Machine learning-integrated surface-enhanced Raman spectroscopy analysis of multicomponent dye mixtures.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Materials Science

Background:

• Surface-enhanced Raman spectroscopy (SERS) offers high sensitivity and specificity for material analysis.
• Overlapping spectral signals from mixtures pose a significant challenge for accurate analyte identification.
• Developing robust methods for analyzing complex SERS data is crucial for advancing its applications.

Purpose of the Study:

• To compare various machine learning models for analyzing complex SERS spectra.
• To develop an effective method for identifying and classifying components in dye mixtures using SERS.
• To optimize SERS substrates and analytical workflows for enhanced detection capabilities.

Main Methods:

• Optimization of silver-coated gold core-shell nanocubes (Au@AgNCs) as SERS substrates.
• Utilizing Independent Principal Component Analysis (ICA) to isolate individual dye signals from mixture spectra.
• Classification of isolated signals using K Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN).

Main Results:

• The Convolutional Neural Network (CNN) model demonstrated superior performance compared to other machine learning algorithms.
• CNN achieved 98% accuracy in classifying single dyes and 97% accuracy in classifying dye mixtures.
• ICA effectively isolated overlapping signals, enabling accurate classification by the CNN model.

Conclusions:

• Independent Principal Component Analysis (ICA) combined with Convolutional Neural Networks (CNN) offers a powerful analytical approach for SERS.
• This integrated method significantly improves the accuracy of analyzing complex dye mixtures.
• The proposed workflow enhances the utility of SERS spectroscopy as a quantitative and qualitative analytical tool.