Raman Activity Investigation and Utilization of Psilocybin
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.