Machine learning-integrated SERS platform based on AuSe nanowires for indigo carmine detection in real samples

Area of Science:

• Nanomaterials
• Analytical Chemistry
• Spectroscopy

Background:

• Food dyes require sensitive detection methods.
• Surface-enhanced Raman scattering (SERS) offers high sensitivity.
• Developing robust SERS substrates is crucial for trace analysis.

Purpose of the Study:

• To develop a novel SERS substrate for trace detection of indigo carmine (IC).
• To evaluate the performance of the AuSe NWs substrate for IC detection.
• To assess the practical applicability and machine learning integration for food analysis.

Main Methods:

• Synthesis of selenium nanowires (Se NWs) via co-precipitation.
• Decoration of Se NWs with gold nanoparticles (Au NPs) using photoreduction.
• Characterization of the AuSe NWs substrate and SERS measurements.
• Detection of indigo carmine (IC) and analysis using machine learning.

Main Results:

• The AuSe NWs substrate showed strong SERS activity with an enhancement factor of 10⁸.
• Sensitive detection of IC was achieved at concentrations as low as 10^-9 M.
• The substrate demonstrated excellent uniformity, reproducibility, and stability over 30 days.
• Practical detection limits of 10^-8 M were reached in real food samples.
• Machine learning accurately classified IC in diverse food matrices.

Conclusions:

• The developed AuSe NWs substrate is a highly sensitive and stable SERS platform.
• This technology provides a reliable tool for real-time monitoring of synthetic dyes in food.
• Integration with machine learning enhances the accuracy and applicability in food safety analysis.