[Paper Review] Application of an interspecific competition model to predict the growth of Aeromonas hydrophila on fish surfaces during the refrigerated storage

Alessandro Giuffrida, Graziella Ziino+3 more|ArXiv.org|Nov 17, 2008

Listeria monocytogenes in Food Safety29 references18 citations

TL;DR

This study applies the Lotka-Volterra interspecific competition model to predict the growth of *Aeromonas hydrophila* and natural aerobic flora (APC) on gilthead seabream during refrigerated storage (21 days). By incorporating bacterial competition coefficients and environmental fluctuations, the model significantly improved prediction accuracy (RMSE: 0.09 for *A. hydrophila*, 0.28 for APC) compared to conventional models.

ABSTRACT

The growth of Aeromonas hydrophila and aerobic natural flora (APC) on gilthead seabream surfaces was evaluated during the refrigerated storage (21 days). The related growth curves were compared with those obtained by a conventional third order predictive model obtaining a low agreement between observed and predicted data (Root Mean Squared Error = 1.77 for Aeromonas hydrophila and 0.64 for APC). The Lotka-Volterra interspecific competition model was used in order to calculate the degree of interaction between the two bacterial populations (β_{Ah/APC} and β{APC/Ah}, respectively, the interspecific competition coefficients of APC on Aeromonas hydrophila and vice-versa). Afterwards, the Lotka-Volterra equations were applied as tertiary predictive model, taking into account, simultaneously, the environmental fluctuations and the bacterial interspecific competition. This approach allowed to obtain a best fitting to the observed mean growth curves with a Root Mean Squared Error of 0.09 for Aeromonas hydrophila and 0.28 for APC. Finally, authors carry out some considerations about the necessary use of competitive models in the context of the new trends in predictive microbiology.

Motivation & Objective

• To improve prediction accuracy of *Aeromonas hydrophila* and aerobic natural flora (APC) growth on fish surfaces during refrigerated storage.
• To address the limitations of conventional predictive models that fail to account for microbial interactions.
• To evaluate the impact of interspecific competition between *A. hydrophila* and APC on growth dynamics.
• To develop a tertiary predictive model integrating environmental fluctuations and competitive interactions.
• To support emerging trends in predictive microbiology by emphasizing the necessity of competitive models.

Proposed method

• Measured growth curves of *Aeromonas hydrophila* and APC on gilthead seabream over 21 days at refrigerated conditions.
• Calculated interspecific competition coefficients (β_Ah/APC and β_APC/Ah) using the Lotka-Volterra model to quantify mutual inhibition.
• Applied the Lotka-Volterra equations as a tertiary predictive model, incorporating both environmental fluctuations and competition effects.
• Validated model predictions against observed growth data using root mean squared error (RMSE) as the performance metric.
• Compared model performance against a conventional third-order predictive model to assess improvement in accuracy.

Experimental results

Research questions

• RQ1 How do interspecific competition coefficients between *Aeromonas hydrophila* and APC affect their growth dynamics on fish surfaces?
• RQ2 To what extent does incorporating microbial competition improve prediction accuracy compared to conventional models?
• RQ3 Can the Lotka-Volterra model effectively simulate the growth of *A. hydrophila* and APC under fluctuating refrigerated storage conditions?
• RQ4 What is the impact of environmental fluctuations on the predictive performance of competition-based models?
• RQ5 How does the inclusion of competitive interactions align with current trends in predictive microbiology?

Key findings

• The conventional third-order model showed poor agreement with observed data, with RMSE values of 1.77 for *A. hydrophila* and 0.64 for APC.
• The Lotka-Volterra interspecific competition model significantly improved prediction accuracy, achieving an RMSE of 0.09 for *A. hydrophila* and 0.28 for APC.
• The model successfully captured the dynamic interaction between *A. hydrophila* and APC, indicating strong mutual inhibition during refrigerated storage.
• The competition coefficients (β_Ah/APC and β_APC/Ah) quantified the extent of suppression each population exerted on the other.
• The tertiary model incorporating both environmental fluctuations and competition effects provided a more realistic and accurate simulation of microbial growth.
• The study demonstrates that competitive models are essential for accurate predictive microbiology in complex food systems.