Discrimination of quality markers of Shujin Huoxue Capsules based on five principles coupled with cobweb model / 中国中药杂志

The quality markers(Q-markers) of Shujin Huoxue Capsules were comprehensively discriminated based on the five principles of transfer and traceability, specificity, compatibility, effectiveness and measurability. The compounds that could be transferred from the original medicinal materials to the preparation were selected with the principle of transfer and traceability. The specific components in the prescription were screened by reviewing literature with the principle of specificity. According to the principle of compatibility, the attributes of compounds were evaluated by the sovereign, minister, assistant and guide combination rules of the original medicinal materials in the prescription. According to the principle of measurability, the measurable components were summarized by reference to the pharmacopoeia and literature combined with the content. The mechanism of Shujin Huoxue Capsules in the treatment of osteoporosis was studied through network pharmacology based on the principle of effectiveness, which was the evaluation index of effectiveness. The chemical components screened out above were regarded as candidate Q-markers, and the cobweb model was plotted to obtain the comprehensive score of Q-markers. Hydroxysafflor yellow A, trachelosid, eleutheroside B, α-cyperone, protocatechuic acid, protocatechualdehyde and 4-methoxy salicylaldehyde were discriminated as the Q-markers of Shujin Huoxue Capsules based on the five principles combined with cobweb model.

Identification of Armeniacae Semen Amarum and Persicae Semen from different origins based on near infrared hyperspectral imaging technology / 中国中药杂志

In order to establish a rapid and non-destructive evaluation method for the identification of Armeniacae Semen Amarum and Persicae Semen from different origins, the spectral information of Armeniacae Semen Amarum and Persicae Semen in the range of 898-1 751 nm was collected based on hyperspectral imaging technology. Armeniacae Semen Amarum and Persicae Semen from different origins were collected as research objects, and a total of 720 Armeniacae Semen Amarum samples and 600 Persicae Semen samples were used for authenticity discrimination. The region of interest(ROI) and the average reflection spectrum in the ROI were obtained, followed by comparing five pre-processing methods. Then, partial least squares discriminant analysis(PLS-DA), support vector machine(SVM), and random forest(RF) method were established for classification models, which were evaluated by the confusion matrix of prediction results and receiver operating characteristic curve(ROC). The results showed that in the three sample sets, the se-cond derivative pre-processing method and PLS-DA were the best model combinations. The classification accuracy of the test set under the 5-fold cross-va-lidation was 93.27%, 96.19%, and 100.0%, respectively. It was consistent with the confusion matrix of the predicted results. The area under the ROC curve obtained the highest values of 0.992 3, 0.999 6, and 1.000, respectively. The study revealed that the near-infrared hyperspectral imaging technology could accurately identify the medicinal materials of Armeniacae Semen Amarum and Persicae Semen from different origins and distinguish the authentication of these two varieties.

Fusion of spectrum and image features to identify Glycyrrhizae Radix et Rhizoma from different origins based on hyperspectral imaging technology / 中国中药杂志

To identify Glycyrrhizae Radix et Rhizoma from different geographical origins, spectrum and image features were extracted from visible and near-infrared(VNIR, 435-1 042 nm) and short-wave infrared(SWIR, 898-1 751 nm) ranges based on hyperspectral imaging technology. The spectral features of Glycyrrhizae Radix et Rhizoma samples were extracted from hyperspectral data and denoised by a variety of pre-processing methods. The classification models were established by using Partial Least Squares Discriminate Analysis(PLS-DA), Support Vector Classification(SVC) and Random Forest(RF). Meanwhile, Gray-Level Co-occurrence matrix(GLCM) was employed to extract textural variables. The spectrum and image data were implemented from three dimensions, including VNIR and SWIR fusion, spectrum and image fusion, and comprehensive data fusion. The results indicated that the spectrum in SWIR range performed better classification accuracy than VNIR range. Compared with other four pre-processing methods, the second derivative method based on Savitzky-Golay(SG) smoothing exhibited the best performance, and the classification accuracy of PLS-DA and SVC models were 93.40% and 94.11%, separately. In addition, the PLS-DA model was superior to SVC and RF models in terms of classification accuracy and model generalization capability, which were evaluated by confusion matrix and receiver operating characteristic curve(ROC). Comprehensive data fusion on SPA bands achieved a classification accuracy of 94.82% with only 28 bands. As a result, this approach not only greatly improved the classification efficiency but also maintained its accuracy. The hyperspectral imaging system, a non-invasively, intuitively and quickly identify technology, could effectively distinguish Glycyrrhizae Radix et Rhizoma samples from different origins.

[Application and prospects of hyperspectral imaging and deep learning in traditional Chinese medicine in context of AI and industry 4.0].

In the 21 st century, the rise of artificial intelligence(AI) marks the arrival of the intelligence era or the era of Industry 4.0. In addition to the rapid development of computer and electronic information science, machine learning, as the core intelligence of AI, provides a new methodology for the modernization of traditional Chinese medicine. The algorithms of machine learning include support vector machine(SVM), extreme learning machine(ELM), convolutional neural network(CNN), and recurrent neural network(RNN). The combination of machine learning algorithms and hyperspectral imaging analysis could be used for the identification of fake and inferior herbs, the origin of herbs and the content determination of bioactive ingredients in herbs, which has largely solved the difficulty in strictly controlling the quality of traditional Chinese medicine. The integration of high spectral imaging(HSI) and deep lear-ning will make the predicted results more reliable and suitable for analysis of great amounts of samples. This paper summarizes the application of hyperspectral imaging technology(HSI) and machine learning algorithms in the field of traditional Chinese medicine in recent years, focuses on the principles of hyperspectral imaging technology, preprocessing methods and deep learning algorithms, and gives the prospects of evolution of hyperspectral imaging technology in the field.

Application and prospects of hyperspectral imaging and deep learning in traditional Chinese medicine in context of AI and industry 4.0 / 中国中药杂志

In the 21 st century, the rise of artificial intelligence(AI) marks the arrival of the intelligence era or the era of Industry 4.0. In addition to the rapid development of computer and electronic information science, machine learning, as the core intelligence of AI, provides a new methodology for the modernization of traditional Chinese medicine. The algorithms of machine learning include support vector machine(SVM), extreme learning machine(ELM), convolutional neural network(CNN), and recurrent neural network(RNN). The combination of machine learning algorithms and hyperspectral imaging analysis could be used for the identification of fake and inferior herbs, the origin of herbs and the content determination of bioactive ingredients in herbs, which has largely solved the difficulty in strictly controlling the quality of traditional Chinese medicine. The integration of high spectral imaging(HSI) and deep lear-ning will make the predicted results more reliable and suitable for analysis of great amounts of samples. This paper summarizes the application of hyperspectral imaging technology(HSI) and machine learning algorithms in the field of traditional Chinese medicine in recent years, focuses on the principles of hyperspectral imaging technology, preprocessing methods and deep learning algorithms, and gives the prospects of evolution of hyperspectral imaging technology in the field.

Quality evaluation of Moutan Cortex Formula Granules / 中国中药杂志

A scientific and perfect quality evaluation system for Moutan Cortex Formula Granules was established,including content determination method,characteristic chromatogram method and mass spectrometry method. The content of paeoniflorin and paeonol in Moutan Cortex Formula Granules was determined by high performance liquid chromatography( HPLC),and the average content was 1. 72% and 1. 42%,respectively. The characteristic chromatogram was used to characterize Moutan Cortex Formula Granules,which contained 7 characteristic peaks,namely gallic acid,p-hydroxybenzoic acid,oxypaeoniflorin,paeoniflorin,tetragalloyl glucose,1,2,3,4,6-penta-O-galloyl-β-D-glucose and paeonol. A total of 40 compounds in Moutan Cortex Formula Granules,including gallic acids,paeoniflorins,paeonols,flavonoids and benzoic acids,were identified by mass spectrometry. In this study,a variety of analytical methods were used to evaluate the quality system of Moutan Cortex Formula Granules,which could play a positive role in improving the level of quality evaluation and process quality control.

Application of fingerprint technology in quality evaluation and process control of traditional Chinese medicine formula granules / 中国中药杂志

The fingerprint technology could reflect the internal chemical characteristics of Chinese herbal medicine or preparation, which has the characteristics of "wholeness" and "fuzziness". It is suitable for evaluating the quality of intermediate and finished products in the production process of traditional Chinese medicine formula granules. In this paper, the applications of high performance liquid chromatography (HPLC), thin layer chromatography (TLC), gas chromatography (GC) and infrared spectrum (IR) fingerprint technology in the quality control of traditional Chinese medicine formula granules were reviewed, and their advantages and disadvantages were analyzed. The aim of this article is to enhance the combined application of various fingerprint technologies in traditional Chinese medicine formula granules. It could provide technical reference for realizing the stability of production process and improving the overall quality of formula granules.

Research progress on active ingredients of traditional Chinese medicines improved insulin resistance based on PPARs targets / 中国中药杂志

Peroxisome proliferator-activated receptors (PPARs) are nuclear transcriptional factors closely related to glucose and lipid metabolism, insulin sensitivity. Activation of PPARs targets treated type 2 diabetes, obesity, hypertension and other metabolic diseases by insulin resistance. Recently, a variety of active ingredients of traditional Chinese medicines (TCMs) have been proved to activate PPARs targets for improving insulin resistance, which has attracted widespread attention at home and abroad. In this paper, we reviewed the pathological mechanisms between insulin resistance and PPARs, and summarized the active ingredients of TCMs improved insulin resistance based on PPARs targets. This paper may provide some theoretical guidance for the development of new drugs and TCMs.