Giarratana Filippo, Nalbone Luca, Ziino Graziella, Donato Giorgio, Marotta Stefania Maria, Lamberta Filippa, Giuffrida Alessandro
Department of Veterinary Sciences, University of Messina.
RICONNEXIA srls, Spin-off of the University of Messina, Polo Universitario dell'Annunziata, Messina, Italy.
Ital J Food Saf. 2022 Feb 22;11(1):9981. doi: 10.4081/ijfs.2022.9981.
This study aims to evaluate the behaviour of under fluctuating temperature comparing the efficacy of deterministic and stochastic methods for its prediction. In the first part of the study, a strain of was maintained at two different fluctuating temperature regimes both from 2 to 8°C and regularly sampled for the quantitative determination. The first temperature regime lasted 204 hours with a fluctuation length of 12 hours whereas the second lasted 167 hours with a fluctuation length of 24 hours. A dynamic predictive model was implemented for the reproduction of the observed data. Model resolution has been carried out by using values of the recorded temperature as well as the value of the mean temperature, the kinetic mean temperature, the 75 and 95 percentile of the temperature. A stochastic resolution was also performed considering the mean temperature and Standard Deviation as stochastic variable. In the second part of the study, a temperature mean curve was constructed by monitoring temperature of 8 refrigerated conveyances, 10 display cabinet and 15 domestic refrigerators. This curve was used to obtain predictive scenarios for based on the above and also considering temperature regime suggested by the EURL Lm TECHNICAL GUIDANCE DOCUMENT on challenge tests and durability studies for assessing shelf-life of ready-to-eat foods related to (Version 4 of 1 July 2021). All predicted behaviours were compared to the observed ones through the Root Mean Squared Error. Firstly, dynamic predictive model as well as the stochastic one, provided the best level of reproducibility of the observed data. The kinetic mean temperature reproduced the observed data better than the mean temperature for the 12 hoursregime while for the 24 hours-regime was the opposite. The 75 and 95 percentile overestimated the observed growths. Secondary, predictions obtained with the mean temperature, kinetic temperature and stochastic approach well fitted the observed data. The 75 and 95 percentile of Temperature and the "Eurl LM" temperature regimes overestimated the observed prediction. Dynamic approach as well as the stochastic one allowed to obtain the lowest values of Root Mean Squared Error. The mean temperature and kinetic mean temperature appeared the most representative values in a deterministic "single-point" approach.
本研究旨在评估在温度波动情况下的行为，比较确定性方法和随机方法对其进行预测的效果。在研究的第一部分，将一种菌株分别置于两种不同的温度波动模式下，温度范围均为2至8°C，并定期取样进行定量测定。第一种温度模式持续204小时，波动时长为12小时，而第二种持续167小时，波动时长为24小时。实施了一个动态预测模型来再现观测数据。通过使用记录温度的值以及平均温度、动态平均温度、温度的75%和95%分位数的值来进行模型解析。还进行了随机解析，将平均温度和标准差视为随机变量。在研究的第二部分，通过监测8辆冷藏运输工具、10个展示柜和15台家用冰箱的温度，构建了一条平均温度曲线。利用这条曲线，基于上述因素并考虑欧洲参考实验室（EURL）Lm关于即食食品保质期评估的挑战试验和耐久性研究的技术指导文件（2021年7月1日第4版）建议的温度模式，获得了该菌株的预测情景。通过均方根误差将所有预测行为与观测行为进行比较。首先，动态预测模型和随机模型都提供了观测数据的最佳再现水平。对于12小时模式，动态平均温度比平均温度能更好地再现观测数据，而对于24小时模式则相反。75%和95%分位数高估了观测到的生长情况。其次，用平均温度、动态温度和随机方法获得的预测结果与观测数据拟合良好。温度的75%和95%分位数以及“Eurl LM”温度模式高估了观测到的预测结果。动态方法和随机方法都能得到最低的均方根误差值。在确定性的“单点”方法中，平均温度和动态平均温度似乎是最具代表性的值。
