Machine learning methods for classifying novel fentanyl analogs from Raman spectra of pure compounds

•Our machine learning (ML) models aim to detect novel fentanyl analogs.•They achieve over 90% probability of detection with 1% probability of false alarm.•These ML models outperform existing library matching techniques.•This detection capability is important for cases when Raman sensing is preferred.