The Analysis of Staphylococcus aureus after Exposure to Electromagnetic, UV Radiation via Support Vector Machine

The identification and examination of bacterial contamination, especially that caused by Staphylococcus aureus (S. aureus), is essential for healthcare, food safety, and environmental surveillance. Conventional microbiological methods for identifying bacteria are often slow, require significant labor, and involve complex sample processing. To overcome these challenges, we introduce a fiber optic biosensor combined with a SVM classification algorithm. Here, S. aureus was subjected to ultraviolet radiation (UV-C (254 nm) in Laminar Hood). The data set included 981 bacterial colonies exposed, 1080 unexposed colonies. The results demonstrate the inhibitory effect of UV light on S. aureus, which could serve as a basis for exploring further UV applications. Additionally, this study highlights the potential use of fiber optic biosensors to detect S. aureus contamination and underscores the role of image processing in microbiology, thus expanding the scope of machine learning applications in this field.