Growth and predictive model of Bacillus cereus on blanched spinach with or without seasoning at various temperatures

Abstract

The growth of Bacillus cereus on blanched spinach with or without seasoning at various temperatures (15,20,25,30, and 35 o C) was investigated. The number of B. cereus on blanched spinach stored at 35 o C was significantly increased and resulted in maximum populations (7.8 log CFU/g) after 10 h. However, the growth rate of B. cereus on blanched spinach with seasoning stored at 35 o C was lower than on blanched spinach without seasoning. The growth rate (GR) of B. cereus on blanched spinach stored at 15 o C was lower than the other temperatures. The lag time (LT) of B. cereus on blanched spinach with seasoning was longer than blanched spinach without seasoning. Primary growth models of B. cereus on blanched spinach fit well to a modified Gompertz model (blanched spinach R 2 =0.98; blanched spinach with seasoning R 2 =0.96). These results might be used for developing safe storage guidelines for cooked vegetable at various temperatures.

Response surface modeling for the inactivation of Bacillus cereus on cooked spinach by natural antimicrobials at various temperatures

“…Several studies also reported strong antibacterial activities of natural antimicrobial substances such as thymol, acetic acid, and nisin against pathogens (Ettayebi, Yamani, & Rossi‐Hassani, ; Fang & Hsueh, ; Zhou et al, ) and spoilage bacteria (Zheng et al, ). Bae et al () showed that growth of B. cereus increased to 7.8 log CFU/g on cooked spinach after storage for 10 hr at 35°C, but the initial levels (1.0–1.5 log CFU/g) of B. cereus were maintained for 9 hr at 15°C. Some studies showed that the combination of natural antimicrobial substances such as thymol, acetic acid, and nisin results in synergistic effects than natural antimicrobial substances alone against foodborne pathogens.…”

Predictive models for the growth of Salmonella spp., Listeria spp., and Escherichia coli in lettuce harvested on Taiwanese farms

“…The modified Ratkowsky equation was used to describe the effect of temperature on μ (Ratkowsky et al., 1982): $$\begin{equation}\sqrt {{\mu }_{\max }} = b\left( {T - {T}_{\min }} \right),\end{equation}$$where b is a constant coefficient, T is the temperature and $${T}_{\min }$$ is the conceptual minimum temperature for microbial growth. These models have been widely used to describe the bacterial behavior in various types of vegetables (Bae et al., 2012; Jayeola et al., 2019; Koseki & Isobe, 2005b; Yoon et al., 2014).…”

“…The performance of the developed predictive models was evaluated using the adjusted coefficient of determination ($$R_{adj}^2$$), root mean square error (RMSE), bias factor ($${B}_f$$), and accuracy factor ($${A}_f$$) (Bae et al., 2012; Jayeola et al., 2019; Yoon et al., 2014): $$\begin{equation}R_{adj}^2 = 1 - \frac{{\left( {1 - {R}^2} \right)\left( {n - 1} \right)}}{{n - p - 1}}\end{equation}$$ $$\begin{equation}{R}^2 = 1 - \frac{{{{\sum {\left( {{y}_i - {{\hat{y}}}_i} \right)} }}^2}}{{\sum {{{\left( {{y}_i - \bar{y}} \right)}}^2} }}\end{equation}$$ $$\begin{equation}RMSE = \sqrt {\frac{{\sum\nolimits_{i = 1}^n {{{({y}_i - \hat{y})}}^2} }}{n}} \end{equation}$$ $$\begi...$$…”

“…where 𝑏 is a constant coefficient, 𝑇is the temperature and 𝑇 min is the conceptual minimum temperature for microbial growth. These models have been widely used to describe the bacterial behavior in various types of vegetables (Bae et al, 2012;Jayeola et al, 2019;Koseki & Isobe, 2005b;Yoon et al, 2014). The performance of the developed predictive models was evaluated using the adjusted coefficient of determination (𝑅 2 𝑎𝑑𝑗 ), root mean square error (RMSE), bias factor (𝐵 𝑓 ), and accuracy factor (𝐴 𝑓 ) (Bae et al, 2012;Jayeola et al, 2019;Yoon et al, 2014):…”

Model of Fungal Development in Stored Barley Ecosystems as a Prognostic Auxiliary Tool for Postharvest Preservation Systems

“…The values of B f obtained for the OPM MG as a result of internal and external validation are given in Table 5. Bae et al (2012) indicated that the range of bias, for which the model can be considered as good, is 0.90-1.05. The same authors reported that models with a bias in the range of 0.70-0.90 or 1.06-1.15 can be considered sufficient, whilst a bias of less than 0.70 or greater than 1.15 indicates a model which cannot be accepted.…”