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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Publications at this Location » Publication #405279

Research Project: Advanced Methods for Predictive Modeling of Bacterial Growth and Survival in Foods

Location: Residue Chemistry and Predictive Microbiology Research

Title: Shelf-life boundaries of listeria monocytogenes in cold smoked salmon during refrigerated storage and temperature abuse

Author
item Huang, Lihan
item Hwang, Cheng An
item Sheen, Shiowshuh - Allen

Submitted to: Food Research International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/5/2023
Publication Date: N/A
Citation: N/A

Interpretive Summary: Cold smoked salmon is a popular seafood product that can be contaminated by Listeria monocytogenes. This study was conducted to evaluate the growth and survival of L. monocytogenes during refrigerated storage and under temperature abuse. A mathematical model was developed to estimate the shelf-life according to different control criteria based on storage time and temperature. This model can be used to adopt a risk-based approach to manage the safety of cold smoked salmon and prevent foodborne listeriosis.

Technical Abstract: Cold smoked salmon (CSS) is a high-value ready-to-eat seafood product, but it generally has a short shelf life even under refrigeration and can support the growth of certain foodborne pathogens, such as Listeria monocytogenes. Therefore, the objective of this study was to examine the growth and survival of L. monocytogenes in CSS during refrigerated storage and temperature abuse. The growth and survival data of L. monocytogenes (116 records, 465 data points) were retrieved from ComBase (www.combase.cc). All records contained storage time and temperature, but other information, such as aw, pH, and salt, was incomplete. Each data point was normalized with the initial population to calculate relative growth (RG, log CFU/g), which was then used to classify the probability of growth. A test data set, containing 80% of the entire data set, was randomly sampled for analyzing the growth probability as affected by storage time and temperature, while the remaining data were set aside for model validation. Logistic regression was used to develop a model for classifying L. monocytogenes growth according to 7 different control thresholds (CT), ranging from 0 to 3 log CFU/g in RG. A probability threshold for the model was then set to judge if a CSS product has exceeded a CT due to growth of L. monocytogenes. The model was validated and showed > 89% of true negative rate for not exceeding the control thresholds. A dynamic method was then developed to predict the growth probabilities under fluctuating temperature conditions. The result of this study suggested that storage time and temperature could be used to predict the growth of L. monocytogenes in CSS and to control listeriosis using a risk-based strategy.