An integrated chemical characterization based on FT-NIR, and GC-MS for the comparative metabolite profiling of 3 species of the genus Amomum.

BACKGROUND: The fruits and seeds of genus Amomum are well-known as medicinal plants and edible spices, and are used in countries such as China, India and Vietnam to treat malaria, gastrointestinal disorders and indigestion. The morphological differences between different species are relatively small, and technical characterization and identification techniques are needed. RESULTS: Fourier transform near infrared spectroscopy (FT-NIR) and gas chromatography-mass spectrometry (GC-MS), combined with principal component analysis and two-dimensional correlation analysis were used to characterize the chemical differences of Amomum tsao-ko, Amomum koenigii, and Amomum paratsaoko. The targets and pathways for the treatment of diabetes mellitus in three species were predicted using network pharmacology and screened for the corresponding pharmacodynamic components as potential quality markers. The results of "component-target-pathway" network showed that (+)-Nerolidol, 2-Nonanol, α-Terpineol, α-Pinene, 2-Nonanone had high degree values and may be the main active components. Partial least squares-discriminant analysis (PLS-DA) was further used to select for differential metabolites and was identified as a potential quality marker, 11 in total. PLS-DA and residual network (ResNet) classification models were developed for the identification of 3 species of the genus Amomum, ResNet model is more suitable for the identification study of large volume samples. SIGNIFICANCE: This study characterizes the differences between the three species in a visual way and also provides a reliable technique for their identification, while demonstrating the ability of FT-NIR spectroscopy for fast, easy and accurate species identification. The results of this study lay the foundation for quality evaluation studies of genus Amomum and provide new ideas for the development of new drugs for the treatment of diabetes mellitus.

Traditional uses, chemical compositions and pharmacological activities of Dendrobium: A review.

ETHNOPHARMACOLOGICAL RELEVANCE: Dendrobium is a kind of medicine food homology plant. Dendrobium has long been used to strengthen "Yin" and tonify five viscera. AIM OF THIS REVIEW: This paper presents a systematic review of the folk usage, chemical composition and pharmacological activity of Dendrobium, aiming to provide a reference for subsequent in-depth understanding and better exploitation of health food, medicine, and natural products. MATERIALS AND METHODS: Available information about the genus Dendrobium was collected via Web of Science, PubMed, Science Direct, Scopus, APA-Psy Articles, Google Scholar, Connected Papers, Springer Search, and KNCI. The keywords for this article are Dendrobium, traditional use, chemical diversity and pharmacological activity. Use the "Dictionary of Chinese Ethnic Medicine" to provide 23 kinds of Dendrobium with medicinal value, the Latin name of Dendrobium is verified by the Flora of China (www.iplant.cn), and its species distribution and related information are collected. RESULTS: There are 78 species of Dendrobium in China, 14 of which are endemic to China. At present, 450 compounds including sesquiterpenoids, lignans compounds, phenolic compounds, phenanthrene compounds, bibenzyls, polysaccharides and flavonoids have been isolated and identified from at least 50 species of Dendrobium. Among them, bibenzyls and polysaccharides are the main active components, phenolics and lignans are widely distributed, sesquiterpenes are the most common chemical constituents in genus Dendrobium plants. The most popular research objects are Dendrobium officinale and Dendrobium huoshanense. CONCLUSIONS: Based on traditional folk uses, chemical composition and pharmacological studies, Dendrobium is considered a promising medicinal and edible plant with multiple pharmacological activities. In addition, a large number of clinical applications and further studies on single chemical components based on the diversity of chemical structures should be conducted, which will lay the foundation for the scientific utilization of genus Dendrobium.

A method of two-dimensional correlation spectroscopy combined with residual neural network for comparison and differentiation of medicinal plants raw materials superior to traditional machine learning: a case study on Eucommia ulmoides leaves.

BACKGROUND: Eucommia ulmoides leaf (EUL), as a medicine and food homology plant, is a high-quality industrial raw material with great development potential for a valuable economic crop. There are many factors affecting the quality of EULs, such as different drying methods and regions. Therefore, quality and safety have received worldwide attention, and there is a trend to identify medicinal plants with artificial intelligence technology. In this study, we attempted to evaluate the comparison and differentiation for different drying methods and geographical traceability of EULs. As a superior strategy, the two-dimensional correlation spectroscopy (2DCOS) was used to directly combined with residual neural network (ResNet) based on Fourier transform near-infrared spectroscopy. RESULTS: (1) Each category samples from different regions could be clustered together better than different drying methods through exploratory analysis and hierarchical clustering analysis; (2) A total of 3204 2DCOS images were obtained, synchronous 2DCOS was more suitable for the identification and analysis of EULs compared with asynchronous 2DCOS and integrated 2DCOS; (3) The superior ResNet model about synchronous 2DCOS used to identify different drying method and regions of EULs than the partial least squares discriminant model that the accuracy of train set, test set, and external verification was 100%; (4) The Xinjiang samples was significant differences than others with correlation analysis of 19 climate data and different regions. CONCLUSIONS: This study verifies the superiority of the ResNet model to identify through this example, which provides a practical reference for related research on other medicinal plants or fungus.

The Traditional Usages, Chemical Components and Pharmacological Activities of Wolfiporia cocos: A Review.

As an endemic species,Wolfiporia cocos (F.A. Wolf) Ryvarden & Gilb. is widely distributed, such as in China, Korea, Japan, and North America, which have had a dual-purpose resource for medicines and food for over 2000 years. The applications of W. cocos were used to treat diseases including edema, insomnia, spleen deficiency, and vomiting. What's more, there have been wide uses of such edible fungi as a function food or dietary supplement recently. Up until now, 166 kinds of chemical components have been isolated and identified from W. cocos including triterpenes, polysaccharides, sterols, diterpenes, and others. Modern pharmacological studies showed that the components hold a wide range of pharmacological activities both in vitro and in vivo, such as antitumor, anti-inflammatory, antibacterial, anti-oxidant, and antidepressant activities. In addition, present results showed that the mechanisms of pharmacological activities were closely related to chemical structures, molecular signaling paths and the expression of relate proteins for polysaccharides and triterpenes. For further in-depth studies on this fungus based on the recent research status, this review provided some perspectives and systematic summaries of W. cocos in traditional uses, chemical components, pharmacological activities, separation and analysis technologies, and structure-activity relationships.

Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species.

Boletes are favored by consumers because of their unique flavor, rich nutrition and delicious taste. However, the different nutritional values of each species lead to obvious price differences, so shoddy products appear on the market, which affects food safety. The aim of this study was to find a rapid and effective method for boletes species identification. In this paper, 1,707 samples of eight boletes species were selected as the research objects. The original Mid-Infrared (MIR) spectroscopy data were adopted for support vector machine (SVM) modeling. The 11,949 spectral images belong to seven data sets such as two-dimensional correlation spectroscopy (2DCOS) and three-dimensional correlation spectroscopy (3DCOS) were used to carry out Alexnet and Residual network (Resnet) modeling, thus we established 15 models for the identification of boletes species. The results show that the SVM method needs to process complex feature data, the time cost is more than 11 times of other models, and the accuracy is not high enough, so it is not recommended to be used in data processing with large sample size. From the perspective of datasets, synchronous 2DCOS and synchronous 3DCOS have the best modeling results, while one-dimensional (1D) MIR Spectrum dataset has the worst modeling results. After comprehensive analysis, the modeling effect of Resnet on the synchronous 2DCOS dataset is the best. Moreover, we use large-screen visualization technology to visually display the sample information of this research and obtain their distribution rules in terms of species and geographical location. This research shows that deep learning combined with 2DCOS and 3DCOS spectral images can effectively and accurately identify boletes species, which provides a reference for the identification of other fields, such as food and Chinese herbal medicine.

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.

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy.

Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750-400, 1,450-1,000, and 1,150-1,000 cm-1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750-400-cm-1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150-1,000-cm-1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.

Mechanisms of Traditional Chinese Medicine Bushenantai granules in promoting angiogenesis at the maternal-fetal interface of recurrent spontaneous abortion mice.

OBJECTIVE: To assess the effects of Bushenantai (BSAT) granule() on angiogenesis-related factors [E2, P, and vascular endothelial growth factor (VEGF)] at the maternal-fetal interface of recurrent spontaneous abortion (RSA) mice, and to evaluate the role of BSAT in promoting angiogenesis at the maternal-fetal interface by influencing the expression of sex hormones, and VEGF. METHODS: A mouse model with normal pregnancy and another with Clark's classic RSA were established. The RSA mice were randomly assigned to six groups: normal, model, progesterone, high-doseBSAT granule (BSAT-H), medium-dose-BSAT granule (BSAT-M), and low-dose-BSAT granule (BSAT-L) (n = 10 for each group). The embryo loss rate and the histopathological changes in the decidual tissues were measured. Serum levels of estrogen (E2), progesterone (P), and VEGF were detected by enzyme-linked immunosorbent assay. The mRNA and protein expressions of estradiol receptor (ER), progesterone receptor (PR), VEGF, and vascular endothelial growth factor receptor 2 (VEGFR2) in the decidual tissues were identified by immunohistochemistry, Western blotting, and quantitative reverse transcription polymerase chain reaction. RESULTS: The embryo loss rate in all groups that received BSAT treatment was reduced, while the number of blood vessels at decidual tissues was increased. The serum levels of E2, P and VEGF were elevated, and the mRNA and protein expressions of ER, PR, VEGF, and VEGFR2 in the decidual tissues were enhanced. CONCLUSION: BSAT can improve angiogenesis at the maternal-fetal interface and reduce the embryo loss rate, which may be associated with its ability to increase the serum levels of estrogen, progesterone, and VEGF, in addition to up-regulation of mRNA and protein expression of ER, PR, VEGF, and VEGFR2 in the decidual tissue.

The traditional uses, phytochemistry, and pharmacological properties of Paris L. (Liliaceae): A review.

ETHNOPHARMACOLOGICAL RELEVANCE: Paris L. (Liliaceae) consisted of 33 species, of which the study focused on Paris polyphylla Smith, P. polyphylla var. chinensis (Franch.) Hara, and P. polyphylla Smith var. yunnanensis (Franch.) Hand. -Mazz. Due of course to the good effects of analgesia and hemostasis, it was traditionally used to treat trauma by folk herbalists. AIM OF THIS REVIEW: This study summarized the traditional uses, distributions, phytochemical components, pharmacological properties, and toxicity evaluation of the genus Paris, and reviewed the economic value of cultivate P. polyphylla. This aim was that of providing a new and comprehensive recognition of these medicinal plants for the further utilization of Paris plants. MATERIALS AND METHODS: The literature about traditional and folk uses of genus Paris was obtained from Duxiu Search, and China National Knowledge Infrastructure (CNKI). The other literature about genus Paris was searched online on Web of Science, PubMed, Google Scholar, Baidu Scholar, Scifinder database, and Springer research. The Scientific Database of China Plant Species (DCP) (http://db.kib.ac.cn/Default.aspx) databases were used to check the scientific names and provide species, varieties, and distribution of genus Paris. The botany studies information of genus Paris was available online from Plant Plus of China (www.iplant.cn). All the molecular structures of chemical compounds displayed in the text were produced by ChemBioDraw Ultra 14.0. RESULTS: The plants of genus Paris, containing about 33 species and 15 varieties, are mainly distributed in Southwest China (Yunnan, Sichuan, and Guizhou provinces). More than 320 chemical components have been isolated from genus Paris since 2020, including steroidal saponins, C-21 steroids, phytosterols, insect hormones, pentacyclic triterpenes, flavonoids, and other compounds. Arrays of pharmacological investigations revealed that compounds and extracts of Paris species possess a wide spectrum of pharmacological effects, such as antitumor, cytotoxic, antimicrobial, antifungal, hemostatic, and anti-inflammatory activities. The studies about toxicity evaluation suggested that Rhizome Paridis had slight liver toxicity. CONCLUSIONS: The dried rhizomes of P. polyphylla, P. polyphylla var. chinensis, and P. polyphylla var. yunnanensis were used to treat wound, bleeding, and stomachache, etc. in folk medicine. Phytochemistry researches showed that different species had pretty similarities especially in terms of chemical constituents. Pharmacological studies witnessed that Rhizome Paridis has various activities. Among these activities, steroidal saponins were the main active ingredients. Furthermore, an important aspect responsible for increasing interest in genus Paris is the use of antifertility-nonhormonal contraceptives by women. Also, the development of TCM (Traditional Chinese medicine) planting industry can improve the income of ethnic minorities and promote economic development.

Study on Quality Response to Environmental Factors and Geographical Traceability of Wild Gentiana rigescens Franch.

Gentiana rigescens Franch. ex Hemsl. is an important medicinal plant in China and the over exploitation of wild resources has affected its quality and clinical efficacy. The accumulation of plant secondary metabolites is not only determined by their genetic characteristics but also influenced by environmental factors. At present, many studies on evaluating the environmental conditions of its planting area are still in the qualitative stage. Therefore, it is necessary to establish a systematic evaluation method to deeply analyze the impact of environmental factors on the quality of medicinal materials and quickly verify the geographical origin. In this study, the contents of five iridoids (loganic acid, swertiamarin, sweroside, gentiopicroside and 6'-O-ß-D-glucopyranosylgentiopicroside) of G. rigescens from 45 different origins (including 441 individuals) of Yunnan Province in China were analyzed by high performance liquid chromatography. Analytical procedures of one-way analysis of variance, correlation analysis, principal components analysis, and hierarchical cluster analysis were employed to interpret the correlation of iridoid content and environmental factors. Fourier transform infrared spectroscopy (FT-IR) combined with two multivariate analysis methods (partial least squares discriminant analysis; support vector machines, SVM) was used to discriminate four major producing areas (158 individuals). The combination of SVM with grid search algorithm achieved an accuracy of 100% in the test set. One-way analysis of variance showed that the contents of five iridoids in root tissues of G. rigescens varied significantly among different origins, which was also verified by the chemometrics analysis results of hierarchical cluster analysis. The results of correlation analysis indicated that the high value of altitude and precipitation were unfavorable for the accumulation of these five iridoids. A correlation between increase of temperature and iridoid accumulation was observed. This study provided a certain theoretical basis for the resource protection and development of G. rigescens based on the correlation analysis between the ecological environment factors and quality.

Traditional uses, chemical diversity and biological activities of Panax L. (Araliaceae): A review.

ETHNOPHARMACOLOGICAL RELEVANCE: Panax L. (Araliaceae) is globally-recognized plant resource suitable for the globalization of traditional Chinese medicines. It has traditionally been used as tonic agents in various ethnomedicinal systems of East Asia, especially in China. It is often used to regulate bodily functions and considered as adjuvant therapy for tumor, resuscitation of traumatic hemorrhagic shock, etc. AIM OF THIS REVIEW: This review systematically summarized the information on distributions, botanical characteristics, traditional uses, chemical components and biological activities of the genus Panax, in order to explore and exploit the therapeutic potential of this plant. MATERIALS AND METHODS: The available information about genus Panax was collected via the online search on Web of Science, Google Scholar, PubMed, Baidu Scholar, Science Direct, China National Knowledge Infrastructure and Springer search. The keywords used include Panax, saponin, secondary metabolites, chemical components, biological activity, pharmacology, traditional medicinal uses, safety and other related words. The Plant List (www.theplantlist.org) and Catalogue of Life: 2019 Annual Checklist (www.catalogueoflife.org/col/) databases were used to provide the scientific names, subspecies classification and distribution information of Panax. RESULTS: Panax is widely assessed concerning its phytochemistry and biological activities. To date, at least 748 chemical compounds from genus Panax were isolated, including saponins, flavonoids, polysaccharides, steroids and phenols. Among them, triterpenoid saponins and polysaccharides were the representative active ingredients of Panax plants, which have been widely investigated. Modern pharmacological studies showed that these compounds exhibited a wide range of biological activities in vitro and in vivo including antineoplastic, anti-inflammatory, hepatorenal protective, neuroprotective, immunoregulatory, cardioprotective and antidiabetic activities. Many studies also confirmed that the mechanisms of organ-protective were closely related to molecular signaling pathways, the expression of related proteins and antioxidant reactions. To sum up, genus Panax has high medicinal and social value, deserving further investigation. CONCLUSIONS: The genus Panax is very promising to be fully utilized in the development of nutraceutical and pharmaceutical products. However, there is a lack of in-depth studies on ethnomedicinal uses of Panax plants. In addition, further studies of single chemical component should be performed based on the diversity of chemical structure, significant biological activities and clinical application. If the bioactive molecules and multicomponent interactions are discovered, it will be of great significance to the clinical application of Panax plants. It is an urgent requirement to carry out detailed phytochemical, pharmacology and clinical research on Panax classical prescriptions for the establishment of modern medication guidelines. Exploring the molecular basis of herbal synergistic actions may provide a new understanding of the complex disease mechanisms and accelerate the process of pharmaceutical development.

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.

[Geographical distribution and bioclimatic characteristics of the wild Gentiana rigescens resources.] / 滇龙胆草野生资源的地理分布与生物气候特征.

Based on the species distribution data provided by the National Specimen Information Infrastructure combined with field survey and MaxEnt model, we simulated and verified the suitable distribution area of Gentiana rigescens. Furthermore, the relationship between the geographical distribution and climatic factors of G. rigescens was analyzed. The results showed that area under curve of training set and test set were more than 0.90, indicating high prediction precision and accurate prediction results. Geographic coordinate range of G. rigescens were 22.2°â”€28.75° N and 98.48°â”€110.59° E. The suitable altitude range was from 1830 to 1959 m. The total area of suitable distribution region was 63.92×104 km2, including the most suitable distribution region (4.33×104 km2) and middle suitable distribution region (21.42×104 km2). Total area of suitable distribution region in Yunnan, Guizhou and Sichuan provinces was about 59.10×104 km2, which accounted for 92.46% of total suitable distribution region in China. Solar radiation (March, June and August), temperature annual range, mean temperature of warmest quarter, mean precipitation of October and November, precipitation of driest quarter were the major climatic factors influencing the geographi-cal distribution of G. rigescens. Results from partial least squares regression analysis showed that solar radiation (March, June and August), temperature annual range, mean precipitation of October and November, precipitation of driest quarter were the main restriction factors in north and east of suitable distribution region. The main restriction factors in south of suitable distribution region were solar radiation of June, mean temperature of warmest quarter, precipitation of October and November. The factors limiting distribution of G. rigescens to basin of Sichuan and Guangxi were solar radiation, temperature annual range, mean temperature of warmest quarter, annual biotempe-rature and annual potential evapotranspiration. In conclusion, suitable distribution region of G. rigescens primarily located in the Yunnan-Guizhou Plateau, where subtropical humid climate was most suitable for its growth.

[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.

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.

Ethnobotany, Phytochemistry and Pharmacological Properties of Eucommia ulmoides: A Review.

Eucommia ulmoides Oliver, a single species of Eucommia genus belonging to the Eucommiaceae family, is an endemic in China and has been used in traditional Chinese medicine for nearly two thousand years. Records from different historical periods highlight E. ulmoides and its officinal botanical parts, usefulness in adaptation to disease and its central role in Chinese medicine theory. There are also historical collection documents for minorities in China. Tearing the leaves, bark and fruit produces strands of latex; a description of E. ulmoides's morphological features is recorded in this paper. This review summarizes 204 natural compounds isolated from this plant, which are divided into seven categories: lignans, iridoids, flavonoids, phenols, steroids, terpenes and others. These components possess wide-ranging pharmacological efficacies, such as antihypertensive, antihyperglycemic, antihyperlipidemia, anti-oxidative, anti-osteoporosis, antitumor, immunomodulatory and neuroprotective activities. This review aims to provide a reference for extensive researches of E. ulmoides crude drugs, especially for quality control, biosynthesis and structure modification of active ingredients and pharmacological mechanism.

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.

Comprehensive quality assessment of Dendrubium officinale using ATR-FTIR spectroscopy combined with random forest and support vector machine regression.

Dendrobium officinale, as a tonic herb, has attracted more and more consumers to consume in daily life. In order to protect the wild resource, the herb has made great progress though cultivation in vitro. However, the quality is fluctuated in Chinese herbal medicine market due to influence such as cultivated areas and harvesting period. Therefore, the herbal samples from different cultivated locations were evaluated with high-performance liquid chromatography with diode array detector (HPLC-DAD) in terms of two chemical components, quercetin and erianin. In addition, two markers in leaf and stem also were used for support vector machine regression (SVMR) prediction. Samples from different harvesting periods were also classified using attenuated total reflectance mid-infrared spectroscopy coupled with random forest model. The results indicated that Pu'er and Menghai in Yunnan Province were suitable places for the herb cultivation and the leaf of the herb was also an exploitable resource just in light of the content of two components. What's more, combination of suitable spectra pretreatment and grid search method efficiently improved the prediction performance of the regression model. The results of random forest model indicated that important variables combination between stem and leaf was an effective tool to predict the harvesting time of the herb with 94.44% accuracy in calibration set and 97.92% classification correct rate in validation set. The results of combination were better than the models using individual stem and leaf spectra. In addition, the suitable harvesting time (December) could be classified efficiently. Our study provides a reference for quality control of raw materials from D. officinale herb.

Traceability of wild Paris polyphylla Smith var. yunnanensis based on data fusion strategy of FT-MIR and UV-Vis combined with SVM and random forest.

Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz (PPY) was a frequently used herbal medicine in pharmaceutical field and different provenances might affect the clinical efficacy. Tracing the geographical origin was an important portion for PPY authentication and quality assessment. Present study was compared low-, mid- and high-level data fusion methodology for geographical traceability of PPY samples (161 batches) combined with multivariate classification methods such as support vector machine gird search (SVM-GS) and random forest (RF) on the basis of Fourier transform mid-infrared (FT-MIR) and ultraviolet-visible (UV-Vis) spectra. Compared with the low- and mid-level data fusion strategy results basing on SVM-GS algorithm, result of high-level data fusion method (calculated by RF) was more satisfying. Result of RF basing on high-level data fusion strategy showed that merely two samples were misclassified and one sample was multiple assigned after voting with fuzzy set theory. Values of specificity, sensitivity, and accuracy rates were exceeded 0.91, 0.99 and 90.91%, for each class respectively, satisfying results of these were shown in training and test sets for high-level data fusion method. This feasible result indicated that the RF algorithm could establish a reliable and good performance model in geographical traceability on the basis of high-level data fusion strategy. Combination of high-level data fusion and RF algorithm could consider as a good choice for establishing a discrimination multivariate model for origins identification of PPY 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.