Systematic kinetic modeling is required to predict frozen systems behavior in cold dynamic conditions. A one-step procedure, where all data are used simultaneously in a non-linear algorithm, is implemented to estimate the kinetic parameters of both primary and secondary models. Compared to the traditional two-step methodology, more precise estimates are obtained, and the calculated parameter uncertainty can be introduced in realistic shelf life predictions, as a tool for cold chain optimization. Additionally, significant variability of the real distribution/storage conditions is recorded, and must be also incorporated in a kinetic prediction scheme. The applicability of the approach is theoretically demonstrated in an analysis of data on frozen green peas Vitamin C content, for the calculation of joint confidence intervals of kinetic parameters. A stochastic algorithm is implemented, through a double Monte Carlo scheme incorporating the temperature variability during distribution, drawn from cold chain databases. Assuming a distribution scenario of 130 days in the cold chain, 93 ± 110 days remaining shelf life was predicted compared to 180 days assumed based on the use by date. Overall, through the theoretical case study investigated, the uncertainty of models’ parameters and cold chain dynamics were incorporated into shelf life assessment, leading to more realistic predictions.

中文翻译：

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动态冷链条件下冷冻食品保质期预测不确定性的整体方法。

需要系统动力学建模来预测在寒冷动态条件下的冻结系统行为。执行一个步骤的程序，其中所有数据都在非线性算法中同时使用，该程序用于估计主要模型和次要模型的动力学参数。与传统的两步法相比，可以得到更精确的估计值，并且可以将计算出的参数不确定性引入实际的货架寿命预测中，作为冷链优化的工具。另外，记录了实际分配/存储条件的显着可变性，并且还必须将其纳入动力学预测方案中。该理论的适用性在冷冻豌豆维生素C含量数据分析中得到了理论证明，用于计算动力学参数的联合置信区间。随机算法是通过双重蒙特卡洛方案实现的，该方案结合了从冷链数据库中得出的配电过程中的温度变化。假设在冷链中的分配场景为130天，则预计剩余保质期为93±110天，而根据日期使用情况假定的保质期为180天。总体而言，通过调查的理论案例研究，将模型参数的不确定性和冷链动力学纳入了货架寿命评估，从而得出了更为现实的预测。预计剩余保质期为93±110天，而基于日期使用的假定保质期为180天。总体而言，通过调查的理论案例研究，将模型参数的不确定性和冷链动力学纳入了货架寿命评估，从而得出了更为现实的预测。预计剩余保质期为93±110天，而基于日期使用的假定保质期为180天。总体而言，通过调查的理论案例研究，将模型参数的不确定性和冷链动力学纳入了货架寿命评估，从而得出了更为现实的预测。