Giannakourou Maria, Taoukis Petros
Department of Food Science and Technology, University of West Attica, 12243 Athens, Greece.
Laboratory of Food Chemistry and Technology, School of Chemical Engineering, National Technical University of Athens, 15780 Athens, Greece.
Foods. 2020 Jun 2;9(6):714. doi: 10.3390/foods9060714.
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.
需要进行系统动力学建模来预测冷冻系统在低温动态条件下的行为。采用一种一步法程序，即在非线性算法中同时使用所有数据，来估计一级和二级模型的动力学参数。与传统的两步法相比，该方法能获得更精确的估计值，并且在实际保质期预测中可以引入计算出的参数不确定性，作为冷链优化的工具。此外，记录到实际分布/储存条件存在显著变异性，这也必须纳入动力学预测方案中。通过对冷冻青豆维生素C含量数据的分析，从理论上证明了该方法在计算动力学参数联合置信区间方面的适用性。通过一种双蒙特卡罗方案实施随机算法，该方案纳入了从冷链数据库获取的配送过程中的温度变异性。假设冷链配送期为130天，预测剩余保质期为93±110天，而根据保质期假设为180天。总体而言，通过所研究的理论案例分析，将模型参数的不确定性和冷链动态特性纳入了保质期评估，从而得出更符合实际的预测结果。
