Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy.

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.

[Plant metabolomics approach for age discrimination of mountain cultivated ginseng using UPLC-Q-TOF/MS].

Growth year is one of the important factors for the quality of mountain cultivated ginseng (MCG). For age differentiation of MCG, rhizome extracts of ginseng aged from 11 to 15 years were analyzed using a non-targeted approach with ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS)-based on plant metabolomics technique. Multivariate statistical methods such as principal component analysis (PCA) and orthogonal partial least squared discrimination analysis (OPLS-DA) were used to compare the derived patterns among the samples. The results showed that the chemical constituents of MCG rhizome extracts of ginseng aged from 11 to 15 years were different. The data set was subsequently applied to metabolite selection by variable importance in the projection (VIP) for sophisticated classification with the optimal number of metabolites. The OPLS-DA model of MCG has a high interpretability and predictive capability, which established by selecting metabolites of MCG aged from 11 to 15 years. By this approach, MCG samples aged from 11 to 15 years, which are the most in demand in the Chinese ginseng market, can be precisely differentiated on the basis of selected metabolites. This proposed analytical method is fast, accurate, and reliable for discriminating the growth year of MCG. Moreover, this study supplies a new method for the age discrimination of other Chinese medicinal materials.

[Application of metabolomics approach to study of different parts of Mountain Cultivated Ginseng using UHPLC-QTOF/MS].

In present study, an ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS)-based plant metabolomics approach was established to investigate the metabolic profiles of the leaves, main root, branch root, and rhizome of Mountain Cultivated Ginseng (MCG). The UHPLC-QTOF/MS data were subjected to principal component analysis(PCA) and orthogonal partial least squared discrimination analysis(OPLS-DA) to find the potential characteristic components of the four parts of MCG in a quick way. The four different parts could be separated into four different groups of phytochemicals according to the PCA scores. The chemical constituents in four parts of MCG were obviously different. The identities of 81 major peaks that were detected in the four parts of MCG and the potential markers were identified by comparison with the reference compounds or were tentatively assigned by matching the retention time, empirical molecular formula and fragment ions with those of the published compounds of the Panax species. This proposed analytical method is fast, accurate, and reliable for differentiating the different parts of MCG. Moreover, this study supplied a new method for the quality evaluation of other Chinese medicinal materials.