of temperature data provides effective information and correct decision for food safety management. In this paper, the time-temperature data collection system is designed. The time-temperature data from the stages of processing, storage and transportation in the aquatic cold chain is collected. The typical data is studied to identify the characteristics, and the classification rules are extracted. An optimized architecture for automatic classification combining data-mining and data fusion is established. The results show that the time-temperature data can be automatically and accurately classified by using this method.