Modeling and predicting the effect of temperature on the growth of Proteus mirabilis in chicken

Zhao, Jingjing; Gao, Jingxian; Chen, Fei; Ren, Fazheng; Dai, Ruitong; Yi, Lunzhao; Li, Xingmin

doi:10.1016/j.mimet.2014.01.016

Cited by 9 publications

(8 citation statements)

References 30 publications

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“…The model with the lower value of MSE or RMSE (0 < MSE/RMSE) is more accurate in predicting model (Brocklehurst, 2003;Zhao et al, 2014). Mean square error (MSE) (eq.…”

Section: Validation Of Mathematical Modelsmentioning

confidence: 99%

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Modelling the effects of natural antimicrobials as food preservatives

Jaiswal¹,

Jaiswal²

2015

Handbook of Natural Antimicrobials for Food Safety and Quality

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A predictive food microbiological model is a mathematical representation of mechanisms that describes the growth, survival, inactivation or biochemical process of an organism and are used in the food industry to predict food pathogen growth and to help in the evaluation of food safety. Furthermore, it reduces the number of expensive and time-consuming experiments; however, adequate statistical analysis is a crucial step during all phases of model development and validation. Therefore, present chapter is focused on the basic concept of mathematical modelling which can be applied to estimate the effects of natural antimicrobials as food preservatives. Initial section discusses about the importance of modelling in food preservation, brief introduction to types of modelling, model development and validation and the final section includes review of recent research in natural antimicrobials modelling.

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“…The model with the lower value of MSE or RMSE (0 < MSE/RMSE) is more accurate in predicting model (Brocklehurst, 2003;Zhao et al, 2014). Mean square error (MSE) (eq.…”

Section: Validation Of Mathematical Modelsmentioning

confidence: 99%

“…It describes the observed values that lie above or below the line of equivalence and, if so, by how much. A bias factor >1 indicates a 'fail safe' model (Brocklehurst, 2003;Zhao et al, 2014;Zhou et al, 2008). Bias factor (eq.14.15) can be defined by the following expression:…”

Section: Validation Of Mathematical Modelsmentioning

confidence: 99%

Modelling the effects of natural antimicrobials as food preservatives

Jaiswal¹,

Jaiswal²

2015

Handbook of Natural Antimicrobials for Food Safety and Quality

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“…As a scientific-based tool, it evaluates the microbiological relations under certain environmental variables by applying and developing mathematic models [ 13 ], which can be classified into kinetic and probability models [ 14 ], empirical and mechanistic models [ 15 ] and primary, secondary and tertiary models [ 16 ] by the variables for modeling. For instance, the growth of Proteus mirabilis [ 17 ], Pseudomonas spp. [ 18 ] and Salmonella [ 19 ] in chicken were modeled and predicted under different temperatures.…”

Section: Introductionmentioning

confidence: 99%

Growth Prediction of the Total Bacterial Count in Freshly Squeezed Strawberry Juice during Cold Storage Using Electronic Nose and Electronic Tongue

Zhang

Pan

2022

Sensors

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The growth models of total bacterial count in freshly squeezed strawberry juice were established by gas and taste sensors in this paper. By selecting the optimal sensors and fusing the response values, the Modified Gompertz, Logistic, Huang and Baranyi models were used to predict and simulate the growth of bacteria. The results showed that the R2 values for fitting the growth model of total bacterial count of the sensor S7 (an electronic nose sensor), of sweetness and of the principal components scores were 0.890–0.944, 0.861–0.885 and 0.954–0.964, respectively. The correlation coefficients, or R-values, between models fitted by the response values and total bacterial count ranged from 0.815 to 0.999. A single system of electronic nose (E-nose) or electronic tongue (E-tongue) sensors could be used to predict the total bacterial count in freshly squeezed strawberry juice during cold storage, while the higher rate was gained by the combination of these two systems. The fusion of E-nose and E-tongue had the best fitting-precision in predicting the total bacterial count in freshly squeezed strawberry juice during cold storage. This study proved that it was feasible to predict the growth of bacteria in freshly squeezed strawberry juice using E-nose and E-tongue sensors.

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“…Temperature is one of the important extrinsic factors that affect the microbial growth and can be easily changed during food processing and storage. Many predictive models have been developed to investigate the growth kinetics under different temperature conditions [16]. To our knowledge, there is no study about the effect of storage temperature on the antimicrobial activities of nisin and lysozyme against A. acidoterrestris in juice.…”

Section: Introductionmentioning

confidence: 99%

Modeling growth of <em>Alicyclobacillus acidoterrestris</em> DSM 3922 type strain vegetative cells in the apple juice with nisin and lysozyme

Molva

Baysal

2017

AIMS Microbiology

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In the present study, the effect of storage temperature on A. acidoterrestris DSM 3922 cells (10 5 CFU/mL) was examined during growth in reconstituted apple juice (pH 3.8, °Brix 11.3) containing nisin (0–100 IU/mL) and lysozyme (0–100 mg/L). The growth curves were obtained at three temperatures of 27, 35 and 43 °C using absorbance data (OD 600 nm ). Based on the results, the minimal inhibitory concentrations (MICs) of nisin were found as 10 IU/mL at all tested temperatures. On the other hand, increasing the temperature decreased the amount of lysozyme for growth inhibition. The MICs of lysozyme were found as 10, 2.5 and 1.25 mg/L at 27, 35 and 43 °C, respectively. At selected non-inhibitory doses, nisin (1.25–5 IU/mL) and lysozyme (0.3–2.5 mg/L) prolonged the lag time compared to the controls at the corresponding temperatures. In addition, there was a strong linear relationship between the lag time and lysozyme concentrations at 27 and 35 °C (R 2 > 0.98). The results of this study demonstrated that both nisin and lysozyme could be used to inhibit the growth of A. acidoterrestris cells in the apple juice. The results also indicated that the growth parameters were variable depending on the storage temperature and the type of the antimicrobial agent used in the apple juice.

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Modeling and predicting the effect of temperature on the growth of Proteus mirabilis in chicken

Cited by 9 publications

References 30 publications

Modelling the effects of natural antimicrobials as food preservatives

Modelling the effects of natural antimicrobials as food preservatives

Growth Prediction of the Total Bacterial Count in Freshly Squeezed Strawberry Juice during Cold Storage Using Electronic Nose and Electronic Tongue

Modeling growth of <em>Alicyclobacillus acidoterrestris</em> DSM 3922 type strain vegetative cells in the apple juice with nisin and lysozyme

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