Identification of geographical origins of Ophiocordyceps sinensis (Berk.) via machine learning-assisted surface-enhanced Raman spectroscopy.

Ophiocordyceps sinensis (Berk.) is a functional food with health. O. sinensis quality varies by geographical origins, and current identification methods are sophisticated and time-consuming. This study aims to develop a rapid and straightforward method for accurately identifying O. sinensis geographic origins. Surface-enhanced Raman spectroscopy (SERS) was applied to analyze O. sinensis from four major production areas in China. Liquid chromatography-mass spectrometry (LC-MS) was used as a reference method to characterize compositional differences among samples and to verify the geographical authenticity of O. sinensis from the four production areas. Six machine learning (ML) algorithms were introduced for predicting geographical origins, and evaluation metrics were used to assess model performance. According to the comparative analysis, the Support Vector Machine (SVM) model performed best with the highest discrimination accuracy. A feature importance map was constructed to understand further how the model makes predictive decisions, revealing the significant Raman shifts in classifying O. sinensis from different geographical origins. The SERS-SVM method developed in this study contributes to the authenticity identification of O. sinensis geographical origins. It shows the potential to serve as an effective quality control method for the valuable TCM (traditional Chinese medicine).