葡萄冷链品质的时间-温度指示器模糊推理预测

为了验证和评价变温环境下时间-温度指示器(TTI)响应值预测农产品品质的适用性,构建了TTI模糊推理预测方法。TTI模糊推理预测是依据拟合程度高的恒温试验农产品品质实际变化经验方程,以及尽可能准确描述任意有效温度与恒温温度之间关系的隶属度函数构建预测模型,实现对任意有效温度下农产品品质预测值计算。同时设置了高低温变温试验模拟鲜食葡萄冷链物流温度特征,用上述方法对Vitsab M25-2、OnVu TTI预测玫瑰香葡萄硬度进行了参数估计与模型建立,并与TTI动力学预测值进行了对比。结果表明,面向鲜食葡萄品质感知的TTI模糊推理预测在低温下相对于TTI动力学预测有所改进,平均相对偏差分别减小了6.03个百分点和2.70个百分点;在高温下没有改进。因此在低温下可选择模糊推理预测方法。

Time-Temperature Indicator Fuzzy Reasoning Prediction for Grape Cold Chain Quality Sensing

With the aim to validate and accurately evaluate the applicability of time-temperature indicator (TTI) application at variable temperatures, a prediction method based on the fuzzy reasoning was built. The method was on the basis of quality experience equation of the monitored products at constant temperature experiment, which could be chosen from polynomial equation, the n-th reaction kinetic equations or other equations according to the fitting coefficients. The key of this method was to build exact membership functions between arbitrate effective temperature and the constant temperature in order to obtain the predicted value at arbitrate effective temperature. The method was analyzed about Muscat Hamburg grape, Vitsab M25-2 and OnVu TTI through two fluctuant temperature experiments simulating temperature characteristics of table grape cold chain logistics. Triangle membership function was chosen in the prediction based on the fuzzy reasoning. The table grape quality predicted values based on the fuzzy reasoning and the kinetics model were compared with the actual measured values. Results showed that the TTI prediction method based on fuzzy reasoning at low fluctuant temperature made improvements (6.03 percentage points and 2.70 percentage points) vs TTI prediction method based on the reaction kinetics equations, whereas made no improvements at high fluctuant temperature. Therefore, TTI prediction method based on fuzzy reasoning could be chosen at low temperature based on the principle of merit.