Determination of organic arsenic acids by machine learning-assisted SERS on a silicon-modified coating of Fe 3 O 4 @Ag

Key Points

• The research aims to improve the detection of organic arsenic acids using machine learning techniques with SERS.
• Employed surface-enhanced Raman spectroscopy (SERS) for analysis
• Utilized machine learning algorithms for data interpretation
• Applied a silicon-modified coating of Fe3O4@Ag to enhance SERS signals
• Successfully detected various organic arsenic acids using the developed method
• Demonstrated the potential of machine learning to enhance identification accuracy
• Improved sensitivity in comparison to traditional detection methods