Modeling the growth of Bacillus cereus in Napa cabbage (Brassica rapa subsp. pekinensis) across different sea salt concentrations

Bacillus cereus is common in nature and can contaminate food through soil and air. Root vegetables like Napa cabbage are especially prone to soil contamination. B. cereus toxins are mainly linked to ready-to-eat foods, such as raw napa cabbage, which poses a risk when consumed salted. This study pro...

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Main Authors: Eun Bi Jeon, Sung-Hee Park, Shin Young Park
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Applied Food Research
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Online Access:http://www.sciencedirect.com/science/article/pii/S2772502224002701
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author Eun Bi Jeon
Sung-Hee Park
Shin Young Park
author_facet Eun Bi Jeon
Sung-Hee Park
Shin Young Park
author_sort Eun Bi Jeon
collection DOAJ
description Bacillus cereus is common in nature and can contaminate food through soil and air. Root vegetables like Napa cabbage are especially prone to soil contamination. B. cereus toxins are mainly linked to ready-to-eat foods, such as raw napa cabbage, which poses a risk when consumed salted. This study proposes growth models for B. cereus growing on Napa cabbage as a function of sea salt concentrations (4–14 %). Samples were stored at 10 °C and analyzed at intervals from 2 to 120 h to determine bacterial growth rates at different salt concentrations. At these sea salt concentrations, the primary models fit well (R2 = 0.98) with the Baranyi model, using a polynomial model to obtain lag time (LT) and specific growth rate (SGR). However, as sea salt concentration increased, the growth of B. cereus decreased. Specifically, no B. cereus growth was observed at concentrations of 12 and 14 %. The LT values were 1.87, 2.87, and 6.04 h, and the SGR values were 0.21, 0.17, and 0.09 log CFU/h at sea salt concentrations of 4, 8, and 10 %, respectively. The suitability of the secondary models for LT and SGR was verified using the mean square error (MSE; <0.01, internal validation, <0.01), bias factor (Bf: 1.043–1.214 internal validation, 1.046–1.086 external validation), and accuracy factor (Af; 1.136–1.113 internal validation, 1.147–1.085 external validation). Ultimately, these models can be applied to improve the safety of various cabbage-based products (e.g., Kimchi).
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spelling doaj-art-9232b8f741804ba0bf3ba4ba005ace2d2024-12-22T05:30:03ZengElsevierApplied Food Research2772-50222025-06-0151100660Modeling the growth of Bacillus cereus in Napa cabbage (Brassica rapa subsp. pekinensis) across different sea salt concentrationsEun Bi Jeon0Sung-Hee Park1Shin Young Park2Department of Seafood Science and Technology, Institute of Marine Industry, Gyeongsang National University, Tongyeong, 53064, KoreaPractical Technology Research Group, World Institute of Kimchi, Gwangju, 61755, Republic of KoreaDepartment of Seafood Science and Technology, Institute of Marine Industry, Gyeongsang National University, Tongyeong, 53064, Korea; Corresponding author at: Department of Seafood Science and Technology, Gyeongsang National University, Tongyeong Haean-ro, Tongyeong, Gyeongsangnam-do, 53064, Republic of Korea.Bacillus cereus is common in nature and can contaminate food through soil and air. Root vegetables like Napa cabbage are especially prone to soil contamination. B. cereus toxins are mainly linked to ready-to-eat foods, such as raw napa cabbage, which poses a risk when consumed salted. This study proposes growth models for B. cereus growing on Napa cabbage as a function of sea salt concentrations (4–14 %). Samples were stored at 10 °C and analyzed at intervals from 2 to 120 h to determine bacterial growth rates at different salt concentrations. At these sea salt concentrations, the primary models fit well (R2 = 0.98) with the Baranyi model, using a polynomial model to obtain lag time (LT) and specific growth rate (SGR). However, as sea salt concentration increased, the growth of B. cereus decreased. Specifically, no B. cereus growth was observed at concentrations of 12 and 14 %. The LT values were 1.87, 2.87, and 6.04 h, and the SGR values were 0.21, 0.17, and 0.09 log CFU/h at sea salt concentrations of 4, 8, and 10 %, respectively. The suitability of the secondary models for LT and SGR was verified using the mean square error (MSE; <0.01, internal validation, <0.01), bias factor (Bf: 1.043–1.214 internal validation, 1.046–1.086 external validation), and accuracy factor (Af; 1.136–1.113 internal validation, 1.147–1.085 external validation). Ultimately, these models can be applied to improve the safety of various cabbage-based products (e.g., Kimchi).http://www.sciencedirect.com/science/article/pii/S2772502224002701Bacillus cereusNapa cabbagePredictive growth modelsSea salt concentrations
spellingShingle Eun Bi Jeon
Sung-Hee Park
Shin Young Park
Modeling the growth of Bacillus cereus in Napa cabbage (Brassica rapa subsp. pekinensis) across different sea salt concentrations
Applied Food Research
Bacillus cereus
Napa cabbage
Predictive growth models
Sea salt concentrations
title Modeling the growth of Bacillus cereus in Napa cabbage (Brassica rapa subsp. pekinensis) across different sea salt concentrations
title_full Modeling the growth of Bacillus cereus in Napa cabbage (Brassica rapa subsp. pekinensis) across different sea salt concentrations
title_fullStr Modeling the growth of Bacillus cereus in Napa cabbage (Brassica rapa subsp. pekinensis) across different sea salt concentrations
title_full_unstemmed Modeling the growth of Bacillus cereus in Napa cabbage (Brassica rapa subsp. pekinensis) across different sea salt concentrations
title_short Modeling the growth of Bacillus cereus in Napa cabbage (Brassica rapa subsp. pekinensis) across different sea salt concentrations
title_sort modeling the growth of bacillus cereus in napa cabbage brassica rapa subsp pekinensis across different sea salt concentrations
topic Bacillus cereus
Napa cabbage
Predictive growth models
Sea salt concentrations
url http://www.sciencedirect.com/science/article/pii/S2772502224002701
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