Simple and Rapid Differentiation of Mutong of Aristolochiaceae by Direct Spray Mass Spectrometry / 分析化学

Direct spray mass spectrometry was used to simply and rapidly differentiate Mutong of Aristolochiaceae from other two kinds of Mutong medicinal materials (Lardizabalaceae and Ranunculacea) by analyzing the chemical profile of Mutong of Aristolochiaceae.A novel method for determination of magnoflorine content in Mutong of Aristolochiaceae was established.The results showed that Mutong of Aristolochiaceae could be identified according to the symbolic component, magnoflorine.Under positive ion mode, semi-quantitative result based on the signal strength ratio of magnoflorine and nuciferin was obtained by choosing nuciferin as an internal standard.The method showed good linear coefficient in the concentration range of 0.50-20.00 mg/L of magnoflorine.The limit of detection was 0.1 mg/L.The method was simple and fast, and could be used for direct and rapid in-situ analysis and identification of Mutong of Aristolochiaceae from other closely related Mutong herbal species without sample pre-treatment.The results were important for the quality control of Mutong herbal medicine.

Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus / 药学学报

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.