Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles,...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Data Science

Background:

• Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for chemical analysis due to its high sensitivity and rich information.
• SERS applications span medical diagnosis, forensics, food safety, and microbiology.
• Selectivity challenges in complex matrices often limit SERS's real-world applicability.

Purpose of the Study:

• To critically review the integration of SERS with chemometrics and machine learning.
• To discuss the synergy, advantages, and limitations of these combined approaches for qualitative and quantitative analysis.
• To explore recent trends and provide guidance on method selection for SERS data analysis.

Main Methods:

• Review of principles and applications of SERS coupled with multivariate statistical analysis.
• Exploration of chemometrics and machine learning algorithms for SERS data processing.
• Discussion of advanced and less common data analysis tools for SERS.

Main Results:

• Multivariate statistics and AI-driven methods effectively address SERS selectivity limitations in complex samples.
• The combination of SERS with chemometrics and machine learning enhances analytical performance for both qualitative and quantitative tasks.
• Emerging data analysis tools offer new possibilities for SERS applications.

Conclusions:

• The synergy between SERS and advanced data analysis techniques is crucial for overcoming current limitations.
• Standardization and benchmarking of chemometric/machine learning methods are necessary for wider SERS adoption.
• This integration can elevate SERS from an alternative method to a routine analytical technique.