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<p>This research investigates the efficacy of UV-C (mercury) lamps and UV-C LEDs in reducing microbial contamination on the surface of strawberries, while also examining bacterial survival and outgrowth during storage. The study utilizes predictive modeling to assess the inactivation/survival rates of microorganisms, providing comprehensive insights into the effectiveness of UV-C treatments. Strawberries inoculated with either <i>Escherichia coli or Listeria monocytogenes</i> were subjected to UV-C lamp or UV-C LED treatment for various durations, up to a maximum of 25&#xa0;min. Subsequently, the treated strawberries were stored under refrigeration conditions to investigate the microbial response over time. In the case of UV-C lamp treatment, <i>E. coli</i> and <i>L. monocytogenes</i> populations were significantly reduced, with the maximum reduction achieved at 25&#xa0;min of treatment. However, microbial reduction became non-significant at 15&#xa0;min for <i>E. coli</i> and at 20&#xa0;min for <i>L. monocytogenes</i>. UV-C LED treatment, with different light configurations, also exhibited microbial reduction, with the most significant reduction observed when all lights were on for 25&#xa0;min. The maximum reductions achieved with the UV-C lamp were 2.75 log CFU/g for <i>E. coli</i> and 2.63 log CFU/g for <i>L. monocytogenes</i>, whereas with the UV-C LEDs, it was 2.39 log CFU/g for <i>E. coli</i> and 2.15 log CFU/g for <i>L. monocytogenes</i>. Our storage study revealed that both <i>E. coli</i> and <i>L. monocytogenes</i> populations kept decreasing over time until the sample was spoiled, with <i>E. coli</i> exhibiting greater resistance to UV-C treatments compared to <i>L. monocytogenes</i>. Predictive modeling using linear and Weibull models further supported these findings, with Weibull models showing upward concavity (α &lt; 1), indicating microbial survival. In conclusion, predictive models provided valuable insights into microbial inactivation/survival, aiding in the optimization of UV-C treatment conditions.</p>

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Modeling the effect of UV-C treatment on the survival of microorganisms on fresh strawberries

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月28日

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  • Prachi Pahariya,
  • Maadh F. Alani,
  • Prabesh Joshi,
  • Abhinav Mishra,
  • Derek J. Fisher,
  • Ruplal Choudhary

摘要

This research investigates the efficacy of UV-C (mercury) lamps and UV-C LEDs in reducing microbial contamination on the surface of strawberries, while also examining bacterial survival and outgrowth during storage. The study utilizes predictive modeling to assess the inactivation/survival rates of microorganisms, providing comprehensive insights into the effectiveness of UV-C treatments. Strawberries inoculated with either Escherichia coli or Listeria monocytogenes were subjected to UV-C lamp or UV-C LED treatment for various durations, up to a maximum of 25 min. Subsequently, the treated strawberries were stored under refrigeration conditions to investigate the microbial response over time. In the case of UV-C lamp treatment, E. coli and L. monocytogenes populations were significantly reduced, with the maximum reduction achieved at 25 min of treatment. However, microbial reduction became non-significant at 15 min for E. coli and at 20 min for L. monocytogenes. UV-C LED treatment, with different light configurations, also exhibited microbial reduction, with the most significant reduction observed when all lights were on for 25 min. The maximum reductions achieved with the UV-C lamp were 2.75 log CFU/g for E. coli and 2.63 log CFU/g for L. monocytogenes, whereas with the UV-C LEDs, it was 2.39 log CFU/g for E. coli and 2.15 log CFU/g for L. monocytogenes. Our storage study revealed that both E. coli and L. monocytogenes populations kept decreasing over time until the sample was spoiled, with E. coli exhibiting greater resistance to UV-C treatments compared to L. monocytogenes. Predictive modeling using linear and Weibull models further supported these findings, with Weibull models showing upward concavity (α < 1), indicating microbial survival. In conclusion, predictive models provided valuable insights into microbial inactivation/survival, aiding in the optimization of UV-C treatment conditions.