2001 Fiscal Year Final Research Report Summary
Change in microbial resistance to heat in non-isothermal process of pasteurization and building of predictive model
| Project/Area Number |
12650793
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
生物・生体工学
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| Research Institution | Kansai University |
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Principal Investigator |
TUCHIDO Tetsuaki Kansai University, Faculty of Engineering, Professor, 工学部, 教授 (50029295)
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| Co-Investigator(Kenkyū-buntansha) |
MATSUMURA Yoshinobu Kansai University, Faculty of Engineering, Lecturer, 工学部, 専任講師 (40268313)
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| Project Period (FY) |
2000 – 2001
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| Keywords | Pasteurization / Non-isothermal process / Eschericha coli / Heat resistnace / Predictive microbiology |
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| Research Abstract |
Recently, the construction of reliable method for evaluation of heat process and the establishment of theorctical background for determ. ining shel-life of foods have been required. We conducted this study in order to build up such a model in which the effect of non-isothermal process on the heat resistance of microorganisms was considered. Escherichia coli cells were pre-incubated at different temperatures between 0 and 45℃ before heat treatment at a lethal temperature and their resistance was evaluated. As a result, the dependency of a parameter D value, the decimal reduction time, on the pre-incubation temperature demonsirated different patterns between below and above 37℃. Above 37℃, the rate of increase in and the steady state level of D value were found to be rather high. The rate of dilution for temperature rise from pre-incubation to heat treatment afiiected the dependency of D value on the pre-incubation. To see the dependency of rate of increase in D value on the preincubation temporature, we took its Arrhenius plot from the data obtained in this study and previously by us. The results should be available for prediction of the thermal death in practical evaluation of heat processes. Furthermore, we obtained data concerning thermal death reaction by changing factors such as heating temperature, pH, and sodium chloride concentration to develop predictive equations between two factors and also between all factors by second-order and third-order regression analyses, respectively.
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