Applied Microbiology Group, Cranfield Health Cranfield University, Cranfield MK43 0AL, UK.
Int J Food Microbiol. 2012 Mar 15;154(3):169-76. doi: 10.1016/j.ijfoodmicro.2011.12.035. Epub 2012 Jan 4.
A fundamental aspect of predictive microbiology is the shape of the microbial growth curve and many models are used to fit microbial count data, the modified Gompertz and Baranyi equation being two of the most widely used. Rapid, automated methods such as turbidimetry have been widely used to obtain growth parameters, but do not directly give the microbial growth curve. Optical density (OD) data can be used to obtain the specific growth rate and if used in conjunction with the known initial inocula, the maximum population data and knowledge of the microbial number at a predefined OD at a known time then all the information required for the reconstruction of a standard growth curve can be obtained. Using multiple initial inocula the times to detection (TTD) at a given standard OD were obtained from which the specific growth rate was calculated. The modified logistic, modified Gompertz, 3-phase linear, Baranyi and the classical logistic model (with or without lag) were fitted to the TTD data. In all cases the modified logistic and modified Gompertz failed to reproduce the observed linear plots of the log initial inocula against TTD using the known parameters (initial inoculum, MPD and growth rate). The 3 phase linear model (3PLM), Baranyi and classical logistic models fitted the observed data and were able to reproduce elements of the OD incubation-time curves. Using a calibration curve relating OD and microbial numbers, the Baranyi equation was able to reproduce OD data obtained for Listeria monocytogenes at 37 and 30°C as well as data on the effect of pH (range 7.05 to 3.46) at 30°C. The Baranyi model was found to be the most capable primary model of those examined (in the absence of lag it defaults to the classic logistic model). The results suggested that the modified logistic and the modified Gompertz models should not be used as Primary models for TTD data as they cannot reproduce the observed data.
预测微生物学的一个基本方面是微生物生长曲线的形状，许多模型被用于拟合微生物计数数据，其中修正的 Gompertz 和 Baranyi 方程是最广泛使用的两种。快速、自动化的方法，如比浊法，已被广泛用于获得生长参数，但不能直接给出微生物生长曲线。光密度（OD）数据可用于获得比生长速率，如果与已知的初始接种物结合使用，以及已知的微生物数量的最大种群数据和在给定的 OD 下的已知时间点的微生物数量，则可以获得重建标准生长曲线所需的所有信息。使用多个初始接种物，可以从给定的标准 OD 获得检测时间（TTD），并从中计算出比生长速率。将修正的 logistic、修正的 Gompertz、3 相线性、Baranyi 和经典 logistic 模型（带或不带滞后）拟合到 TTD 数据。在所有情况下，修正的 logistic 和修正的 Gompertz 都无法使用已知参数（初始接种物、MPD 和生长速率）再现观察到的线性图，该线性图是对数初始接种物与 TTD 的关系。3 相线性模型（3PLM）、Baranyi 和经典 logistic 模型拟合了观察到的数据，并能够再现 OD 孵育时间曲线的元素。使用将 OD 与微生物数量相关联的校准曲线，Baranyi 方程能够再现李斯特菌在 37 和 30°C 下的 OD 数据以及 30°C 下 pH（范围 7.05 至 3.46）的影响数据。结果表明，在没有滞后的情况下，Baranyi 模型是所检查的主要模型中最有能力的模型（它默认为经典 logistic 模型）。结果表明，修正的 logistic 和修正的 Gompertz 模型不应用于 TTD 数据的主要模型，因为它们不能再现观察到的数据。
