Liu Qing-Hua, Tang Jia-Wei, Ma Zhang-Wen, Hong Yong-Xuan, Yuan Quan, Chen Jie, Wen Xin-Ru, Tang Yu-Rong, Wang Liang
State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau SAR, China.
Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China.
Curr Res Food Sci. 2024 Aug 14;9:100820. doi: 10.1016/j.crfs.2024.100820. eCollection 2024.
is a genus of ascomycete fungi that has been widely used as a valuable tonic or medicine. However, due to over-exploitation and the destruction of natural ecosystems, the shortage of wild resources has led to an increase in artificially cultivated . To rapidly and accurately identify the molecular differences between cultivated and wild , this study employs surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms to distinguish the two categories. Specifically, we collected SERS spectra for wild and cultivated and validated the metabolic profiles of SERS spectra using Ultra-Performance Liquid Chromatography coupled with Orbitrap High-Resolution Mass Spectrometry (UPLC-Orbitrap-HRMS). Subsequently, we constructed machine learning classifiers to mine potential information from the spectral data, and the spectral feature importance map is determined through an optimized algorithm. The results indicate that the representative characteristic peaks in the SERS spectra are consistent with the metabolites identified through metabolomics analysis, confirming the feasibility of the SERS method. The optimized support vector machine (SVM) model achieved the most accurate and efficient capacity in discriminating between wild and cultivated (accuracy = 98.95%, 5-fold cross-validation = 98.38%, time = 0.89s). The spectral feature importance map revealed subtle compositional differences between wild and cultivated . Taken together, these results are expected to enable the application of SERS in the quality control of raw materials, providing a foundation for the efficient and rapid identification of their quality and origin.
是一种子囊菌真菌属,已被广泛用作珍贵的滋补品或药物。然而,由于过度开发和自然生态系统的破坏,野生资源的短缺导致人工栽培的增加。为了快速准确地识别栽培和野生之间的分子差异,本研究采用表面增强拉曼光谱(SERS)结合机器学习算法来区分这两类。具体而言,我们收集了野生和栽培的SERS光谱,并使用超高效液相色谱与轨道阱高分辨率质谱联用(UPLC-Orbitrap-HRMS)验证了SERS光谱的代谢谱。随后,我们构建了机器学习分类器以从光谱数据中挖掘潜在信息,并通过优化算法确定光谱特征重要性图。结果表明,SERS光谱中的代表性特征峰与通过代谢组学分析鉴定的代谢物一致,证实了SERS方法的可行性。优化后的支持向量机(SVM)模型在区分野生和栽培方面实现了最准确和高效的能力(准确率 = 98.95%,五折交叉验证 = 98.38%,时间 = 0.89秒)。光谱特征重要性图揭示了野生和栽培之间细微的成分差异。综上所述,这些结果有望使SERS应用于原材料的质量控制,为高效快速地识别其质量和来源提供基础。