Buss da Silva Nathália, Baranyi József, Carciofi Bruno A M, Ellouze Mariem
Department of Chemical and Food Engineering, Federal University of Santa CatarinaFlorianópolis, Brazil.
Nestlé Research CenterLausanne, Switzerland.
Front Microbiol. 2017 Sep 21;8:1799. doi: 10.3389/fmicb.2017.01799. eCollection 2017.
Predictive models of the growth of foodborne organisms are commonly based on data generated in laboratory medium. It is a crucial question how to apply the predictions to realistic food scenarios. A simple approach is to assume that the bias factor, i.e., the ratio between the maximum specific growth rate in culture medium and the food in question is constant in the region of interest of the studied environmental variables. In this study, we investigate the validity of this assumption using two well-known link functions, the square-root and the natural logarithm, both having advantageous properties when modeling the variation of the maximum specific growth rate with temperature. The main difference between the two approaches appears in terms of the respective residuals as the temperature decreases to its minimum. The model organism was . Three strains (B594, B596, and F4810/72) were grown in Reconstituted Infant Formulae, while one of them (F4810/72) was grown also in culture medium to calculate the bias factor. Their growth parameters were estimated using viable count measurements at temperatures ranging from 12 to 25°C. We utilized the fact that, if the bias factor is independent of the temperature, then the minimum growth temperature parameter of the square-root model of Ratkowsky et al. (1982) is the same for culture medium and food. We concluded, supported also by mathematical analysis, that the Ratkowsky model works well but its rearrangement for the natural logarithm of the specific growth rate is more appropriate for practical regression. On the other hand, when analyzing mixed culture data, available in the ComBase database, we observed a trend different from the one generated by pure cultures. This suggests that the identity of the strains dominating the growth of mixed cultures depends on the temperature. Such analysis can increase the accuracy of predictive models, based on culture medium, to food scenarios, bringing significant saving for the food industry.
食源性病原体生长的预测模型通常基于实验室培养基中生成的数据。如何将这些预测应用于实际的食品场景是一个关键问题。一种简单的方法是假设偏差因子，即培养基中的最大比生长速率与所研究食品之间的比率，在所研究环境变量的感兴趣区域内是恒定的。在本研究中，我们使用两个著名的链接函数——平方根函数和自然对数函数来研究这一假设的有效性，这两个函数在模拟最大比生长速率随温度的变化时都具有优势。当温度降至最低时，两种方法的主要差异体现在各自的残差方面。模型生物是……三株菌株（B594、B596和F4810/72）在重构婴儿配方奶粉中培养，而其中一株（F4810/72）也在培养基中培养以计算偏差因子。使用在12至25°C温度范围内的活菌计数测量值来估计它们的生长参数。我们利用了这样一个事实，即如果偏差因子与温度无关，那么Ratkowsky等人（1982年）的平方根模型的最低生长温度参数对于培养基和食品是相同的。我们得出结论，数学分析也支持这一结论，即Ratkowsky模型效果良好，但其针对比生长速率自然对数的重新排列更适合实际回归。另一方面，在分析ComBase数据库中可用的混合培养数据时，我们观察到一种与纯培养产生的趋势不同的趋势。这表明主导混合培养生长的菌株身份取决于温度。这种分析可以提高基于培养基的预测模型对食品场景的准确性，为食品行业带来显著的节省。
