Journal of Raman Spectroscopy
May 28, 2026
A new detection method using surface-enhanced Raman spectroscopy with silver-coated gold nanoparticles can identify psilocybin and psilocin in human urine at extremely low concentrations. The technique achieved detection limits of 1.11 × 10⁻¹⁰ mol·L⁻¹ for psilocybin and 6.75 × 10⁻¹² mol·L⁻¹ for psilocin. In tests with real urine samples, recovery rates ranged from 80.79% to 109.53% for psilocybin and 83.98% to 106.78% for psilocin, with relative standard deviations below 9%, indicating good accuracy and stability. This approach may be useful for drug enforcement, clinical toxicology, and monitoring psychoactive substances.
Journal of Raman Spectroscopy
March 19, 2026
Mingyu Sun, Xiaoyu Zhao, Fandi Kong et al.
A Raman spectroscopy method combined with machine learning can rapidly and non-destructively identify psilocybin, the psychoactive compound in magic mushrooms. Theoretical Raman spectra predicted by density functional theory matched experimental spectra from fresh and heat-treated Psilocybe cubensis samples, establishing characteristic fingerprint features. Among feature extraction methods, competitive adaptive reweighted sampling (CARS) selected the most discriminative variables. An XGBoost model, optimized with Bayesian tuning and balanced via SMOTE, was integrated into a Bagging framework with KNN, SVM, and Decision Tree. The final model achieved 0.984 accuracy, 0.984 F1-score, and 0.976 ROC AUC, showing strong stability under storage conditions. This approach enables rapid, accurate, cost-effective, and contamination-free psilocybin detection for food safety and toxic mushroom screening.