Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography-Reference-Cited by-同舟云学术

Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography

Published:2008-05 Issue:9 Volume:31 Page:1550-1563
ISSN:1615-9306
Container-title:Journal of Separation Science
language:en
Short-container-title:J. Sep. Sci.

Author:

Quiming Noel S.,Denola Nerissa L.,Saito Yoshihiro,Jinno Kiyokatsu

Publisher

Wiley

Subject

Filtration and Separation,Analytical Chemistry

Reference48 articles.

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2. 3-oxo-serratene triterpenoids from the stem bark of Picea jezoensis Carr. hondoensis

3. Improved high performance liquid chromatographic determination of ginsenosides inPanax ginseng-based pharmaceuticals using a diol column

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