Network pharmacology and fingerprint for the integrated analysis of mechanism, identification and prediction in Panax notoginseng.

INTRODUCTION: Panax notoginseng (Burkill) F. H. Chen ex C. H. Chow, is a well-known herb with multitudinous efficacy. In this study, a series of overall analyses on the action mechanism, component content, origin identification, and content prediction of P. notoginseng are conducted. OBJECTIVES: The purpose was to analyse the mechanism of pharmacological efficacy, differences between contents and groups of P. notoginseng from different origins, and to identify the origin and predict the content. MATERIALS AND METHODS: The P. notoginseng samples from four different origins were used for analysis by the database, network pharmacology (Q-marker) and fingerprint analysis [high-performance liquid chromatography (HPLC), attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and near-infrared (NIR)] combined with data fusion strategy (low- and feature-level). RESULTS: Four saponins were identified as Q-markers, and exerted pharmacological effects on signalling pathways through 24 core targets. The qualitative and quantitative analysis of HPLC showed that there were differences among groups and different origins. Therefore, considering the need to treat diseases, combined with network database and network pharmacology, the suitable producing areas were determined through the mechanism of action and the required saponin content. The low-level data fusion successfully identified the origin and predicted the content of P. notoginseng from different origins. The accuracy rate of each evaluation index of the partial least squares discriminant analysis (PLS-DA) model was 1, and the t-SNE (t-distributed stochastic neighbor embedding) visualisation results were good. The coefficient of determination (R2 ) of the partial least squares regression (PLSR) model ranged from 0.9235-0.9996, and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) range is 0.301-1.519. CONCLUSION: This study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng.

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review.

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.

ResNet and MaxEnt modeling for quality assessment of Wolfiporia cocos based on FT-NIR fingerprints.

As a fungus with both medicinal and edible value, Wolfiporia cocos (F. A. Wolf) Ryvarden & Gilb. has drawn more public attention. Chemical components' content fluctuates in wild and cultivated W. cocos, whereas the accumulation ability of chemical components in different parts is different. In order to perform a quality assessment of W. cocos, we proposed a comprehensive method which was mainly realized by Fourier transform near-infrared (FT-NIR) spectroscopy and ultra-fast liquid chromatography (UFLC). A qualitative analysis means was built a residual convolutional neural network (ResNet) to recognize synchronous two-dimensional correlation spectroscopy (2DCOS) images. It can rapidly identify samples from wild and cultivated W. cocos in different parts. As a quantitative analysis method, UFLC was used to determine the contents of three triterpene acids in 547 samples. The results showed that a simultaneous qualitative and quantitative strategy could accurately evaluate the quality of W. cocos. The accuracy of ResNet models combined synchronous FT-NIR 2DCOS in identifying wild and cultivated W. cocos in different parts was as high as 100%. The contents of three triterpene acids in Poriae Cutis were higher than that in Poria, and the one with wild Poriae Cutis was the highest. In addition, the suitable habitat plays a crucial role in the quality of W. cocos. The maximum entropy (MaxEnt) model is a common method to predict the suitable habitat area for W. cocos under the current climate. Through the results, we found that suitable habitats were mostly situated in Yunnan Province of China, which accounted for approximately 49% of the total suitable habitat area of China. The research results not only pave the way for the rational planting in Yunnan Province of China and resource utilization of W. cocos, but also provide a basis for quality assessment of medicinal fungi.

Practical Qualitative Evaluation and Screening of Potential Biomarkers for Different Parts of Wolfiporia cocos Using Machine Learning and Network Pharmacology.

Wolfiporia cocos is a widely used traditional Chinese medicine and dietary supplement. Artificial intelligence algorithms use different types of data based on the different strategies to complete multiple tasks such as search and discrimination, which has become a trend to be suitable for solving massive data analysis problems faced in network pharmacology research. In this study, we attempted to screen the potential biomarkers in different parts of W. cocos from the perspective of measurability and effectiveness based on fingerprint, machine learning, and network pharmacology. Based on the conclusions drawn from the results, we noted the following: (1) exploratory analysis results showed that differences between different parts were greater than those between different regions, and the partial least squares discriminant analysis and residual network models were excellent to identify Poria and Poriae cutis based on Fourier transform near-infrared spectroscopy spectra; (2) from the perspective of effectiveness, the results of network pharmacology showed that 11 components such as dehydropachymic acid and 16α-hydroxydehydrotrametenolic acid, and so on had high connectivity in the "component-target-pathway" network and were the main active components. (3) From a measurability perspective, through orthogonal partial least squares discriminant analysis and the variable importance projection > 1, it was confirmed that three components, namely, dehydrotrametenolic acid, poricoic acid A, and pachymic acid, were the main potential biomarkers based on high-performance liquid chromatography. (4) The content of the three components in Poria was significantly higher than that in Poriae cutis. (5) The integrated analysis showed that dehydrotrametenolic acid, poricoic acid A, and pachymic acid were the potential biomarkers for Poria and Poriae cutis. Overall, this approach provided a novel strategy to explore potential biomarkers with an explanation for the clinical application and reasonable development and utilization in Poria and Poriae cutis.

Evaluation of Ecological Suitability and Quality Suitability of Panax notoginseng Under Multi-Regionalization Modeling Theory.

Panax notoginseng is an important medicinal plant in China, but there are some limitations in the ecological suitability study, such as incomplete investigation of species distribution, single regionalization modeling, and lack of collaborative evaluation of ecological suitability, and quality suitability. In this study, the maximum entropy model was used to analyze the ecological suitability of P. notoginseng under current and future climates. The multi-source chemical information of samples was collected to evaluate the uniformity between quality and ecology. The results showed that the current suitable habitat was mainly in southwest China. In the future climate scenarios, the high suitable habitat will be severely degraded. Modeling based on different regionalization could predict larger suitable habitat areas. The samples in the high suitable habitat had both quality suitability and ecological suitability, and the accumulation of chemical components had different responses to different environmental factors. Two-dimensional correlation spectroscopy combined with deep learning could achieve rapid identification of samples from different suitable habitats. In conclusion, global warming is not conducive to the distribution and spread of P. notoginseng. The high suitable habitat was conducive to the cultivation of high-quality medicinal materials. Actual regionalization modeling had more guiding significance for the selection of suitable habitats in a small area. The multi-regionalization modeling theory proposed in this study could provide a new perspective for the ecological suitability study of similar medicinal plants. The results provided a reference for the introduction and cultivation, and lay the foundation for the scientific and standardized production of high-quality P. notoginseng.

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.

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.

Study of the suitable climate factors and geographical origins traceability of Panax notoginseng based on correlation analysis and spectral images combined with machine learning.

Introduction: The cultivation and sale of medicinal plants are some of the main ways to meet the increased market demand for plant-based drugs. Panax notoginseng is a widely used Chinese medicinal material. The growth and accumulation of bioactive constituents mainly depend on a satisfactory growing environment. Additionally, the occurrence of market fraud means that care should be taken when purchasing. Methods: In this study, we report the correlation between saponins and climate factors based on high performance liquid chromatography (HPLC), and evaluate the influence of climate factors on the quality of P. notoginseng. In addition, the synchronous two-dimensional correlation spectroscopy (2D-COS) images of near infrared (NIR) data combined with the deep learning model were applied to traceability of geographic origins of P. notoginseng at two different levels (district and town levels). Results: The results indicated that the contents of saponins in P. notoginseng are negatively related to the annual mean temperature and the temperature annual range. A lower annual mean temperature and temperature annual range are favorable for the content accumulation of saponins. Additionally, high annual precipitation and high humidity are conducive to the content accumulation of Notoginsenoside R1 (NG-R1), Ginsenosides Rg1 (G-Rg1), and Ginsenosides Rb1 (G-Rb1), while Ginsenosides Rd (G-Rd), this is not the case. Regarding geographic origins, classifications at two different levels could be successfully distinguished through synchronous 2D-COS images combined with the residual convolutional neural network (ResNet) model. The model accuracy of the training set, test set, and external validation is achieved at 100%, and the cross-entropy loss function curves are lower. This demonstrated the potential feasibility of the proposed method for P. notoginseng geographic origin traceability, even if the distance between sampling points is small. Discussion: The findings of this study could improve the quality of P. notoginseng, provide a reference for cultivating P. notoginseng in the future and alleviate the occurrence of market fraud.

A Novel Multi-Preprocessing Integration Method for the Qualitative and Quantitative Assessment of Wild Medicinal Plants: Gentiana rigescens as an Example.

Until now, the over-exploitation of wild resources has increased growing concern over the quality of wild medicinal plants. This led to the necessity of developing a rapid method for the evaluation of wild medicinal plants. In this study, the content of total secoiridoids (gentiopicroside, swertiamarin, and sweroside) of Gentiana rigescens from 37 different regions in southwest China were analyzed by high performance liquid chromatography (HPLC). Furthermore, Fourier transform infrared (FT-IR) was adopted to trace the geographical origin (331 individuals) and predict the content of total secoiridoids (273 individuals). In the traditional FT-IR analysis, only one scatter correction technique could be selected from a series of preprocessing candidates to decrease the impact of the light correcting effect. Nevertheless, different scatter correction techniques may carry complementary information so that using the single scatter correction technique is sub-optimal. Hence, the emerging ensemble approach to preprocessing fusion, sequential preprocessing through orthogonalization (SPORT), was carried out to fuse the complementary information linked to different preprocessing methods. The results suggested that, compared with the best results obtained on the scatter correction modeling, SPORT increased the accuracy of the test set by 12.8% in qualitative analysis and decreased the RMSEP by 66.7% in quantitative analysis.

Study on the identification and evaluation of growth years for Paris polyphylla var. yunnanensis using deep learning combined with 2DCOS.

Paris polyphylla var. yunnanensis, as perennial plants, its quality is closely related to growth period. Different harvest years determine the dry matter accumulation of its medicinal parts and the dynamic accumulation of active ingredients, as well as its economic value and medicinal value. Therefore, it is necessary to establish a systematic evaluation method for the identification and evaluation of P. polyphylla var. yunnanensis with different growth years. Deep learning has a powerful ability in recognition. This study extends it to the identification analysis of medicinal plants from the perspective of spectrum. For the first time, two-dimensional correlation spectroscopy (2DCOS) based on the attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR) combined with residual neural network (Resnet) was used to identify growth years. 525 samples were collected, 4725 2DCOS images were drawn, and the dry matter accumulation in rhizomes of different growth years and different sampling sites were briefly analyzed. The results show that the eight-year-old P. polyphylla var. yunnanensis in Dali has higher economic value and medicinal value. The synchronous 2DCOS models based on ATR-FTIR can realize the identification of growth years with accuracy of 100%. Synchronous 2DCOS are more suitable for the identification of medicinal plants with complex systems. 2DCOS images with different colors and second derivative processing cannot optimize the modeling results. In summary, the method we established is innovative and feasible. It not only solved the identification of growth years, expanded the application field of deep learning, but could also be extended to further research on other medicinal plants.

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.

Geographical traceability and multielement analysis of edible and medicinal fungi: Taking Wolfiporia cocos (F.A. Wolf) Ryvarden and Gilb. as an example.

Different geographical environment has a certain influence on the accumulation of fungi elements and chemical components. However, our knowledge is limited to elucidate the fungi elements in response to heterogeneous environmental and the quality differences among different habitats. Here, multielement analysis, FTIR spectrum, and feature-level fusion technique combined with chemometrics were used to study Wolfiporia cocos from different geographical areas, different sampling sites and different altitude sources. From the results, (1) there is significant difference in element content of samples from different sampling sites and no positive correlation with geographical ranges. (2) There is a correlation between elevation and elements, and relatively low elevation (<1,800 m) is conducive to the enrichment of elements. (3) From the perspective of elements, the W. cocos in Yuxi have relatively better quality. (4) FTIR and feature-level models can well realize origin identification. The SVM models are better than the PLS-DA models, and the feature-level model is better than the single FTIR models. In summary, this study demonstrated that the developed method was reliable and could realize the genuineness evaluation and origin identification of W. cocos. The results have implications for the establishment of the technology system of geographical traceability and the development of high-quality geographical indication products of W. cocos.

Application of Identification and Evaluation Techniques for Ethnobotanical Medicinal Plant of Genus Panax: A Review.

Genus Panax, as worldwide medicinal plants, has a medical history for thousands of years. Most of the entire genus are traditional ethnobotanical medicine in China, Myanmar, Thailand, Vietnam and Laos, which have given rise to international attention and use. This paper reviewed more than 210 articles and related books on the research of Panax medicinal plants and their Chinese patent medicines published in the last 30 years. The purpose was to review and summarize the species classification, geographical distribution, and ethnic minorities medicinal records of the genus Panax, and further to review the analytical tools and data analysis methods for the authentication and quality assessment of Panax medicinal materials and Chinese patent medicines. Five main technologies applied in the identification and evaluation of Panax have been introduced and summarized. Chromatography was the most widely used one. Further research and development of molecular identification technology had the potential to become a mainstream identification technology. In addition, some novel, controversial, and worthy methods including electronic noses, electronic eyes, and DNA barcoding were also introduced. At the same time, more than 80% of the researches were carried out by a combination of chemometric pattern-recognition technologies and multi-analysis technologies. All the technologies and methods applied can provide strong support and guarantee for the identification and evaluation of genus Panax, and also conduce to excellent reference value for the development and in-depth research of new technologies in Panax.

Deep learning for species identification of bolete mushrooms with two-dimensional correlation spectral (2DCOS) images.

Bolete is well-known and widely consumed mushroom in the world. However, its medicinal properties and nutritional are completely different from one species to another. Therefore, the consumers need a fast and effective detection method to discriminate their species. A new method using directly digital images of two-dimensional correlation spectroscopy (2DCOS) for the species discrimination with deep learning is proposed in this paper. In our study, a total of 2054 fruiting bodies of 21 wild-grown bolete species were collected in 52 regions from 2011 to 2014. Firstly, we intercepted 1750-400 cm-1 fingerprint regions of each species from their mid-infrared (MIR) spectra, and converted them to 2DCOS spectra with matlab2017b. At the same time, we developed a specific method for the calculation of the 2DCOS spectra. Secondly, we established a deep residual convolutional neural network (Resnet) with 1848 (90%) 2DCOS spectral images. Therein, the discrimination of the bolete species using directly 2DCOS spectral images instead of data matric from the spectra was first to be reported. The results displayed that the respective identification accuracy of these samples was 100% in the training set and 99.76% in the test set. Then, 203 samples were accurately discriminated in 206 (10%) samples of external validation set. Thirdly, we employed t-SNE method to visualize and evaluate the spectral dataset. The result indicated that most samples can be clustered according to different species. Finally, a smartphone applications (APP) was developed based on the established 2DCOS spectral images strategy, which can make the discrimination of bolete mushrooms more easily in practice. In conclusion, deep learning method by using directly 2DCOS spectral image was considered to be an innovative and feasible way for the species discrimination of bolete mushrooms. Moreover, this method may be generalized to other edible mushrooms, food, herb and agricultural products in the further research.

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.

Investigation of a Medical Plant for Hepatic Diseases with Secoiridoids Using HPLC and FT-IR Spectroscopy for a Case of Gentiana rigescens.

Secoiridoids could be used as a potential new drug for the treatment of hepatic disease. The content of secoiridoids of G. rigescens varied in different geographical origins and parts. In this study, a total of 783 samples collected from different parts of G. rigescens in Yunnan, Sichuan, and Guizhou Provinces. The content of secoiridoids including gentiopicroside, swertiamarin, and sweroside were determined by using HPLC and analyzed by one-way analysis of variance. Two selected variables including direct selected and variable importance in projection combined with partial least squares regression have been used to establish a method for the determination of secoiridoids using FT-IR spectroscopy. In addition, different pretreatments including multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative and second derivative (SD), and orthogonal signal correction (OSC) were compared. The results indicated that the sample (root, stem, and leaf) with total secoiridoids, gentiopicroside, swertiamarin, and sweroside from west Yunnan had higher content than samples from the other regions. The sample from Baoshan had more total secoiridoids than other samples for the whole medicinal plant. The best performance using FT-IR for the total secoiridoid was with the direct selected variable method involving pretreatment of MSC+OSC+SD in the root and stem, while in leaf, of the best method involved using original data with MSC+OSC+SD. This method could be used to determine the bioactive compounds quickly for herbal medicines.

Comparison and quantitative analysis of wild and cultivated Macrohyporia cocos using attenuated total refection-Fourier transform infrared spectroscopy combined with ultra-fast liquid chromatography.

Dried sclerotium of Macrohyporia cocos is a well-known and widely-consumed traditional Chinese medicine and is also used as dietary supplement. According to the differential treatment between cultivation and wild habitats in the market, the comparison and quantitative analysis of wild and cultivated M. cocos were performed using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy and ultra-fast liquid chromatography combined with partial least squares discriminant analysis and partial least squares regression (PLSR). 636 samples were used for the spectral scan and chromatographic analysis. Results indicated that contents of dehydrotumulosic acid, poricoic acid A and dehydrotrametenolic acid in cultivated samples were significantly different from wild samples in two medicinal parts. Differences of dehydropachymic acid and pachymic acid just existed in inner part samples (P < 0.05). Wild M. cocos samples could be discriminated with cultivated samples with >95.14% efficiency using spectral data. ATR-FTIR combined with PLSR provided satisfactory performance for content predictions of poricoic acid A and dehydrotrametenolic acid. This study demonstrated that growth patterns could affect the quality of inner part and epidermis of M. cocos, and ATR-FTIR was a promising technique for the identification of wild and cultivated M. cocos and the rapid determination of triterpene acids contents.

Origin identification of Panax notoginseng by multi-sensor information fusion strategy of infrared spectra combined with random forest.

Traditional Chinese medicine Panax notoginseng is a valuable geo-authentic herbal material. The difference of growth environment in different producing areas has significant influence on the quality of traditional Chinese medicine, and origin identification is an important part of the quality assessment of P. notoginseng. In this study, Fourier transform mid-infrared (FT-MIR) and near infrared (NIR) sensor technologies combined with single spectra analysis and multi-sensor information fusion strategy (low-, mid- and high-level) for the origin identification of 210 P. notoginseng samples from five cities in Yunnan Province, China. FT-MIR spectra were considered to play a greater role in data analysis than NIR spectra. Random forest (RF) was used to establish classification models. The result of the random forest Boruta (RF-Bo) model and the random forest variable selection (RF-Vs) model based on high-level multi-sensor information fusion strategy was satisfactory. In addition, the RF-Bo model based on high-level multi-sensor information fusion strategy was faster and simpler in data analysis and the accuracy was 95.6%.

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.

Original plant traceability of Dendrobium species using multi-spectroscopy fusion and mathematical models.

Dendrobium is the largest genus of orchids most of which have excellent medicinal properties. Fresh stems of some species have been consumed in daily life by Asians for thousands of years. However, there are differences in flavour and clinical efficacy among different species. Therefore, it is necessary for a detector to establish an effective and rapid method controlling botanical origins of these crude materials. In our study, three spectroscopies including mid-infrared (MIR) (transmission and reflection mode) and near-infrared (NIR) spectra were investigated for authentication of 12 Dendrobium species. Generally, two fusion strategies, reflection MIR and NIR spectra, were combined with three mathematical models (random forest, support vector machine with grid search (SVM-GS) and partial least-squares discrimination analysis (PLS-DA)) for discrimination analysis. In conclusion, a low-level fusion strategy comprising two spectra after pretreated by the second derivative and multiplicative scatter correction was recommended for discrimination analysis because of its excellent performance in three models. Compared with MIR spectra, NIR spectra were more responsible for the discrimination according to a bi-plot analysis of PLS-DA. Moreover, SVM-GS and PLS-DA were suitable for accurate discrimination (100% accuracy rates) of calibration and validation sets. The protocol combined with low-level fusion strategy and chemometrics provides a rapid and effective reference for control of botanical origins in crude Dendrobium materials.