Introduction Pharmacological studies indicate that Astragalus (AR) has various bioactivities, including anticancer, antiaging, anti‐inflammatory, antiviral, and antioxidant activities. Flavonoids, saponins, amino acids, and polysaccharides are the main active components in AR. However, its complex chemical compositions bring certain difficulties to the analysis of this traditional Chinese medicine (TCM). Therefore, there is an urgent need to establish a method for rapid classification and identification of the chemical constituents in AR. Objective To establish a method for rapid classification and identification of the main components of flavonoids, saponins, and amino acids in AR. Methods The samples were analysed with ultra‐high‐performance liquid chromatography time‐of‐flight quadrupole mass spectrometry (UPLC‐Q‐TOF‐MS) and data post‐processing techniques. Firstly, fragmentation information was obtained in the positive and negative ion modes. Then, to realize the rapid classification and identification of AR components, the characteristic fragmentations (CFs) and neutral losses (NLs) were compared with information described in the literature. Results A total of 45 chemical constituents were successfully screened out, including 22 flavonoids, 13 saponins, and 10 amino acids. Conclusion The established method realised the efficient classification and identification of flavonoids, saponins, and amino acid compounds in AR, which provided a basis for further study on AR.

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Animal bile is an important component of natural medicine and is widely used in clinical treatment. However, it is easy to cause mixed applications during processing, resulting in uneven quality, which seriously affects and harms the interests and health of consumers. Bile acids are the major bioactive constituents of bile and contain a variety of isomeric constituents. Although the components are structurally similar, they exhibit different pharmacological activities. Identifying the characteristics of each animal bile is particularly important for processing and reuse. It is necessary to establish an accurate analysis method to distinguish different types of animal bile. We evaluated the biological activity of key feature markers from various animal bile samples. In this study, a strategy combining metabolomics and machine learning was used to compare the bile of three different animals, and four key markers were screened. Quantitative analysis of the key markers showed that the levels of Glycochenodeoxycholic acid (GCDCA) and Taurodeoxycholic acid (TDCA) were highest in pig bile; Glycocholic acid (GCA) and Cholic acid (CA) were the most abundant in bovine and sheep bile, respectively. In addition, four key feature markers significantly inhibited the production of NO in LPS-stimulated RAW264.7 macrophage cells. These findings will contribute to the targeted development of bile in various animals and provide a basis for its rational application.

In recent years, due to the development of Internet technology in the financial industry, cardless and cashless payments have become increasingly popular. At present, people only need to bind their cards to their cell phones to scan the code for payment. In the meanwhile, as the use of credit cards is vigorously promoted nationwide, younger generations relied more on the credit card debt for consumption. However, although these developments have brought a lot of convenience to people's travel and their life, the virtual nature of the Internet and the "Enjoy First, Pay Later" feature of credit cards have led to a significant increase in potential credit risk in the entire transaction, triggering more and more new credit card fraud incidents. While the overall financial environment in China is relatively healthy, the fact that fraudulent behaviors such as credit card overdrafts, counterfeit cards, and credit card frauds are all personal conduct, making it difficult to detect and prevent these individuals from acting illegally in a timely manner. Although fraud represents a small percentage of the overall transaction size, its bad debt losses to merchants and banks can be significant. In order to make a timely prediction of fraud, there are already many single-model machine learning methods such as decision trees and logistic regression. However, the generalization ability of these models is not good enough in the face of complex user behavior features. In addition, since the probability of fraudulent behavior is very small, resulting in few samples that can be trained, even if a model has a high accuracy rate, there is no guarantee that it can accurately predict fraudulent behavior. Therefore, this paper proposes the Adaboost method based on ensemble learning and uses SMOTE oversampling based on a few classes of fraudulent samples of user features to solve the above existing problems.