A novel method based on GC-MS, near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was established to simultaneously analyze differential volatile components (DVCs) of herb samples. Herein, Florists Chrysanthemum was adopted as the representative sample. Through the introduction of Automatic data analysis workflow (AntDAS) and one-class partial least squares discriminant analysis (O-PLSDA) model, five kinds of terpenes and five kinds of alcohols were efficiently screened as DVCs. By using the selected NIR-MIR spectra sections combined with O-PLSDA, it could achieve the accurate identification of Florists Chrysanthemum from Chrysanthemum morifolium Ramat. What’s more, since the selected spectra sections were closely related to the structural and content of DVCs, they could be further used for simultaneous quantitative analysis of DVCs combined with optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). This method only adopted the same NIR-MIR sections for multiple component accurate quantification, highlighting its convenience.

建立了一种基于 GC-MS、近红外 (NIR) 和中红外 (MIR) 光谱与化学计量学相结合的新方法，用于同时分析草药样品的差异挥发性成分 (DVC)。在此，Florists Chrysanthemum被用作代表性样本。通过引入自动数据分析工作流程（AntDAS）和一类偏最小二乘判别分析（O-PLSDA）模型，高效筛选了五种萜类和五种醇作为DVC。利用选取的NIR-MIR光谱切片结合O-PLSDA，实现了菊花中菊花的准确鉴定。更重要的是，由于所选光谱部分与 DVC 的结构和含量密切相关，因此它们可以进一步用于 DVC 的同步定量分析，结合基于粒子群优化的优化可变加权最小二乘支持向量机（PSO- VWLS-支持向量机）。该方法仅采用相同的 NIR-MIR 截面进行多组分准确定量，凸显了其便捷性。