The Most Probable Curve method - A robust approach to estimate kinetic models from low plate count data resulting in reduced uncertainty

A novel method is proposed for fitting microbial inactivation models to data on liquid media: the Most Probable Curve (MPC) method. It is a multilevel model that makes a separation between the “true” microbial concentration according to the model, the “actual” concentration in the media considering...

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Bibliographic Details
Published in:International journal of food microbiology Vol. 380; p. 109871
Main Authors: Garre, Alberto, Zwietering, Marcel H., van Boekel, Martinus A.J.S.
Format: Journal Article
Language:English
Published: Elsevier B.V 02.11.2022
Subjects:
Bayesian statistics
food microbiology
Gamma-Poisson regression
Kinetic modelling
kinetics
liquids
Listeria monocytogenes
Microbial inactivation
Pasteurization
plate count
Poisson distribution
Poisson regression
Predictive microbiology
regression analysis
Robust statistics
stress tolerance
uncertainty
Robust statistics
Bayesian statistics
Predictive microbiology
Kinetic modelling
Pasteurization
Gamma-Poisson regression
Microbial inactivation
Poisson regression
ISSN:0168-1605, 1879-3460, 1879-3460
Online Access:Get full text
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Summary:A novel method is proposed for fitting microbial inactivation models to data on liquid media: the Most Probable Curve (MPC) method. It is a multilevel model that makes a separation between the “true” microbial concentration according to the model, the “actual” concentration in the media considering chance, and the actual counts on the plate. It is based on the assumptions that stress resistance is homogeneous within a microbial population, and that there is no aggregation of microbial cells. Under these assumptions, the number of colonies in/on a plate follows a Poisson distribution with expected value depending on the proposed kinetic model, the number of dilutions and the plated volume. The novel method is compared against (non)linear regression based on a normal likelihood distribution (traditional method), Poisson regression and gamma-Poisson regression using data on the inactivation of Listeria monocytogenes. The conclusion is that the traditional method has limitations when the data includes plates with low (or zero) cell counts, which can be mitigated using more complex (discrete) likelihoods. However, Poisson regression uses an unrealistic likelihood function, making it unsuitable for survivor curves with several log-reductions. Gamma-Poisson regression uses a more realistic likelihood function, even though it is based mostly on empirical hypotheses. We conclude that the MPC method can be used reliably, especially when the data includes plates with low or zero counts. Furthermore, it generates a more realistic description of uncertainty, integrating the contribution of the plating error and reducing the uncertainty of the primary model parameters. Consequently, although it increases modelling complexity, the MPC method can be of great interest in predictive microbiology, especially in studies focused on variability analysis. •Models fitted using normal likelihood functions have limitations for low plate counts.•Poisson regression is unsuitable to describe survivor curves with multiple log-reductions.•Gamma-Poisson provides a suitable empirical extension of Poisson regression.•The Most Probable Curve (MPC) method estimates kinetic parameters from low plate counts reliably.•The MPC method can integrate the uncertainty resulting from serial dilutions and plating.
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ISSN:0168-1605
1879-3460
1879-3460
DOI:10.1016/j.ijfoodmicro.2022.109871