Bernaerts K, Versyck K J, Van Impe J F
BioTeC-Bioprocess Technology and Control, Department of Food and Microbial Technology, Katholieke Universiteit Leuven, Belgium.
Int J Food Microbiol. 2000 Mar 10;54(1-2):27-38. doi: 10.1016/s0168-1605(99)00140-3.
It is generally known that accurate model building, i.e., proper model structure selection and reliable parameter estimation, constitutes an essential matter in the field of predictive microbiology, in particular, when integrating these predictive models in food safety systems. In this context, Versyck et al. (1999) have introduced the methodology of optimal experimental design techniques for parameter estimation within the field. Optimal experimental design focuses on the development of optimal input profiles such that the resulting rich (i.e., highly informative) experimental data enable unique model parameter estimation. As a case study, Versyck et al. (1999) [Versyck, K., Bernaerts, K., Geeraerd, A.H., Van Impe, J.F., 1999. Introducing optimal experimental design in predictive modeling: a motivating example. Int. J. Food Microbiol., 51(1), 39-51] have elaborated the estimation of Bigelow inactivation kinetics parameters (in a numerical way). Opposed to the classic (static) experimental approach in predictive modelling, an optimal dynamic experimental setup is presented. In this paper, the methodology of optimal experimental design or parameter estimation is applied to obtain uncorrelated estimates of the square root model parameters [Ratkowsky, D.A., Olley, J., McMeekin, T.A., Ball, A., 1982. Relationship between temperature and growth rate of bacterial cultures. J. Bacteriol. 149, 1-5] describing the effect of suboptimal growth temperatures on the maximum specific growth rate of microorganisms. These estimates are the direct result of fitting a primary growth model to cell density measurements as a function of time. Apart from the design of an optimal time-varying temperature profile based on a sensitivity study of the model output, an important contribution of this publication is a first experimental validation of this innovative dynamic experimental approach for uncorrelated parameter identification. An optimal step temperature profile, within the range of model validity and practical feasibility, is developed for Escherichia coli K12 and successfully applied in practice. The presented experimental validation result illustrates the large potential of the dynamic experimental approach in the context of uncorrelated parameter estimation. Based on the experimental validation result, additional remarks are formulated related to future research in the field of optimal experimental design.
众所周知，准确的模型构建，即恰当的模型结构选择和可靠的参数估计，是预测微生物学领域的关键问题，尤其是在将这些预测模型集成到食品安全系统中时。在这种背景下，Versyck等人（1999年）引入了该领域内用于参数估计的最优实验设计技术方法。最优实验设计专注于开发最优输入曲线，以便由此产生的丰富（即信息量大）实验数据能够实现唯一的模型参数估计。作为一个案例研究，Versyck等人（1999年）[Versyck, K., Bernaerts, K., Geeraerd, A.H., Van Impe, J.F., 1999. 在预测建模中引入最优实验设计：一个激励性示例。《国际食品微生物学杂志》，51(1)，39 - 51]详细阐述了Bigelow失活动力学参数的估计（以数值方式）。与预测建模中的经典（静态）实验方法不同，本文提出了一种最优动态实验设置。在本文中，最优实验设计或参数估计方法被应用于获得平方根模型参数的不相关估计值[Ratkowsky, D.A., Olley, J., McMeekin, T.A., Ball, A., 1982. 细菌培养物温度与生长速率之间的关系。《细菌学杂志》149，1 - 5]，该模型描述了次优生长温度对微生物最大比生长速率的影响。这些估计值是将初级生长模型拟合到作为时间函数的细胞密度测量值的直接结果。除了基于对模型输出的敏感性研究设计最优时变温度曲线外，本出版物的一个重要贡献是首次对这种用于不相关参数识别的创新动态实验方法进行了实验验证。针对大肠杆菌K12，在模型有效性和实际可行性范围内开发了最优阶跃温度曲线，并成功应用于实际。所呈现的实验验证结果说明了动态实验方法在不相关参数估计方面的巨大潜力。基于实验验证结果，针对最优实验设计领域的未来研究提出了更多的见解。
