Machine learning-assisted SERS detection of pyrethroid pesticides in edible fungi using a magnetic nanosensor

Key Points

• Detection of pyrethroid pesticides achieved with high sensitivity using a machine learning approach.
• Machine learning yielded a 95% accuracy rate in identifying pesticide levels in fungi samples.
• SERS spectroscopy was employed to analyze the effectiveness of the magnetic nanosensor framework.
• Findings point to improved food safety measures, but broader validations are needed for field applications.