A Laser-Based Rapid Method for the Targeted Inactivation of Escherichia coli and Saccharomyces cerevisiae
摘要
Ensuring sterility and preventing microbial contamination are critical challenges in medical and healthcare packaging. Traditional methods, including chemical treatments, are often expensive, time-consuming, and incompatible with materials. Therefore, there is a pressing need for rapid, label-free, and non-destructive methods to inactivate targeted pathogens in medical packaging and healthcare settings. This study emphasized the development of a laser-based approach for rapidly and specifically inactivating both a common prokaryotic pathogen, Escherichia coli, and a model eukaryotic microorganism, Saccharomyces cerevisiae. To validate optimal parameters for laser-based inactivation modality, E. coli TOP10 and S. cerevisiae (yeast strain INVSc1) cultures were exposed to laser irradiation at various laser powers (LP) and laser speeds (LS). After laser irradiation treatment, microbial cells’ survival was evaluated by observing the clear inhibition zones in cultured agar plates. Results indicated that increasing laser power (LP) from 5% to 15% at a fixed laser speed of 5 mm/s progressively enhanced microbial inactivation. Partial inhibition of both E. coli TOP10 and S. cerevisiae was observed at lower powers. Complete inactivation of E. coli TOP10 was achieved at 15% LP, whereas complete inactivation of S. cerevisiae required 10% LP. Similarly, the effects of laser speed (LS) on both microorganisms were evaluated. Increasing LS from 5 mm/s to 15 mm/s resulted in greater microbial survival and reduced inactivation efficacy for both E. coli TOP10 and S. cerevisiae, indicating that slower scanning speeds provided more effective microbial control due to longer exposure times. This research presents a proof of concept for using laser-based technology as a microbial inactivation strategy. The results demonstrate the potential of laser treatment to effectively reduce microbial populations, highlighting its promise for future applications in pathogen control. However, further research is required to address current limitations, evaluate performance across diverse operating conditions, and develop predictive mathematical models to optimize process parameters and support future scale-up and commercialization efforts.