A new logistic model for Escherichia coli growth at constant and dynamic temperatures

Fujikawa, Hiroshi; Kai, Akemi; Morozumi, Satoshi

doi:10.1016/j.fm.2004.01.007

Cited by 198 publications

(166 citation statements)

References 23 publications

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“…FCW ( Figure 1a) grew rapidly during days 1-5, with growth rate slowing between days 5-9, and reached a plateau between days 9-14; while DCW grew quickly during days 1-5, and slowed over the final 9 days (5)(6)(7)(8)(9)(10)(11)(12)(13)(14). DCW reached its peak (0.89 g per flask) on the 5th day after subculture, as shown in Figure 1b, then continued to decrease during the culture, and this was due to the decrease of nutrient intake in cell proliferation caused by nutrient consumption in the later period of the culture.…”

Section: Growth Curves Of Suspension Cellsmentioning

confidence: 99%

“…In this study, we chose to add elicitor on day 5 after the subculture was taken. Population quantity of suspension cells, a typical generation overlapping of a single population in a limited space, presents an S curve in a growth cycle [12,13], so the equation of Logistic f(x) = b/(1 + a × exp(−k × x)) was used to fit changes of FCW and DCW with time in a growth cycle, and the results are shown in Table 1. The degree of fitting of FCW was better than that of DCW, as the R-square described in Table 1, so FCW was used to fit the curve, as following:…”

Section: Solving Key Point Of the Logistic Growth Modelmentioning

confidence: 99%

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Biphenyl Phytoalexin in Sorbus pohuashanensis Suspension Cell Induced by Yeast Extract

et al. 2016

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Biphenyls are unique phytoalexins de novo synthesized in plants in response to pathogen attack. These compounds are found in Maloideae, a subfamily of the Rosaceae. The anti-microbial activities of biphenyls have been reported in a number of studies and they appear to represent an important defense strategy against pathogens common in the Maloideae, such as species in Malus, Pyrus, Sorbus, and Chaenomeles. Here, cell suspension cultures of Sorbus pohuashanensis were established to study biphenyl phytoalexins formation after yeast extract (YE) treatment. An ultra-performance liquid chromatography (UPLC) method coupled with quadrupole time of flight mass spectrometry (Q-TOF-MS) LC−MS/MS was applied to determine the time course of these biphenyl biomarkers accumulation in YE-treated S. pohuashanensis suspension cells. The results of quantitative analyses show the content of Noraucuparin, 2 -Hydroxyaucuparin, and their glycosides initially increased, then decreased over time. The Noraucuparin content reached its highest (225.76 µg·g −1 ) at 18 h after treatment, 6 hours earlier than that of Noraucuparin 5-O-β-D-glucopyranoside. The content of 2 -Hydroxyaucuparin reached its highest (422.75 µg·g −1 ) at 30 h after treatment, also earlier than that of its glycoside. The understanding of phytoalexin metabolism in this study may provide a basis for improving Maloideae resistance to pathogens.

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Section: Growth Curves Of Suspension Cellsmentioning

confidence: 99%

Section: Solving Key Point Of the Logistic Growth Modelmentioning

confidence: 99%

Biphenyl Phytoalexin in Sorbus pohuashanensis Suspension Cell Induced by Yeast Extract

et al. 2016

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“…Many mathematical growth models have been reported so far (Baranyi and Roberts, 1994;Gibson et al, 1987). Recently we developed a new logistic model, which can successfully predict bacterial growth at constant and varying temperatures (Fujikawa et al, 2003 and2004;Morozumi, 2005, 2006). Namely, the model could successfully predict the growth of Escherichia coli, Staphylococcus aureus and Salmonella in broth and milk, on the surface of a nutrient agar plate and in a pouched food (mashed potatoes) when exposed to various temperature patterns.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Estimation of Temperature in Food Exposed to Abuse Temperature

Fujikawa

Kano²

2008

FSTR

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A new method of estimating the temperature in a food (sausage) exposed to dynamic temperatures was developed to predict bacterial growth in the food. First, the temperatures of nodes constructed in the sausage were successfully predicted from the measured temperatures of the surface nodes using the universal heat conduction equation. Second, to apply the method to real food industry contexts, the nodes for measurement were reduced to two standard nodes, namely, the centers of the top and bottom surfaces of the food. Temperature differences between the surface and standard nodes were mostly independent of the exposure temperature. Using the difference data and the temperatures measured at the standard nodes, the temperatures in the food exposed to various patterns of temperature were also successfully predicted. This method is thought to be empirical but practical. A new alert system which predicts the growth of microorganisms contaminating foods might be developed by coupling our method with a microbial growth model.

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“…The growth of microbial species in monoculture has been successfully predicted with the combination of the primary growth model like the NL model and a secondary model for r like the square root model Fujikawa et al, 2004 . Similarly, in the competitive growth of microbial species at dynamic temperatures, the growth of each species can be predicted with the NL-LV model and a secondary model.…”

Section: Prediction Of Competitive Growth At Dynamic Temperaturesmentioning

confidence: 99%

“…However, the modified Gompertz model is not suitable for application at changing temperatures and the Baranyi model has a problem in structure Baranyi and Roberts, 1995 . A new growth model, which is an extended version of the logistic model, was also developed by the author; the model was called the new logistic NL model Fujikawa et al, 2003Fujikawa et al, , 2004 . The NL model is described as below.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Competitive Microbial Growth

Fujikawa

2016

Biocontrol Sci.

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Prediction of competitive microbial growth is becoming important for microbial food safety. There would be two approaches to predict competitive microbial growth with mathematical models. The first approach is the development of a growth model for competitive microbes. Among several candidates for the competition model considered, the combination of the primary growth model of the new logistic NL model and the competition model of the Lotka-Vorttera LV model showed the best performance in predicting microbial competitive growth in the mixed culture of two species. This system further successfully predicted the growth of three competitive species in mixed culture. The second approach is the application of the secondary model especially for the parameter of the maximum cell population in the primary growth model. The combination of the NL model and a polynomial model for the maximum population successfully predicted Salmonella growth in raw ground beef. This system further successfully predicted Salmonella growth in beef at various initial concentrations and temperatures. The first approach requires microbial growth data in monoculture for analysis. The second approach to the prediction of competitive growth from the viewpoint of microbial food safety would be more suitable for practical application.

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A new logistic model for Escherichia coli growth at constant and dynamic temperatures

Cited by 198 publications

References 23 publications

Biphenyl Phytoalexin in Sorbus pohuashanensis Suspension Cell Induced by Yeast Extract

Biphenyl Phytoalexin in Sorbus pohuashanensis Suspension Cell Induced by Yeast Extract

A New Method for Estimation of Temperature in Food Exposed to Abuse Temperature

Prediction of Competitive Microbial Growth

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