Schaffner D W
Food Science Department, Rutgers State University of New Jersey, New Brunswick 08903.
Int J Food Microbiol. 1994 Dec;24(1-2):309-14. doi: 10.1016/0168-1605(94)90128-7.
The inherent variability or 'variance' of growth rate measurements is critical to the development of accurate predictive models in food microbiology. A large number of measurements are typically needed to estimate variance. To make these measurements requires a significant investment of time and effort. If a single growth rate determination is based on a series of independent measurements, then a statistical bootstrapping technique can be used to simulate multiple growth rate measurements from a single set of experiments. Growth rate variances were calculated for three large datasets (Listeria monocytogenes, Listeria innocua, and Yersinia enterocolitica) from our laboratory using this technique. This analysis revealed that the population of growth rate measurements at any given condition are not normally distributed, but instead follow a distribution that is between normal and Poisson. The relationship between growth rate and temperature was modeled by response surface models using generalized linear regression. It was found that the assumed distribution (i.e. normal, Poisson, gamma or inverse normal) of the growth rates influenced the prediction of each of the models used. This research demonstrates the importance of variance and assumptions about the statistical distribution of growth rates on the results of predictive microbiological models.
生长速率测量的固有变异性或“方差”对于食品微生物学中准确预测模型的开发至关重要。通常需要大量测量来估计方差。进行这些测量需要投入大量的时间和精力。如果单个生长速率测定基于一系列独立测量，则可以使用统计自展技术从一组实验中模拟多个生长速率测量。使用该技术对我们实验室的三个大型数据集（单核细胞增生李斯特菌、无害李斯特菌和小肠结肠炎耶尔森菌）计算了生长速率方差。该分析表明，在任何给定条件下的生长速率测量总体并非呈正态分布，而是遵循介于正态分布和泊松分布之间的一种分布。使用广义线性回归通过响应面模型对生长速率与温度之间的关系进行了建模。结果发现，生长速率的假定分布（即正态分布、泊松分布、伽马分布或逆正态分布）会影响所使用的每个模型的预测。这项研究证明了方差以及关于生长速率统计分布的假设对预测微生物模型结果的重要性。
