Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review

Area of Science:

• Analytical Chemistry
• Environmental Science
• Data Science

Background:

• Surface-Enhanced Raman Spectroscopy (SERS) offers sensitive and specific detection of environmental contaminants.
• SERS faces challenges with high-dimensional data, low-concentration targets, and spectral overlap.
• Machine Learning (ML) is increasingly adopted for advanced SERS data analysis.

Purpose of the Study:

• To provide a comprehensive review of applying ML techniques to SERS data analysis.
• To explore environmental applications of ML-integrated SERS for pollutant and pathogen detection.
• To discuss the benefits and considerations of using ML with SERS.

Main Methods:

• Review of existing literature on ML applications in SERS analysis.
• Exploration of diverse ML algorithms and multivariate tools for SERS data.
• Case studies of environmental monitoring using ML-enhanced SERS.

Main Results:

• Detailed procedural steps for implementing ML in SERS analysis are outlined.
• Various ML tools demonstrate effectiveness in handling complex SERS data.
• Successful integration of ML with SERS for detecting environmental pathogens and pollutants.

Conclusions:

• ML significantly improves the analysis of complex SERS data, addressing key limitations.
• ML-SERS synergy holds great promise for sensitive and specific environmental monitoring.
• Future research should focus on optimizing ML algorithms for real-world SERS applications.