Performance of response surface model for prediction of Leuconostoc mesenteroides growth parameters under different experimental conditions

Zurera‐Cosano, Gonzalo; Garcı́a-Gimeno, Rosa Marı́a; Rodríguez-Pérez, Rosa; Hervás-Martínez, C.

doi:10.1016/j.foodcont.2005.02.003

Cited by 55 publications

(40 citation statements)

References 30 publications

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“…Baranyi et al (1999), proceeding on their previous works, reported values of standard errors in the range of 5-15%. The SEP of the presented RSM for the growth rate of Candida maltosa in co-existence with L. rhamnosus of various initial concentrations was comparable to those published by Zurerra-Cosano et al (2006). RSM for lag time of C. maltosa provided worse results in terms of % SEP.…”

Section: Validation Of the Response Surface Modelssupporting

confidence: 69%

“…For internal validation, Zurerra- Cosano et al (2006) used the % SEP parameter that, within their response surface model of Leuconostoc mesenteroides (data measured by optical density) in aerobic and anaerobic conditions, ranged from 6.58 to 27.63. Naturally, the higher values of SEP were related with the prediction of lag time.…”

Section: Validation Of the Response Surface Modelsmentioning

confidence: 99%

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Characterisation of Lactobacillus rhamnosus VT1 and its effect on the growth of Candida maltosa YP1

Liptáková¹,

Valı́k²,

Lauková³

et al. 2007

Czech J. Food Sci.

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The combined effect of initial amount of 18 h L. rhamnosus VT1 inoculum and incubation temperature on the growth of Candida maltosa YP1, an oxidative food spoilage yeast strain, was primarily modelled and studied by standard response surface methodology. This study resulted in the following linear regression equations characterising lag time and growth rate of C. maltosa YP1 in milk in competition with the potentially protective lactobacillus strain. Lag-phase of C. maltosa was strongly influenced by the amount of lactobacillus inoculum (V0) and incubation temperature (1/T). The synergic effect of both these factors was also evident as results from the equation lag = –33.50 + 186.38 × V0 × 1/T + 512.27 × 1/T – 5.511 × V0 (R2(λ) = 0.849). The growth rate was sufficiently described by the linear relation: GrCm = –0.00046 + 0.0033 × T – 0.0016 × V0 (R2(Gr) = 0.847). On the basis of these equations, the mutual microbial interactions and the potential application of the lactobacillus strains to food protection are discussed.

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Section: Validation Of the Response Surface Modelssupporting

confidence: 69%

Section: Validation Of the Response Surface Modelsmentioning

confidence: 99%

Characterisation of Lactobacillus rhamnosus VT1 and its effect on the growth of Candida maltosa YP1

Liptáková¹,

Valı́k²,

Lauková³

et al. 2007

Czech J. Food Sci.

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“…Predictive microbiology is a useful tool in food industry to predict behaviors of microorganisms (24), where primary model describes the growth data under constant environmental conditions and secondary model describes the dependence of primary model parameters on environmental factors such as temperature, water activity, and pH. Primary models such as Logistic, Gompertz and Baranyi model are often used to fitting microbial growth data.…”

Section: Growth Of V Harveyimentioning

confidence: 99%

Effect of combined function of temperature and water activity on the growth of Vibrio harveyi

Zhang¹,

Guo²,

Li³

et al. 2012

Braz. J. Microbiol.

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Vibrio harveyi is considered as a causative agent of the systemic disease, vibriosis, which occurs in many biological fields. The effects of temperatures (12.9-27.1 o C) and water activity (NaCl% 0.6%-3.4%) on V.harveyi were investigated. The behavior and growth characteristics of V. harveyi was studied and modeled.Growth curves were fitted by using Gompertz and Baranyi models, and the Baranyi model showed a better fittness. Then, the maximum growth rates (μ max ) and lag phase durations (LPD, λ) obtained from both Gompertz and Baranyi model were modeled as a combination function of temperature and water activity using the response surface and Arrhenius-Davey models for secondary model. The value of r 2 , MSE, bias and accuracy factor suggest Baranyi model has better fitness than Gompertz model. Furthermore, validation of the developed models with independent data from ComBase also shown better interrelationship between observed and predicted growth parameter when using Baranyi model.

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“…RSM has become very popular in the recent period for description and optimisation of various processes. In the predictive microbiology, for example, it is often used to describe the relationships between the combination of factors and the growth curve parameters in order to predict the growth parameters of leuconostoc mesenteroides (Zurera-Cosano et al 2006) or heat-resistance of alicyclobacillus acidoterrestis (Bahçeci & Acar 2007).…”

mentioning

confidence: 99%

Application of quantitative approach focused on the competition of lactic acid bacteria culture with coagulase-positive staphylococci under the conditions related to artisanal cheese fermentation

Valı́k¹,

Medveďová²,

Liptáková³

2011

Czech J. Food Sci.

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A quantitative analysis of Staphylococcus aureus growth was carried out in milk fermented with the mesophilic mixed-strain culture of lactic acid bacteria (LAB), in relation to the conditions prevailing during artisanal ewes' lump cheese production. Both the temperature and initial volume of the culture had a dramatic effect on the behaviour of the S. aureus strain under study. Depending on the conditions, the growth, reaching maximal population density (N max ) and a strong inhibition of S. aureus were observed. Regression analysis of the results enabled us to explain the relationships between the parameters such as the growth or inhibition rate, lag phase and N max -N 0 and the temperature and initial culture density on the other hand. The surface methodology enabled to show that the rate of S. aureus inhibition (r inh ) was affected by the temperature and volume of LAB inoculum following the equation: r inh =-0.1302 + 0.02325 × t -0.000975 × t 2 -0.00001 × t 2 × V 0 2 (R = 0.965). The fact that S. aureus increased in number mostly only by about 1 log and did not reach 10 6 CFU/ml during our model experiments was the most useful for good manufacturing practice. The knowledge obtained in this study concerned with the quantitative behaviour of S. aureus including the information on the inhibition at the selected environmental factors (temperature, inoculum size, and pH) is essential for the improvement of the fermentation process, quality and safety of artisanal ewes' cheese production used as a raw material for industrial Bryndza cheese production.

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Performance of response surface model for prediction of Leuconostoc mesenteroides growth parameters under different experimental conditions

Cited by 55 publications

References 30 publications

Characterisation of Lactobacillus rhamnosus VT1 and its effect on the growth of Candida maltosa YP1

Characterisation of Lactobacillus rhamnosus VT1 and its effect on the growth of Candida maltosa YP1

Effect of combined function of temperature and water activity on the growth of Vibrio harveyi

Application of quantitative approach focused on the competition of lactic acid bacteria culture with coagulase-positive staphylococci under the conditions related to artisanal cheese fermentation

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