A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition.

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

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.

Machine learning and deep learning based on the small FT-MIR dataset for fine-grained sampling site recognition of boletus tomentipes.

This study proposed the necessity of identifying the sampling sites for Boletus tomentipes (B.tomentipes) in combination with cadmium content and environmental factors. Based on fourier transform mid-infrared spectroscopy (FT-MIR) preprocessing by 1st, 2nd, MSC, SNV and SG, five machine learning (ML) algorithms (NB, DT, KNN, RF, SVM) and three Gradient Boosting Machine (GBM) algorithms (XGBoost, LightGBM, CatBoost) were built. To avoid complex preprocessing, we construct BoletusResnet model, propose the concepts of 3DCOS, 3DCOS projected images, index images in addition to 2DCOS, and combine them with deep learning (DL) for classification for the first time. It shows that GBM has higher accuracy than ML and DL has better accuracy than GBM. The four DL models presented in this paper achieve fine-grained sampling sites recognition based on small samples with 100 % accuracy, and a computer application system was developed on them. Therefore, spectral image processing combined with DL is a rapid and efficient classification method which can be widely used in food identification.

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.

Application of UV-Vis and Infrared Spectroscopy on Wild Edible Bolete Mushrooms Discrimination and Evaluation: A Review.

Nowadays, wild edible bolete mushrooms are more and more attractive among consumers due to their natural health, nutrition, and delicious characteristics. Appropriate analytical techniques together with multivariate statistics analysis are required for the quality control and evaluation of these edible mushrooms. Ultraviolet-visible (UV-Vis) and infrared (IR) technologies have the advantages of time-saving, low-cost, and environmentally friendly, are now prominent among major analytical technologies for quality evaluation of bolete mushrooms. Chemometrics methods have been developed to solve classification and regression issues of bolete mushrooms in combination with spectrum. This paper reviewed the most recent applications of UV-Vis and IR technology coupled with chemometrics in wild edible bolete mushrooms, including the identification of species, origin, and storage duration, fraud detection, and antioxidant properties evaluation, and discussed the limitations and prospects of spectroscopy technologies in the researches of bolete mushrooms, excepting to provide a reference for further research and practical application of wild edible bolete mushrooms.

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.

Rapid identification of the storage duration and species of sliced boletes using near-infrared spectroscopy.

Boletes are recognized as a worldwide delicacy. Adulteration of the expired and low-value sliced boletes is a pressing problem in the supply chain of commercial sliced boletes. This study aimed at developing a rapid method to identify the storage duration and species of sliced boletes, using near-infrared (NIR) spectroscopy. In the study, 1376 fruiting bodies of wild-grown boletes were collected from 2017 to 2020 in Yunnan, containing four common species of edible boletes. A NIR spectroscopy-based strategy was proposed, that is, identify the storage duration of sliced boletes to ensure that they are within the shelf life firstly; then identify the species of sliced boletes within the shelf life to evaluate their economic value. Three supervised methods, partial least squares discriminant analysis (PLS-DA), extreme learning machine (ELM), and two-dimensional correlation spectroscopy (2DCOS) images with residual convolutional neural network (ResNet) model were applied to identify. The results showed that PLS-DA model cannot accurately identify the storage duration and species of sliced boletes, and the ELM model can identify the storage duration of boletes samples, but cannot accurately discriminate different species of samples. And ResNet model established by 2DCOS images showed superiority in classification performance, 100% accuracy was obtained for both the storage duration and species classification. Moreover, compared to traditional methods, the 2DCOS images with ResNet model was free of complicated data preprocessing. The results obtained in the present study indicated a promising way of combining 2DCOS images with ResNet methods, in tandem with NIR for the rapid identification of the storage duration and species of sliced boletes. PRACTICAL APPLICATION: In the boletes supply chain, the method can be considered as a reliable method for testing the authenticity of boletes slices. The current study can also provide a reference for quality control of other edible mushroom.

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.

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.

Effects of Different Pre-drying and Drying Methods on Volatile Compounds in the Pericarp and Kernel of Amomum tsao-ko.

The effects of twelve different pre-drying and drying methods on the chemical composition in the pericarp and kernel of Amomum tsao-ko were studied. The volatile components were isolated from the samples by simultaneous distillation and extraction and analyzed by gas chromatography-mass spectrometry (GC-MS). Sixty and thirty-eight compounds were identified from pericarp and kernel, respectively, and the main constituents were oxygenated monoterpenes. These compounds were not only significantly affected by pre-drying and drying methods but also varied in content due to different tissue locations. The total volatile content of pericarp varied from 0.70 to 1.55%, with the highest obtained by microwave-dried samples (150 W) and the lowest in freeze-dried samples. The total volatile content of the kernel varied from 6.11 to 10.69%, with the highest content obtained during sun drying (SD) and the lowest content in samples treated with boiling water for 2 min. Oxygenated monoterpenes were the highest compounds in pericarp and kernel, which were also the most affected by drying methods. The highest content of oxygenated monoterpenes in the pericarp (0.77%) could be obtained by boiling water treatment for 5 min, and the highest content of oxygenated monoterpenes in the kernel (7.48%) could be obtained by SD. Additionally, the main components such as 1,8-cineole, 2-carene, (Z)-citral, nerolidol, (Z)-2-decenal, (E)-2-dodecenal, citral, (E)-2-octenal, 4-propylbenzaldehyde, and phthalan showed remarkable variations in pre-drying and drying methods.

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.

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.

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.

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.

Mitochondrial stress adaptation promotes resistance to aromatase inhibitor in human breast cancer cells via ROS/calcium up-regulated amphiregulin-estrogen receptor loop signaling.

Many breast cancer patients harbor high estrogen receptor (ER) expression in tumors that can be treated with endocrine therapy, which includes aromatase inhibitors (AI); unfortunately, resistance often occurs. Mitochondrial dysfunction has been thought to contribute to progression and to be related to hormone receptor expression in breast tumors. Mitochondrial alterations in AI-resistant breast cancer have not yet been defined. In this study, we characterized mitochondrial alterations and their roles in AI resistance. MCF-7aro AI-resistant breast cancer cells were shown to have significant changes in mitochondria. Low expressions of mitochondrial genes and proteins could be poor prognostic factors for breast cancer patients. Long-term mitochondrial inhibitor treatments-mediated mitochondrial stress adaptation could induce letrozole resistance. ERα-amphiregulin (AREG) loop signaling was activated and contributed to mitochondrial stress adaptation-mediated letrozole resistance. The up-regulation of AREG-epidermal growth factor receptor (EGFR) crosstalk activated the PI3K/Akt/mTOR and ERK pathways and was responsible for ERα activation. Moreover, mitochondrial stress adaptation-increased intracellular levels of reactive oxygen species (ROS) and calcium were shown to induce AREG expression and secretion. In conclusion, our results support the claim that mitochondrial stress adaptation contributes to AI resistance via ROS/calcium-mediated AREG-ERα loop signaling and provide possible treatment targets for overcoming AI resistance.

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.

A fast and effective way for authentication of Dendrobium species: 2DCOS combined with ResNet based on feature bands extracted by spectrum standard deviation.

Dendrobium Sw., as a traditional herb and function food with over 1500 years of history, shows a significant effect in improving immunity and fatigue resistance. However, due of course the large number of species and the quality fluctuating in different species, a fast and effective discrimination method is in need. Recently, spectroscopic techniques combined with chemometrics have become an effective method for low-cost and fast analysis in food and herb. Nevertheless, chemometrics method which based on one-dimensional spectral dataset still encounter the difficulty that can not effectively extract useful information from the spectra. Different from one-dimensional spectra, the two-dimensional correlation spectroscopy (2DCOS) can reveal more detail information of the spectral dataset. Moreover, the appearance of convolutional neural network makes the application of deep learning in image recognition faster and more accurate. In this study, a novel method 2DCOS combined with residual convolutional neural network (ResNet) was used to discriminate the 20 species of Dendrobium. Five feature bands were selected based on spectrum standard deviation (SDD) method in NIR and MIR spectra. Moreover, the models based on full band, total five feature bands, and their fusion-bands had been compared. The results showed that two feature bands 1800-450 cm-1 and 2400-1900 cm-1 displayed 100% accuracy in both training set and test set. And also, the accurate discrimination of 10% external validation showed that these models have good generalization ability. In conclusion, 2DCOS combined with ResNet could be an effective and accurate method for classify different Dendrobium species.

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.

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.