Codonopsis Radix (CR) is an edible food and traditional Chinese herb medicine in China. Various varieties of Codonopsis Radix have different tastes. To make the flavor of processed food stable, two kinds of electronic sensory devices, electronic nose and electronic tongue, were used to establish a discrimination model to identify the botanical origin of each sample. The optimal model built on the 88 batches of samples was selected from the models trained with all combination of two pretreatment methods and three classification methods. A comparison were performed on the models trained on the data collected by electronic nose and electronic tongue. The results showed that the model trained on the fused dataset outperformed the models trained separately on the electronic nose data and electronic tongue data. The two preprocessing approaches could improve the prediction performance of all classification methods. Classification and Regression Tree approach performed better than Partial Least Square Discriminant Analysis and Linear Discriminant Analysis in terms of accuracy. But Classification and Regression Tree tends to assign the samples of minority class to the majority class. Meanwhile, Partial Least Square Discriminant Analysis keeps a good balance between the identification requirements of all the two groups of samples. Taking all the results above, the model built using the Partial Least Square Discriminant Analysis method on the fused data after z-score was used to identify the botanical origin of Codonopsis Radix.

Drug repositioning is gaining attention as a method for developing new drugs due to its low cost, short cycle time, and high success rate. One important approach is to explore new uses for already marketed drugs. In this study, we utilized the strategy of drug repositioning, focusing on the Dan-Lou tablet. We predicted the efficacy of Dan-Lou tablet against non-small cell lung cancer based on gene expression similarity and verified it by in vitro experiments. Next, we performed further analysis and validation using network pharmacology, molecular docking and molecular dynamics. Based on the results, it was concluded that Dan-Lou tablet mainly acted through nine compounds, Quercetin, Luteolin, Scoparone, Isorhamnetin, Eugenol, Genistein, Coumestrol, Hederagenin, Succinic Acid, and mainly targeted CCL2, FEN1, TPI1, RMI2 by six pathways. This discovery not only provides a new idea for the development of Dan-Lou tablet but also provides useful predictive information for clinical treatment. The method we adopted has great development prospects as a way to predict the efficacy of new drugs and their main mechanisms of action, and it has a positive impact on the research and development of new drugs using drug repositioning and the modernization of traditional Chinese medicine.