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Raman Activity Investigation and Utilization of Psilocybin

Mingyu Sun, Xiaoyu Zhao, Fandi Kong, Jinsong Wang, Yunchang Lu, Liang Tong

Journal of Raman Spectroscopy March 19, 2026 DOI: 10.1002/jrs.70133 (opens in new tab)

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Study at a glance

AI-extracted from the abstract
Characteristics Experimental study Peer reviewed
Population Psilocybe cubensis mushroom samples
Topics Psilocybin
Keywords Raman spectroscopy Pattern recognition psychology Interpretability Sensitivity control systems Discriminative model Identification biology Fingerprint computing Artificial intelligence Biological system Support vector machine Mushroom Noise video False alarm Feature linguistics Analytical chemistry journal Feature extraction
Key findings A Bagging-based machine learning model integrating XGBoost, KNN, SVM, and Decision Tree achieved 0.984 accuracy and 0.976 ROC AUC for identifying psilocybin using Raman spectroscopy.

Abstract

ABSTRACT Accidental ingestion of psilocybin‐containing mushrooms can cause poisoning and hallucinations, making their rapid detection a public health priority. Conventional methods such as HPLC, GC–MS, LC–MS, TLC, CE, and ELISA provide sensitivity but are often destructive, time‐consuming, or impractical for real‐time applications. This study introduces a Raman spectroscopy–based approach for rapid, nondestructive identification of psilocybin. The molecular geometry of psilocybin was optimized using density functional theory (B3LYP/6‐31G(d,p)), and theoretical Raman spectra were generated to assign characteristic vibrational peaks, confirming its Raman activity. Experimental spectra of fresh and heat‐treated Psilocybe cubensis mushroom samples were collected, with peak alignment demonstrating good agreement with theoretical predictions, thus establishing psilocybin fingerprint features. For classification, raw and preprocessed spectra (MSC, SNV, 1st‐D, detrending) were evaluated. Among feature extraction methods (PCA, SPA, UVE, CARS), CARS yielded the most discriminative variables. An XGBoost model was developed and optimized via Bayesian tuning, while SMOTE addressed class imbalance. Furthermore, psilocybin fingerprint features were fused with CARS features to enhance interpretability and model robustness. Finally, a Bagging framework integrating XGBoost, KNN, SVM, and Decision Tree was implemented to improve generalization and noise resistance. The final Bagging‐based model achieved high performance (accuracy 0.984, F1‐score 0.984, ROC AUC 0.976), with strong stability under storage conditions. Overall, this study elucidates psilocybin's Raman spectral characteristics and establishes a machine learning–assisted detection model. The approach enables rapid, accurate, cost‐effective, and contamination‐free identification of psilocybin, with potential applications in food safety monitoring and on‐site toxic mushroom screening.

Comparable studies

Other experimental studies on psilocybin, most cited first.

Study Year Design Participants
Psilocybin induces schizophrenia-like psychosis in humans via a serotonin-2 agonist action Healthy human volunteers 1998 Experimental study
Validation of the revised Mystical Experience Questionnaire in experimental sessions with psilocybin Participants in controlled laboratory experiments receiving a moderate to high oral... 2015 Validation study using pooled data from five laboratory experiments n = 184
Broadband Cortical Desynchronization Underlies the Human Psychedelic State Healthy human participants 2013 Experimental study
Long-term follow-up of psilocybin-assisted psychotherapy for psychiatric and existential distress in patients with life-threatening cancer Participants with cancer-related psychiatric distress who completed a parent randomized... 2020 Long-term within-subjects follow-up analysis n = 15
Enzymatic Synthesis of Psilocybin 2017 Laboratory study

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