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Inactivation kinetic parameters of hydrogen peroxide application in commercial sterility of aseptic processing systems
Hydrogen peroxide (H2O2) is a commonly used sterilant for aseptic processing and packaging systems. Surrogate bacterial spores are commonly used for the microbiological validation of the sterilization process. However, limited information is available on the resistance of these surrogate spores in l...
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Published in: | Journal of food engineering 2024-10, Vol.378, p.112105, Article 112105 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Hydrogen peroxide (H2O2) is a commonly used sterilant for aseptic processing and packaging systems. Surrogate bacterial spores are commonly used for the microbiological validation of the sterilization process. However, limited information is available on the resistance of these surrogate spores in liquid H2O2 (LHP) and vapor H2O2 (VHP). This study aims to fill the gaps in understanding of microbial inactivation parameters and the impact of temperature on VHP effectiveness. 35 % LHP experiments were conducted at temperatures ranging from 25 to 70 °C, while VHP tests were performed using vaporized sterilant technology (VST) and temperatures ranging from 43.3 to 60 °C. Exposure times ranged from 1 to 18 s, and VHP concentrations of 2500 and 4450 PPM were used while maintaining a relative humidity of 0.5%. The model performance of the log-linear and Weibull model was assessed using Root Mean Square Error (RMSE), the sum of the square, and Akaike Information Criterion (AIC) scores. The Weibull model outperformed the log-linear model in terms of parameter estimation and statistical indices.
•Effectiveness of liquid and vapor hydrogen peroxide were tested for commercial applications.•Kinetic parameters were estimated for surrogate microorganism used for aseptic processing microbiological validation.•Weibull model performed better than the log-linear model based on estimated parameters and statistical indices. |
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ISSN: | 0260-8774 1873-5770 |
DOI: | 10.1016/j.jfoodeng.2024.112105 |