Comparing uncertainty resulting from two-step and global regression procedures applied to microbial growth models
Comparing uncertainty resulting from two-step and global regression procedures applied to microbial growth models
复制标题
DOI：
10.4315/0362-028x-70.12.2811
复制
发表时间：
2007-12-01
影响因子：
2
通讯作者：
Marks, B. P.
中科院分区：
文献类型：
--
作者：
Martino, K. G.;Marks, B. P.
根据单核细胞增生李斯特菌生长的独立数据对两种不同的微生物建模程序进行了比较和验证。最常用的方法是两次连续回归：根据微生物计数的初级回归估计生长参数，以及将生长参数与实验条件相关联的二次回归。全局回归是一种将初级模型和次级模型相结合的替代方法，给出了实验因素和微生物计数之间的直接关系。 Gompertz 方程是主要模型，响应面模型是辅助模型。来自肉类和家禽产品的独立数据用于验证建模程序。全局回归产生较低的校准标准误差，有氧条件下为 0.95 log CFU/ml，厌氧条件下为 1.21 log CFU/ml。两步程序在需氧条件下产生的误差为 1.35 log CFU/ml，在厌氧条件下产生的误差为 1.62 log CFU/ml。对于食品，对于 65% 的研究案例，全局回归比两步程序更加稳健。全局回归的稳健性指数范围为 0.27（表现好于预期）到 2.60。对于两步法，稳健性指数范围为0.42至3.88。在超过 50% 的使用全局回归的案例和超过 70% 的使用两步回归的案例中，预测被高估（故障安全）。总体而言，对于该特定应用，全局回归比两步程序表现更好。
Two different microbial modeling procedures were compared and validated against independent data for Listeria monocytogenes growth. The most generally used method is two consecutive regressions: growth parameters are estimated from a primary regression of microbial counts, and a secondary regression relates the growth parameters to experimental conditions. A global regression is an alternative method in which the primary and secondary models are combined, giving a direct relationship between experimental factors and microbial counts. The Gompertz equation was the primary model, and a response surface model was the secondary model. Independent data from meat and poultry products were used to validate the modeling procedures. The global regression yielded the lower standard errors of calibration, 0.95 log CFU/ml for aerobic and 1.21 log CFU/ml for anaerobic conditions. The two-step procedure yielded errors of 1.35 log CFU/ml for aerobic and 1.62 log CFU/ ml for anaerobic conditions. For food products, the global regression was more robust than the two-step procedure for 65% of the cases studied. The robustness index for the global regression ranged from 0.27 (performed better than expected) to 2.60. For the two-step method, the robustness index ranged from 0.42 to 3.88. The predictions were overestimated (fail safe) in more than 50% of the cases using the global regression and in more than 70% of the cases using the two-step regression. Overall, the global regression performed better than the two-step procedure for this specific application.