Xu Jinchao, Ma Ruiqin, Stankovski Stevan, Liu Xue, Zhang Xiaoshuan
Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, China.
National Research Faculty for Phenotypic and Genotypic Analysis of Model Animals, China Agricultural University, Beijing 100083, China.
Foods. 2022 Mar 15;11(6):836. doi: 10.3390/foods11060836.
With the enhancement of consumers' food safety awareness, consumers have become more stringent on meat quality. This study constructs an intelligent dynamic prediction model based on knowledge rules and integrates flexible humidity sensors into the non-destructive monitoring of the Internet of Things to provide real-time feedback and dynamic adjustments for the chilled chicken cold chain. The optimized sensing equipment can be attached to the inside of the packaging to deal with various abnormal situations during the cold chain, effectively improving the packaging effect. Through correlation analysis of collected data and knowledge rule extraction of critical factors in the cold chain, the established quality evaluation and prediction model achieved detailed chilled chicken quality level classification and intelligent quality prediction. The obtained results show that the accuracy of the prediction model is higher than 90.5%, and all the regression coefficients are close to 1.00. The relevant personnel (workers and cold chain managers) were invited to participate in the performance analysis and optimization suggestion to improve the applicability of the established prediction model. The optimized model can provide a more efficient theoretical reference for timely decision-making and further e-commerce management.
随着消费者食品安全意识的提高，消费者对肉类品质的要求变得更加严格。本研究构建了基于知识规则的智能动态预测模型，并将柔性湿度传感器集成到物联网的无损监测中，为冷藏鸡肉冷链提供实时反馈和动态调整。优化后的传感设备可附着在包装内部，以应对冷链过程中的各种异常情况，有效提高包装效果。通过对收集数据的相关性分析和冷链关键因素的知识规则提取，所建立的质量评估和预测模型实现了对冷藏鸡肉质量水平的详细分类和智能质量预测。所得结果表明，预测模型的准确率高于90.5%，所有回归系数均接近1.00。邀请相关人员（工人和冷链管理人员）参与性能分析和优化建议，以提高所建立预测模型的适用性。优化后的模型可为及时决策和进一步的电子商务管理提供更有效的理论参考。
