Predictive Modeling for Inactivation of <i>Escherichia coli</i> Biofilm with Combined Treatment of Thermosonication and Organic Acid on Polystyrene Surface
The present study aimed to evaluate the antibiofilm effect of combined sonication treatment with organic acids on polystyrene surfaces and to develop a predictive model for the inactivation of <i>Escherichia coli</i> biofilms. Polystyrene plates containing <i>E. coli</i> biof...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/13/24/4002 |
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| Summary: | The present study aimed to evaluate the antibiofilm effect of combined sonication treatment with organic acids on polystyrene surfaces and to develop a predictive model for the inactivation of <i>Escherichia coli</i> biofilms. Polystyrene plates containing <i>E. coli</i> biofilms were subjected to sonication using different inactivation solutions (PBS, lactic acid, and acetic acid) at varying temperatures (20 °C, 40 °C, and 50 °C) and durations (2 and 5 min). The effects of temperature, treatment duration, and inactivation solution on <i>E. coli</i> biofilm removal were statistically significant (<i>p</i> < 0.05). The use of organic acids, along with increased treatment time and temperature, led to a significant reduction in viable cell counts (0.43–6.21 log CFU/mL) and optical density (0.13–0.72 at OD600) of <i>E. coli</i> biofilms (<i>p</i> < 0.05). The highest <i>E. coli</i> biofilm inactivation, with a reduction of 6.21 CFU/mL and 0.72 OD, was achieved by combining organic acid and thermosonication at 50 °C for 5 min. A significant positive correlation was observed between test methods based on viable cell count and optical density (OD) measurements. According to multiple linear regression analysis results, the <i>R</i><sup>2</sup> values of the predictive models for biofilm inactivation, based on viable cell count and OD measurements, were 0.84 and 0.80, respectively. Due to its higher accuracy, the predictive model developed using viable cell count data is recommended for applications in the food industry and processing sectors. |
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| ISSN: | 2304-8158 |