Strain-specific Growth Parameters are Important to Accurately Model Bacterial Growth on Baby Spinach in Simulation Models.

Autor: Sunil S; Department of Food Science, Cornell University, Ithaca, NY 14853, USA., Murphy SI; Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, USA., Orsi RH; Department of Food Science, Cornell University, Ithaca, NY 14853, USA., Ivanek R; Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, USA., Wiedmann M; Department of Food Science, Cornell University, Ithaca, NY 14853, USA. Electronic address: martin.wiedmann@cornell.edu.
Jazyk: angličtina
Zdroj: Journal of food protection [J Food Prot] 2024 May; Vol. 87 (5), pp. 100270. Date of Electronic Publication: 2024 Mar 27.
DOI: 10.1016/j.jfp.2024.100270
Abstrakt: Digital tools to predict produce shelf life have the potential to reduce food waste and improve consumer satisfaction. To address this need, we (i) performed an observational study on the microbial quality of baby spinach, (ii) completed growth experiments of bacteria that are representative of the baby spinach microbiota, and (iii) developed an initial simulation model of bacterial growth on baby spinach. Our observational data showed that the predominant genera found on baby spinach were Pseudomonas, Pantoea and Exiguobacterium. Rifampicin-resistant mutants (rif R mutants) of representative bacterial subtypes were subsequently generated to obtain strain-specific growth parameters on baby spinach. These experiments showed that: (i) it is difficult to select rif R mutants that do not have fitness costs affecting growth (9 of 15 rif R mutants showed substantial differences in growth, compared to their corresponding wild-type strain) and (ii) based on estimates from primary growth models, the mean (geometric) maximum population of rif R mutants on baby spinach (7.6 log 10 CFU/g, at 6°C) appears lower than that of the spinach microbiota (9.6 log 10 CFU/g, at 6°C), even if rif R mutants did not have substantial growth-related fitness costs. Thus, a simulation model, parameterized with the data obtained here as well as literature data on home refrigeration temperatures, underestimated bacterial growth on baby spinach. The root mean square error of the simulation's output, compared against data from the observational study, was 1.11 log 10 CFU/g. Sensitivity analysis was used to identify key parameters (e.g., strain maximum population) that impact the simulation model's output, allowing for prioritization of future data collection to improve the simulation model. Overall, this study provides a roadmap for the development of models to predict bacterial growth on leafy vegetables with strain-specific parameters and suggests that additional data are required to improve these models.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Martin Wiedmann reports a relationship with Neogen Corporation that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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