The aim of this study was to ascertain the anti-arthritic active fraction of Capparis spinosa L. (Capparidaceae) fruits and its chemical constituents. The adjuvant arthritic rat model was developed to evaluate the anti-arthritic eŠects of diŠerent fractions of ethanol extraction from C. spinosa L. The fraction eluted by ethanol-water (50：50, v/v) had the most signiˆcant anti-arthritic activity. The chemical constituents of this fraction were therefore studied; seven known compounds were isolated and identiˆed as: (1) P-hydroxy benzoic acid; (2) 5-(hydroxymethyl) furfural; (3) bis (5-formylfurfuryl) ether; (4) daucosterol; (5) a-D-fructofuranosides methyl; (6) uracil; and (7) stachydrine.

No abstract

The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method. The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value.

No abstract

No abstract