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Addressing SERS data scarcity for sulfonamide detection in water using a supervised VAE-WGAN framework
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DOI：10.1016/j.ceja.2026.101205.png)
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Abstract
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• A supervised VAE-WGAN framework is proposed for SERS data augmentation. • Synthetic SERS spectra enable improved sulfonamide classification in water. • Specificity increased from 73.33% to 90.00%. • Generated data preserves spectral structure and enhances multiple ML models. • The approach supports reliable SERS-based environmental monitoring.
Keywords:
Surface-enhanced raman scattering
Sulfonamides
Generative deep learning
Data augmentation
Environmental monitoring
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