Perimenopausal and Menopausal Mammary Glands In A 4-Vinylcyclohexene Diepoxide Mouse Model.

As both perimenopausal and menopausal periods are recognized critical windows of susceptibility for breast carcinogenesis, development of a physiologically relevant model has been warranted. The traditional ovariectomy model causes instant removal of the entire hormonal repertoire produced by the ovary, which does not accurately approximate human natural menopause with gradual transition. Here, we characterized the mammary glands of 4-vinylcyclohexene diepoxide (VCD)-treated animals at different time points, revealing that the model can provide the mammary glands with both perimenopausal and menopausal states. The perimenopausal gland showed moderate regression in ductal structure with no responsiveness to external hormones, while the menopausal gland showed severe regression with hypersensitivity to hormones. Leveraging the findings on the VCD model, effects of a major endocrine disruptor (polybrominated diphenyl ethers, PBDEs) on the mammary gland were examined during and after menopausal transition, with the two exposure modes; low-dose, chronic (environmental) and high-dose, subacute (experimental). All conditions of PBDE exposure did not augment or compromise the macroscopic ductal reorganization resulting from menopausal transition and/or hormonal treatments. Single-cell RNA sequencing revealed that the experimental PBDE exposure during the post-menopausal period caused specific transcriptomic changes in the non-epithelial compartment such as Errfi1 upregulation in fibroblasts. The environmental PBDE exposure resulted in similar transcriptomic changes to a lesser extent. In summary, the VCD mouse model provides both perimenopausal and menopausal windows of susceptibility for the breast cancer research community. PBDEs, including all tested models, may affect the post-menopausal gland including impacts on the non-epithelial compartments.

Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR.

INTRODUCTION: Wolfiporia cocos, as a kind of medicine food homologous fungus, is well-known and widely used in the world. Therefore, quality and safety have received worldwide attention, and there is a trend to identify the geographic origin of herbs with artificial intelligence technology. OBJECTIVE: This research aimed to identify the geographical traceability for different parts of W. cocos. METHODS: The exploratory analysis is executed by two multivariate statistical analysis methods. The two-dimensional correlation spectroscopy (2DCOS) images combined with residual convolutional neural network (ResNet) and partial least square discriminant analysis (PLS-DA) models were established to identify the different parts and regions of W. cocos. We compared and analysed 2DCOS images with different fingerprint bands including full band, 8900-6850 cm-1 , 6300-5150 cm-1 and 4450-4050 cm-1 of original spectra and the second-order derivative (SD) spectra preprocessed. RESULTS: From all results: the exploratory analysis results showed that t-distributed stochastic neighbour embedding was better than principal component analysis. The synchronous SD 2DCOS is more suitable for the identification and analysis of complex mixed systems for the small-band for Poria and Poriae cutis. Both models of PLS-DA and ResNet could successfully identify the geographical traceability of different parts based on different bands. The 10% external verification set of the ResNet model based on synchronous 2DCOS can be accurately identified. CONCLUSION: Therefore, the methods could be applied for the identification of geographical origins of this fungus, which may provide technical support for quality evaluation.

Extended application of deep learning combined with 2DCOS: Study on origin identification in the medicinal plant of Paris polyphylla var. yunnanensis.

INTRODUCTION: Medicinal plants are very important to human health, and ensuring their quality and rapid evaluation are the current research concerns. Deep learning has a strong ability in recognition. This study extended it to the identification of medicinal plants from the perspective of spectrum. OBJECTIVE: In order to realise the rapid identification and provide a reference for the selection of high-quality resources of medicinal plants, a combination of deep learning and two-dimensional correlation spectroscopy (2DCOS) was proposed. METHODS: For the first time, Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR) spectroscopy 2DCOS images combined with residual neural network (ResNet) was used for the origin identification of Paris polyphylla var. yunnanensis. In total 1593 samples were collected and 12821 2DCOS images were drawn. The climate of different origins was briefly analysed. RESULTS: The xishuangbanna, puer, lincang, honghe and wenshan are the five regions with more ecological advantages. The synchronous 2DCOS models of FT-MIR and NIR could realise origin identification with the accuracy of 100%. The synchronous images were suitable for the identification of medicinal plants with complex systems. The full band, feature band and different contour models had no big difference in distinguishing ability, so they were not the key factors affecting the discrimination results. CONCLUSION: The ResNet models established were stable, reliable, and robust, which not only solved the problem of origin identification, expanded the application field of deep learning, but also provided practical reference for the related research of other medicinal plants.

Anticoagulant activity analysis and origin identification of Panax notoginseng using HPLC and ATR-FTIR spectroscopy.

INTRODUCTION: Panax notoginseng is one of the traditional precious and bulk-traded medicinal materials in China. Its anticoagulant activity is related to its saponin composition. However, the correlation between saponins and anticoagulant activities in P. notoginseng from different origins and identification of the origins have been rarely reported. OBJECTIVES: We aimed to analyze the correlation of components and activities of P. notoginseng from different origins and develop a rapid P. notoginseng origin identification method. MATERIALS AND METHODS: Pharmacological experiments, HPLC, and ATR-FTIR spectroscopy (variable selection) combined with chemometrics methods of P. notoginseng main roots from four different origins (359 individuals) in Yunnan Province were conducted. RESULTS: The pharmacological experiments and HPLC showed that the saponin content of P. notoginseng main roots was not significantly different. It was the highest in main roots from Wenshan Prefecture (9.86%). The coagulation time was prolonged to observe the strongest effect (4.99 s), and the anticoagulant activity was positively correlated with the contents of the three saponins. The content of ginsenoside Rg1 had the greatest influence on the anticoagulant effect. The results of spectroscopy combined with chemometrics show that the variable selection method could extract a small number of variables containing valid information and improve the performance of the model. The variable importance in projection has the best ability to identify the origins of P. notoginseng; the accuracy of the training set and the test set was 0.975 and 0.984, respectively. CONCLUSION: This method is a powerful analytical tool for the activity analysis and identification of Chinese medicinal materials from different origins.

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization.

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000-4000 cm-1) and Fourier transform mid-infrared (MIR: 4000-600 cm-1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen's kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

Structural characterisation and discrimination of the aerial parts of Paris polyphylla var. yunnanensis and Paris polyphylla var. chinensis by UHPLC-QTOF-MS coupled with multivariate data analysis.

INTRODUCTION: As sources of Rhizoma Paridis are facing shortages, utilising the aerial parts of Paris polyphylla has emerged as a promising additional source. However, the components in the aerial parts still need to be explored, and it is difficult to distinguish the aerial parts of P. polyphylla Smith var. yunnanensis (PPY) and P. polyphylla var. chinensis (PPC), two varieties of P. polyphylla. OBJECTIVE: This study aimed to establish a comprehensive platform to characterise steroid saponins from the aerial parts of PPY and PPC and to discriminate these two varieties. METHODOLOGY: A dereplication approach and ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) analysis were used for the characterisation of steroidal saponins in the aerial parts of PPY and PPC. Multivariate statistical analysis was performed to differentiate these two varieties and screen discriminant variables. In addition, a genetic algorithm-optimised for support vector machines (GA-SVM) model was developed to predict P. polyphylla samples. The distribution of steroidal saponins in PPY and PPC was visualised by a heatmap. RESULTS: A total of 102 compounds were characterised from the aerial parts of PPY and PPC by dereplication. A clear separation of PPY and PPC was achieved, and 35 saponins were screened as marker compounds. The established GA-SVM model showed excellent prediction performance with a prediction accuracy of 100%. CONCLUSIONS: Many steroid saponins that have been reported in Rhizoma Paridis also exist in the aerial parts of P. polyphylla. Samples from the aerial parts of PPY and PPC could be discriminated using our platform.

Assessing Geographical Origin of Gentiana Rigescens Using Untargeted Chromatographic Fingerprint, Data Fusion and Chemometrics.

Gentiana rigescens Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of G. rigescens and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies ("low-level" and "mid-level"). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities (the accuracy is higher than 99% and sensibility, specificity, Matthews correlation coefficient, and efficiency range from 0.95 to 1.00). Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of G. rigescens and developed a reliable and accurate identification method for G. rigescens that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials.

Geographical Authentication of Macrohyporia cocos by a Data Fusion Method Combining Ultra-Fast Liquid Chromatography and Fourier Transform Infrared Spectroscopy.

Macrohyporia cocos is a medicinal and edible fungi, which is consumed widely. The epidermis and inner part of its sclerotium are used separately. M. cocos quality is influenced by geographical origins, so an effective and accurate geographical authentication method is required. Liquid chromatograms at 242 nm and 210 nm (LC242 and LC210) and Fourier transform infrared (FTIR) spectra of two parts were applied to authenticate the geographical origin of cultivated M. cocos combined with low and mid-level data fusion strategies, and partial least squares discriminant analysis. Data pretreatment involved correlation optimized warping and second derivative. The results showed that the potential of the chromatographic fingerprint was greater than that of five triterpene acids contents. LC242-FTIR low-level fusion took full advantage of information synergy and showed good performance. Further, the predictive ability of the FTIR low-level fusion model of two parts was satisfactory. The performance of the low-level fusion strategy preceded those of the single technique and mid-level fusion strategy. The inner parts were more suitable for origin identification than the epidermis. This study proved the feasibility of the data fusion of chromatograms and spectra, and the data fusion of different parts for the accurate authentication of geographical origin. This method is meaningful for the quality control of food and the protection of geographical indication products.

Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species.

Due to the existence of Lingzhi adulteration, there is a growing demand for species classification of medicinal mushrooms by various techniques. The objective of this study was to explore a rapid and reliable way to distinguish between different Lingzhi species and compare the influence of data pretreatment methods on the recognition results. To this end, 120 fresh fruiting bodies of Lingzhi were collected, and all of them were analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Random forest (RF), support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification models were established for raw and pretreated second derivative (SD) spectral matrices to authenticate different Lingzhi species. The results of multivariate statistical analysis indicated that the SD preprocessing method displayed a higher classification ability, which may be attributed to the analysis of powder samples that requires removal of overlapping peaks and baseline shifts. Compared with RF, the results of the SVM and PLS-DA methods were more satisfying, and their accuracies for the test set were both 100%. Among SVM and PLS-DA, the training set and test set accuracy of PLS-DA were both 100%. In conclusion, ATR-FTIR spectroscopy data pretreated by SD combined with PLS-DA is a simple, rapid, non-destructive and relatively inexpensive method to discriminate between mushroom species and provide a good reference to quality assessment.

Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis.

Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis-PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.

[Research progress in chemical constituents in plants of Polygonatum and their pharmacological effects].

Polygonatum plants are perennial plants of Liliaceae. There are about 60 species reported at home and abroad,32 species in China,mainly distributed in the north temperate zone. Their main chemical components are steroidal saponins,polysaccharides,flavonoids,alkaloids,etc. They have anti-aging,anti-tumor,immunomodulation,antibacterial,antiviral,hypoglycemic and blood lipid effects. With the development of health industry,Polygonati Rhizome used as medicine and food has attracted great attention in recent years,and has become a research hotspot. However,the material basis of its efficacy is unclear and the product quality is uneven,which seriously limited the rapid upgrading of the industry. This review summarizes Polygonatum plants system classification,the chemical composition and pharmacological activity to provide theoretical basis for the development and utilization of Polygonatum plants.

FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng.

Three data fusion strategies (low-llevel, mid-llevel, and high-llevel) combined with a multivariate classification algorithm (random forest, RF) were applied to authenticate the geographical origins of Panax notoginseng collected from five regions of Yunnan province in China. In low-level fusion, the original data from two spectra (Fourier transform mid-IR spectrum and near-IR spectrum) were directly concatenated into a new matrix, which then was applied for the classification. Mid-level fusion was the strategy that inputted variables extracted from the spectral data into an RF classification model. The extracted variables were processed by iterate variable selection of the RF model and principal component analysis. The use of high-level fusion combined the decision making of each spectroscopic technique and resulted in an ensemble decision. The results showed that the mid-level and high-level data fusion take advantage of the information synergy from two spectroscopic techniques and had better classification performance than that of independent decision making. High-level data fusion is the most effective strategy since the classification results are better than those of the other fusion strategies: accuracy rates ranged between 93% and 96% for the low-level data fusion, between 95% and 98% for the mid-level data fusion, and between 98% and 100% for the high-level data fusion. In conclusion, the high-level data fusion strategy for Fourier transform mid-IR and near-IR spectra can be used as a reliable tool for correct geographical identification of P. notoginseng. Graphical abstract The analytical steps of Fourier transform mid-IR and near-IR spectral data fusion for the geographical traceability of Panax notoginseng.

Comparison and Identification for Rhizomes and Leaves of Paris yunnanensis Based on Fourier Transform Mid-Infrared Spectroscopy Combined with Chemometrics.

Paris polyphylla, as a traditional herb with long history, has been widely used to treat diseases in multiple nationalities of China. Nevertheless, the quality of P. yunnanensis fluctuates among from different geographical origins, so that a fast and accurate classification method was necessary for establishment. In our study, the geographical origin identification of 462 P. yunnanensis rhizome and leaf samples from Kunming, Yuxi, Chuxiong, Dali, Lijiang, and Honghe were analyzed by Fourier transform mid infrared (FT-MIR) spectra, combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. The obvious cluster tendency of rhizomes and leaves FT-MIR spectra was displayed by principal component analysis (PCA). The distribution of the variable importance for the projection (VIP) was more uniform than the important variables obtained by RF, while PLS-DA models obtained higher classification abilities. Hence, a PLS-DA model was more suitably used to classify the different geographical origins of P. yunnanensis than the RF model. Additionally, the clustering results of different geographical origins obtained by HCA dendrograms also proved the chemical information difference between rhizomes and leaves. The identification performances of PLS-DA and the RF models of leaves FT-MIR matrixes were better than those of rhizomes datasets. In addition, the model classification abilities of combination datasets were higher than the individual matrixes of rhizomes and leaves spectra. Our study provides a reference to the rational utilization of resources, as well as a fast and accurate identification research for P. yunnanensis samples.

[Data fusion and multi-components quantitative analysis for identification and quality evaluation of Gentiana rigescens from different geographical origins].

The accumulation of secondary metabolites of traditional Chinese medicine (TCM) is closely related to its origins. The identification of origins and multi-components quantitative evaluation are of great significance to ensure the quality of medicinal materials. In this study, the identification of Gentiana rigescens from different geographical origins was conducted by data fusion of Fourier transform infrared (FTIR) spectroscopy and high performance liquid chromatography (HPLC) in combination of partial least squares discriminant analysis; meanwhile quantitative analysis of index components was conducted to provide an accurate and comprehensive identification and quality evaluation strategy for selecting the best production areas of G. rigescens. In this study, the FTIR and HPLC information of 169 G. rigescens samples from Yunnan, Sichuan, Guangxi and Guizhou Provinces were collected. The raw infrared spectra were pre-treated by multiplicative scatter correction, standard normal variate (SNV) and Savitzky-Golay (SG) derivative. Then the performances of FTIR, HPLC, and low-level data fusion and mid-level data fusion for identification were compared, and the contents of gentiopicroside, swertiamarin, loganic acid and sweroside were determined by HPLC. The results showed that the FTIR spectra of G. rigescens from different geographical origins were different, and the best pre-treatment method was SNV+SG-derivative (second derivative, 15 as the window parameter, and 2 as the polynomial order). The results showed that the accuracy rate of low- and mid-level data fusion (96.43%) in prediction set was higher than that of FTIR and HPLC (94.64%) in prediction set. In addition, the accuracy of low-level data fusion (100%) in the training set was higher than that of mid-level data fusion (99.12%) in training set. The contents of the iridoid glycosides in Yunnan were the highest among different provinces. The average content of gentiopicroside, as a bioactive marker in Chinese pharmacopoeia, was 47.40 mg·g⁻¹, and the maximum was 79.83 mg·g⁻¹. The contents of loganic acid, sweroside and gentiopicroside in Yunnan were significantly different from other provinces (P<0.05). In comparison of total content of iridoid glycosides in G. rigescens with different geographical origins in Yunnan, it was found that the amount of iridoid glycosides was higher in Eryuan Dali (68.59 mg·g⁻¹) and Yulong Lijiang (66.68 mg·g⁻¹), significantly higher than that in Wuding Chuxiong (52.99 mg·g⁻¹), Chengjiang Yuxi (52.29 mg·g⁻¹) and Xundian Kunming (46.71 mg·g⁻¹) (P<0.05), so these two places can be used as a reference region for screening cultivation and excellent germplasm resources of G. rigescens. A comprehensive and accurate method was established by data fusion of HPLC-FTIR and quantitative analysis of HPLC for identification and quality evaluation of G. rigescens, which could provide a support for the development and utilization of G. rigescens.

Qualitative and quantitative analysis of chemical constituents in Ardisiae Japonicae Herba.

Ardisiae Japonicae Herba is a well-known traditional Chinese medicine for the treatment of bronchitis conjunctivitis, pneumonia, and trauma. In this work, a high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry method was first established for the separation and structural identification of the chemical constituents in Ardisiae Japonicae Herba. A total of 15 compounds including coumarins, flavonoid glycosides, and catechins were identified or tentatively characterized based on their chromatographic behaviors and mass spectral fragmentation and by comparisons with the reference standards. Furthermore, a simple high-performance liquid chromatography with diode array detection method was developed for the simultaneous determination of five major constituents. Results obtained from method validation, including linearity, precision, repeatability, stability, and recovery, showed that the established method was reliable and accurate. Bergenin and quercitrin were found to be the most abundant constituents and could be served as chemical markers for quality control of Ardisiae Japonicae Herba.

Quantitative determination and evaluation of Paris polyphylla var. yunnanensis with different harvesting times using UPLC-UV-MS and FT-IR spectroscopy in combination with partial least squares discriminant analysis.

A rapid method was developed and validated by ultra-performance liquid chromatography-triple quadrupole mass spectroscopy with ultraviolet detection (UPLC-UV-MS) for simultaneous determination of paris saponin I, paris saponin II, paris saponin VI and paris saponin VII. Partial least squares discriminant analysis (PLS-DA) based on UPLC and Fourier transform infrared (FT-IR) spectroscopy was employed to evaluate Paris polyphylla var. yunnanensis (PPY) at different harvesting times. Quantitative determination implied that the various contents of bioactive compounds with different harvesting times may lead to different pharmacological effects; the average content of total saponins for PPY harvested at 8 years was higher than that from other samples. The PLS-DA of FT-IR spectra had a better performance than that of UPLC for discrimination of PPY from different harvesting times.

[Application of FTIR and Active Ingredients Quantitative Analysis on Quality Control of Dai Medicine Alstonia scholaris (L.) R. Br.].

FTIR fingerprint of the leaves and immature stems of Alstonia scholaris (L.) R. Br. was established as a content determination method for the detection of picrinine, ursolic acid and oleanolic acid. Different medicinal parts were identified based on principal component analysis, while exploring the influence of immature stems for the leaves and the application of FTIR and HPLC in the Dai quality control in order to speed up the pace of Dai medicine modernization. Infrared spectroscopy of different batches samples were collected and the data was preprocessed as to automatic baseline correction, smooth, ordinate normalization, second order derivative, and then to PCA, all the datum in triplicate. For content determination of picrinine, mobile phase was acetonitrile (40) water (contain 0.1% ammonia water) (60) and the wavelength was set at 287 nm. For ursolic acid and oleanolic acid, the mobile phase was mixture (12∶88) of 0.1% formic acid in water (A) and methanol (B). Wavelength was 210 nm. As the results, the original spectrum difference was not obvious for leaves and stems. Pretreatment spectroscopy had a significant variation on absorption peak number and intensity in 3 000～2 800 and 1 800～500 cm(-1). The results of PCA showed that, the leaves and stems were separated; in addition the difference of different batches leaves was bigger than the stems. The mean contents of picrinine, ursolic acid and oleanolic acid in leaves were 0.79，8.47，7.51 and 0.21，1.78，1.67 mg·g(-1) in stems, respectively. The content of ursolic acid and oleanolic acid is higher than picrinine, but ursolic acid and oleanolic acid content had no obvious difference. Mean content of three ingredients in leaves is much higher than in stems. Picrinine content in leaves was 3.8 times of immature stems, ursolic acid and oleanolic acid content were 5.1 and 4.2 times of immature stems, respectively. The variety of picrinine content in different batches samples was biggest, ursolic acid and oleanolic acid content was relatively stable. The overall quality of leaves has an obvious difference compared with the immature stems, so the leaves of A. scholaris mix with immature stems could not be as Dai medicine in Dai clinic. Infrared spectroscopy combined with chromatography can quickly identify different medicinal parts and evaluate overall quality of Dai medicine, which can apply to quality control of Dai medicine.

[Rapid Prediction Study of Total Flavonids Content in Panax notoginseng Using Infrared Spectroscopy Combined with Chemometrics].

The variation on origin and growth environment could make a holistic impact on the secondary metabolites and quality of traditional Chinese medicine. In recent years, the origin of Panax notoginseng is spread from the genuine producing area of Wenshan to surrounding cities. The content of three saponins, as an indicator, is to ensure the quality of Panax notoginseng in Chinese pharmacopoeia. However, a single indicator is limited to comprehensive quality evaluation of Panax notoginseng. In this study, the total flavonoids content of Panax notoginseng was determinated by ultraviolet-visible (UV-Vis) spectrophotometry, Fourier transform infrared (FTIR) spectroscopy combined with chemometrics, as a rapid prediction model of total flavonoids content, was establish to provide some basic information for rapid and holistic quality assessment of Panax notoginseng. A total of 96 UV-Vis and FTIR spectra of Panax notoginseng originated from 12 regions were collected. The UV-Vis spectra of samples were recorded at 268 nm, and the content of total flavonoids was calculated based on standard linear equation of rutin. Pre-processing data were calculated with first (1D) and second derivative (2D), Savitsky-Golay smoothing with seven, nine, and eleven points. 2/3 of the 96 individuals were selected to form the training set by using Kennard-stone algorithm, and the rest were used as prediction set. Training set data were used to establish the orthogonal signal correction-partial least squares regression (OSC-PLSR) model and the 1/7 cross-validation method was used for screening optimal numbers of principal component, the prediction set was utilized to verify the accuracy and reliability of the OSC-PLSR model. Results showed that: (1) The correlation coefficient r of standard rutin was 0.9997, and the linear concentration range was from 5.6 to 72.0 µg·mL(-1), namely, there were good correlation between the absorbance and concentration. (2) The Panax notoginseng contained higher content of total flavonoids (more than 7 mg·g(-1)) in three genuie producing areas of Wenshan, Luoping county and Shilin county. (3) After the same points of Savitsky-Golay smoothing, the model predictive ability of 2D is better than that of 1D, and the predictive ability of different processing model has an obvious difference. (4) In all prediction models, the 2D+SG 7+OSC-PLSR (R(2)(pre)=0.976 1, RMSEP=0.325 2) and 2D+SG 11+OSC-PLSR (R(2)(pre)=0.946 9, RMSEP=0.382 0) model showed an excellent predictive effect, the value of RMSEP was below 0.4, and the predicted values were close to the detection values. The result indicated that FTIR combined with OSC-PLSR could accurately predict the content of total flavonoids. It could provide a rapid, simple, and effective method for the holistic quality control of Panax notoginseng.

[Rapid prediction of the content of polyphyllin in various species of Paris by infrared spectrometry].

Polyphyllin is the main active constituent in Paris which was a traditional Chinese medicine. In order to evaluate the quality of Paris rapidly and ensure the efficacy in clinical therapy, we quantified the contents of polyphyllin Ⅰ, polyphyllin Ⅱ and polyphyllin Ⅶ using infrared spectroscopy with partial least squares regression(PLSR). The method for evaluating the quality of Paris was established. Infrared spectra of 78 samples from various species in different origins were collected. The contents of polyphyllin Ⅰ, Ⅱ and Ⅶ were determined by high performance liquid chromatography(HPLC). The HPLC data were combined with the spectral data to predict the contents of three polyphyllin rapidly. Multiplicative signal correction(MSC), standard normal variate(SNV), orthogonal signal correction(OSC), first derivative and second derivative were utilized for the spectral preprocessing. Then, the optimized spectral data were used to establish the quantitative prediction model based on PLSR. The results showed that the best spectral pretreatment of polyphyllin Ⅰ and Ⅱ were MSC+OSC+2nd Der and that of polyphyllin Ⅶ was MSC+SNV+OSC+2nd Der. In the quantitative calibration model, the determination coefficients (R²) of polyphyllin Ⅰ, polyphyllin Ⅱ and polyphyllin Ⅶ were 0.930 8, 0.934 8 and 0.912 3, respectively while the Root mean square error of estimation(RMSEE) were 1.855 0, 0.632 3 and 0.001 6 mg•g⁻¹, respectively. In the verification model, the R² of polyphyllin Ⅰ, polyphyllin Ⅱ and polyphyllin Ⅶ were 0.948 8, 0.703 6 and 0.801 7, respectively, and the root mean square error of prediction(RMSEP)were 1.704 6, 1.227 8 and 0.002 0 mg•g⁻¹, respectively. Because of the predictive value of quantitative model was closed to the real value, the effect of the model was good. The model of polyphyllin Ⅰ and Ⅱ were better than that of polyphyllin Ⅶ. The developed method was non-destructive, fast, and accurate. It was feasible to determine the content of polyphyllin in Paris.

Investigation of chemical diversity in different parts and origins of ethnomedicine Gentiana rigescens Franch using targeted metabolite profiling and multivariate statistical analysis.

Gentiana rigescens, an ethnomedicine, is widely cultivated in Yunnan province of China. Although a wide range of metabolites including iridoid glycosides, flavonoids and triterpenoids have been reported in this ethnomedicine, the data on accumulation and distribution of metabolites in certain parts are limited. In this study, targeted metabolic fingerprinting of iridoid glycosides based on liquid chromatography-ultraviolet detection-tandem mass spectrometry (LC-UV-MS/MS) was developed to investigate the metabolic similarities and differences in different parts and origins. Thirty-one compounds, including iridoid glycosides and flavonoids, were detected from targeted metabolite profiling and plausibly assigned to the different parts of G. rigescens. Multivariate statistical analysis was designed to reveal close chemical similarities between all the selected samples and to identify key metabolites characteristic of the standard. The results suggested that accumulation and distribution of metabolites in aerial and underground parts were different. Moreover, root samples tended to be grouped on the basis of the geographical closeness of region. Five metabolites can be considered as potential markers for the classification of underground parts from different regions. These results provided chemical information on the potential pharmaceutical value for further research, making G. rigescens ideal for the rational usage of different parts and exploitation of the source.