We are delighted to share that our paper, "Functional Regression for SERS Spectrum Transformation Across Diverse Instruments," has been accepted by Analyst. This work introduces SpectraFRM, a cutting-edge method to standardize SERS spectra across instruments using penalized functional regression.
Key Highlights:
Accuracy Boost: SpectraFRM reduces spectral error by 11% and improves classification accuracy by up to 10%.
Interpretability: Unlike traditional methods, SpectraFRM ensures flexible and interpretable transformations.
Real-World Impact: The method lays the foundation for universal SERS databases, critical for applications in diagnostics and environmental monitoring.
A Collaborative Triumph
This work would not have been possible without the contributions of our dedicated team members and collaborators, including experts in experimental SERS, statistical modeling, and artificial intelligence. Special thanks to all our collaborators for their commitment and innovative insights.
Why It Matters
The standardization of SERS spectra is not just a technical advancement; it has wide-reaching implications for applications in biomedical diagnostics, environmental monitoring, and beyond. By enabling reliable cross-instrument comparisons, SpectraFRM lays the groundwork for universal SERS databases and accelerates the integration of machine learning in SERS-based sensing technologies.
The preprint of the paper can be found at D4AN01177E.
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