Application of intelligent modeling to predict the population dynamics of Pseudomonas aeruginosa in Frankfurter sausage containing Satureja bachtiarica extracts

Alghooneh, Ali; Behbahani, Behrooz Alizadeh; Khorasgani, Mohammad Rabbani; Yazdi, Farideh Tabatabaei

doi:10.1016/j.micpath.2015.06.003

Cited by 39 publications

(21 citation statements)

References 31 publications

(37 reference statements)

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“…Finally, according to the four statistical parameters and Figures and , one can conclude that the kinetic pattern of E. coli population dynamic was the homographic type, while it was the n ‐order kinetic type for S. enteritidis population dynamic. Alghooneh et al () employed stepwise regression, genetic algorithm‐artificial neural network and co‐active neuro‐fuzzy inference system (CANFIS) techniques to predict the effect of Satureja extracts on the population dynamics of Pseudomonas aeruginosa in Frankfurter sausage, and found that ANN with one hidden layer comprising 14 neurons gave the best fitting with the experimental data *( R 2 = 0.98). They also reported an excellent correlation between CANFIS predictions and experimental data ( R 2 = 0.96).…”

Section: Resultsmentioning

confidence: 99%

“…A 0.22 mm sterilizing grade syringe filter (Durapore) was used to eliminate any microbial contamination and sterilize the extract. The obtained RE was finally kept in sterile containers in the refrigerator (4°C; Alghooneh et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Frankfurter sausage has been studied with regard to the determination of most formulations of the meat products containing nitrite as a main ingredient to prohibit the growth of pathogenic bacteria (Marco, Navarro, & Flores, ). Concerns over the safety of nitrite and its role in the formation of nitrosamines have aroused considerable interest in developing alternative antimicrobial agents, such as plant‐derived agents, to replace nitrite (Alghooneh, Alizadeh Behbahani, Noorbakhsh, & Yazdi, ; Collins‐Thompson et al, ; Kavuncuoglua et al, ; Yolmeh, Habibi Najafi, & Salehi, ).…”

Section: Introductionmentioning

confidence: 99%

“…Modeling is essential for characterization of a system, designing of a new process, developing a better insight of the present process and prediction, and optimization of the processes. Generally, three types of modeling, that is, physical, empirical, and semiempirical are used in modeling of complex biological processes (Alghooneh et al, ). Up to now, due to the complicated nonlinear nature of bacteria population dynamic, any physical model was not used for the investigation of these processes.…”

Section: Introductionmentioning

confidence: 99%

“…Up to now, due to the complicated nonlinear nature of bacteria population dynamic, any physical model was not used for the investigation of these processes. Many researchers have focused on using empirical models such as neuron‐fuzzy, artificial neural networks (ANNs) and genetic algorithm in microbial population dynamic investigation (Alghooneh et al, ; Fernandes, Rigden, & Franco, ; Soleimanzadeh, Hemati, Yolmeh, & Salehi, ; Yolmeh et al, ). Although empirical models are very precise, they have some major drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Kinetic pattern and microbial population dynamic characterization ofEscherichia coliandSalmonella enteritidisin Frankfurter sausage: An experimental and modeling study

Alghooneh

Behrouzian

Yazdi

et al. 2019

Journal of Food Safety

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Concerns over the safety of nitrite and its role in the formation of nitrosamines have aroused considerable interest in developing alternative antimicrobial agents to replace nitrite. Plant‐derived antimicrobial agents could serve against food spoilage and pathogens. For the first time in this study, three semiempirical kinetic models, that is, exponential, homographic, and n‐order kinetic models were established to describe the microbiological population pattern and dynamic behavior. The Escherichia coli and Salmonella enteritidis population dynamics were investigated in Frankfurter sausage as a complex food system, contained four different antimicrobial redcurrant extract (RE) concentrations (0, 0.1, 0.2, and 0.4% wt/wt) at two temperatures (4 and 25°C) during 17 days storage. Furthermore, the results were compared with a neuro‐fuzzy system. Although, all three kinetic models showed a reasonable prediction accuracy, homographic kinetic model was the best for predicting antibacterial effects of RE against E. coli (R2 = 0.94–1, Radj2 = 0.93–0.99, Q‐squared = 0.80–0.89, and RMSE = 0.21–0.68) and the n‐order kinetic model did the best in describing the kinetic pattern of S. enteritidis (R2 = 0.97–0.99, Radj2 = 0.94–0.99, Q‐squared = 0.85–0.93, RMSE = 0.06–0.74). The increase in the RE concentration and temperature resulted in the increase of kinetic parameters of microbial population decline rate and extent for both bacteria, significantly. Furthermore, all the homographic model parameters (except of equilibrium population, p∞) and all the kinetic model parameters with n order (except of reaction order) the synergistic effect of temperature and concentration was clear. RE at 0.4% concentration induced 0.12 log colony forming unit (CFU)/g E. coli and 0.45 log CFU/g S. enteritidis equilibrium population ( p∞) at 17th day of storage at 25°C. Compared to S. enteritidis, E. coli was more sensitive to RE concentration at both temperatures. Practical applications Most formulations of the meat products contain nitrite as a main ingredient to prohibit the pathogenic bacteria growth. The results of the present study showed that all the three studied kinetic models (exponential, homographic, and n‐order kinetic models) appropriately modeled the S. enteritidis and E. coli population dynamics. Regarding the statistical parameters, n‐order and homographic kinetic models were found to be the best models for predicting antibacterial effects of RE against S. enteritidis and E. coli, respectively. Furthermore, E. coli was more sensitive against RE than S. enteritidis in Frankfurter sausage samples at 4 and 25°C during 17 days storage. With the increase in the RE concentration at the constant temperature and with the increase in temperature at the constant extract concentration, the decline rate of E. coli population increased significantly (p < .05), while the needed time for 50% reduction decreased; besides, S. enteritidis population dynamics showed the increase in the rate and extent of decline, and a decrease in the equ...

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%