Natural Products from Smilax china

Natural Products Isolated from Smilax china

BioCrick provides high-purity natural products and bioactive compounds isolated and purified from natural sources for scientific research.

  • Natural product compounds selected from diverse chemical and biological sources.
  • Broad structural diversity and coverage of biological activities.
  • Product activity information can be supported by published literature, patents and research reports.
  • Natural products can be selected according to source, target, activity and disease research interests.
  • Compounds should be stored according to the product specifications after receipt.

Natural Products from Smilax china

9 natural product s associated with Smilax china

Natural products and bioactive compounds from Smilax china
Catalog No. Product Name CAS Number COA
BCN5204 Astilbin
Astilbin chemical structure
29838-67-3 COA
BCN5549 Astragalin
Astragalin chemical structure
480-10-4 COA
BCN6273 Dioscin
Dioscin chemical structure
19057-60-4 COA
BCN5772 Engeletin
Engeletin chemical structure
572-31-6 COA
BCN5719 Isoastilbin
Isoastilbin chemical structure
54081-48-0 COA
BCN5201 Oxyresveratrol
Oxyresveratrol chemical structure
29700-22-9 COA
BCN5824 Piceatannol
Piceatannol chemical structure
10083-24-6 COA
BCN6274 Protodioscin
Protodioscin chemical structure
55056-80-9 COA
BCN5607 Resveratrol
Resveratrol chemical structure
501-36-0 COA

References

Distinguishing Smilax glabra and Smilax china rhizomes by flow-injection mass spectrometry combined with principal component analysis.[Pubmed: 29453916]


Flow-injection mass spectrometry (FIMS) coupled with a chemometric method is proposed in this study to profile and distinguish between rhizomes of Smilax glabra (S. glabra) and Smilax china (S. china). The proposed method employed an electrospray-time-of-flight MS. The MS fingerprints were analyzed using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) with the aid of SIMCA software. Findings showed that the two kinds of samples perfectly fell into their own classes. Further predictive study showed desirable predictability and the tested samples were successfully and reliably identified. The study demonstrated that the proposed method could serve as a powerful tool for distinguishing between S. glabra and S. china.