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Critical Assessment of the Time‐to‐Detection Method for Accurate Estimation of Microbial Growth Parameters
Author(s) -
Baka Maria,
Noriega Estefanía,
Stamati Ioanna,
Logist Filip,
Van Impe Jan F.M.
Publication year - 2015
Publication title -
journal of food safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.427
H-Index - 43
eISSN - 1745-4565
pISSN - 0149-6085
DOI - 10.1111/jfs.12170
Subject(s) - lag , sensitivity (control systems) , limit (mathematics) , estimation , calibration , estimation theory , lag time , range (aeronautics) , phase (matter) , computer science , selection (genetic algorithm) , statistics , bacterial growth , biological system , mathematics , engineering , chemistry , electronic engineering , biology , artificial intelligence , mathematical analysis , computer network , genetics , systems engineering , organic chemistry , bacteria , aerospace engineering
The time‐to‐detection ( TTD ) method is a rapid and high throughput approach for the estimation of microbial growth parameters (maximum specific growth rate μ max and lag phase duration λ), which relies on optical density ( OD ) measurements. The performance of this method depends on several factors that are often selected in an arbitrary way. In this work, a sensitivity analysis was performed to assess the effect of several key factors on the resulting output data of this method with L isteria monocytogenes . The factors showing higher influence on the results include (1) the calibration curve relating viable plate counts and OD data; (2) the approach to estimate TTD values; (3) the detection limit of OD measurements; and (4) the range of the initial cell concentrations considered ( N i ). In general, lag phase (λ) estimates were more sensitive than maximum specific growth rate (μ max ) estimates. The approach to estimate TTD values and the OD detection limit was the most influential factors for the μ max and λ estimation. This work has illustrated that, despite all the advantages of the TTD method, there are crucial steps in experimental design and data processing that significantly influence its output in terms of lag phase duration and maximum specific growth rate. Practical Applications This study contributes to understanding which are the most influential factors on the output of time‐to‐detection ( TTD ) method. These factors can be considered throughout different steps of the method, experimental design, data processing and growth parameter estimation. Guidelines are provided about how the selection of the factors assessed should be made and which variables – i.e., research scope, materials and equipment available for experimental and analytical steps – should be considered. In addition, some limitations of the method regarding the range and quality of the experimental data when implementing an automated global fitting are described. Overall, the performance of the TTD method and the accuracy of microbial growth parameters estimation will be improved.