Dong Ji-Jing, Qi Lu-Ming, Wang Ke, Ding Yu-Gang, Ma Yun-Tong
State Key Laboratory of Southwestern Chinese Medicine Resource Chengdu 611137, China School of Pharmacy, Chengdu University of Traditional Chinese Medicine Chengdu 611137, China.
Health and Rehabilitation College, Chengdu University of Traditional Chinese Medicine Chengdu 610075, China.
Zhongguo Zhong Yao Za Zhi. 2023 May;48(10):2713-2724. doi: 10.19540/j.cnki.cjcmm.20221210.102.
The grey correlation-TOPSIS method was used to evaluate the quality of the origin herbs of Lonicerae Japonicae Flos, and the Fourier transform near-infrared(NIR) and mid-infrared(MIR) spectroscopy was applied to establish the identification model of origin herbs of Lonicerae Japonicae Flos by combining chemometrics and spectral fusion strategies. The content of neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, caffeic acid, secoxyloganin, isoquercitrin, isochlorogenic acid B, isochlorogenic acid A, and isochlorogenic acid C in six origin herbs of Lonicerae Japonicae Flos was determined by high-performance liquid chromatography(HPLC), and their quality was evaluated by the grey correlation-TOPSIS method. The Fourier transform NIR and MIR spectra of six origin herbs of Lonicerae Japonicae Flos(Lonicera japonica, L. macranthoides, L. hypoglauca, L. fulvotomentosa, L. confuse, and L. similis) were collected. At the same time, principal component analysis(PCA), support vector machine(SVM), and spectral data fusion technology were combined to determine the optimal identification method for the origin herbs of Lonicerae Japonicae Flos. There were differences in the quality of the origin herbs of Lonicerae Japonicae Flos. Specifically, there were significant differences between L. japonica and the other five origin herbs(P<0.01). The quality of L. similis was significantly different from that of L. fulvotomentosa, L. macranthoides, and L. hypoglauca(P=0.008, 0.027, 0.01), and there were also significant differences in the quality of L. hypoglauca and L. confuse(P=0.001). The PCA and SVM 2D models based on a single spectrum could not be used for the effective identification of the origin herbs of Lonicerae Japonicae Flos. The data fusion combined with the SVM model further improved the identification accuracy, and the identification accuracy of the mid-level data fusion reached 100%. Therefore, the grey correlation-TOPSIS method can be used to evaluate the quality of the origin herbs of Lonicerae Japonicae Flos. Based on the infrared spectral data fusion strategy and SVM chemometric model, it can accurately identify the origin herbs of Lonicerae Japonicae Flos, which can provide a new method for the origin identification of medicinal materials of Lonicerae Japonicae Flos.
采用灰色关联 - TOPSIS法评价金银花道地药材的质量,并应用傅里叶变换近红外(NIR)和中红外(MIR)光谱,结合化学计量学和光谱融合策略,建立金银花道地药材的鉴别模型。采用高效液相色谱(HPLC)法测定6种金银花道地药材中新绿原酸、绿原酸、隐绿原酸、咖啡酸、裂环马钱苷、异槲皮苷、异绿原酸B、异绿原酸A和异绿原酸C的含量,并通过灰色关联 - TOPSIS法对其质量进行评价。采集了6种金银花道地药材(忍冬、大花忍冬、淡红忍冬、黄褐毛忍冬、皱叶忍冬和细毡毛忍冬)的傅里叶变换NIR和MIR光谱。同时,结合主成分分析(PCA)、支持向量机(SVM)和光谱数据融合技术,确定金银花道地药材的最佳鉴别方法。金银花道地药材的质量存在差异。具体而言,忍冬与其他5种道地药材之间存在显著差异(P<0.01)。细毡毛忍冬与黄褐毛忍冬、大花忍冬和淡红忍冬的质量有显著差异(P = 0.008、0.027、0.01),淡红忍冬和皱叶忍冬的质量也有显著差异(P = 0.001)。基于单光谱的PCA和SVM二维模型不能用于金银花道地药材的有效鉴别。数据融合结合SVM模型进一步提高了鉴别准确率,中级数据融合的鉴别准确率达到100%。因此,灰色关联 - TOPSIS法可用于评价金银花道地药材的质量。基于红外光谱数据融合策略和SVM化学计量模型,能够准确鉴别金银花道地药材,可为金银花药材的产地鉴别提供新方法。