Surface-Enhanced Raman Spectroscopy Semi-Quantitative Molecular Profiling with a Convolutional Neural Network.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Biomolecular Analysis

Background:

• Surface-Enhanced Raman Scattering (SERS) offers sensitive, label-free molecular identification.
• Analyzing complex mixtures with multiple analytes presents significant challenges.
• Simultaneous detection and quantification in diverse matrices require advanced analytical tools.

Purpose of the Study:

• To develop an integrated analytical framework combining SERS with machine learning for multi-analyte identification and quantification.
• To address the challenges of analyzing complex biological samples.
• To establish a robust method for precise biomarker detection in mixtures.

Main Methods:

• Integration of SERS spectroscopy with a hierarchical machine learning framework.
• Utilized a deep learning convolutional neural network (CNN) for analyte discrimination.
• Employed a support vector regression (SVR) model for semi-quantitative concentration analysis.

Main Results:

• The SERS-CNN-SVR approach demonstrated robust classification accuracy for structurally similar analytes.
• Accurate detection of short-chain fatty acids (SCFAs) at physiologically relevant concentrations was achieved.
• Consistent performance was observed in both simple aqueous media and complex cell culture environments.

Conclusions:

• The integrated SERS-CNN-SVR methodology provides a viable solution for advanced mixture analysis.
• This approach enables precise identification and quantification of multiple biomarkers.
• The framework shows potential for applications requiring sensitive detection in complex biological systems.