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Online near-infrared analysis coupled with MWPLS and SiPLS models for the multi-ingredient and multi-phase extraction of licorice (Gancao)

Overview of attention for article published in Chinese Medicine, December 2015
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Title
Online near-infrared analysis coupled with MWPLS and SiPLS models for the multi-ingredient and multi-phase extraction of licorice (Gancao)
Published in
Chinese Medicine, December 2015
DOI 10.1186/s13020-015-0069-2
Pubmed ID
Authors

Yang Li, Mingye Guo, Xinyuan Shi, Zhisheng Wu, Jianyu Li, Qun Ma, Yanjiang Qiao

Abstract

This study aims to analyze the active pharmaceutical ingredients (APIs) of licorice (Radix Glycyrrhizae; gancao), including glycyrrhizic acid, liquiritin, isoliquiritin and total flavonoids, in multi-ingredient and multi-phase extraction by online near-infrared technology with fiber optic probes and chemometric analysis. High-performance liquid chromatography and ultraviolet spectrophotometry determined the APIs content in different extraction phases by online near-infrared analysis, which included sample set selection by the Kennard-Stone algorithm, optimization of spectral pretreatment methods (i.e., orthogonal signal correction and wavelet denoising spectral correction), and model calibration by the partial least-squares algorithm, moving-window partial least-squares algorithm and synergy interval partial least-squares (SiPLS) algorithm. The relative errors and F values were used to assess the models in different extraction phases. The root-mean-square error of correction, root-mean-square error of cross-validation and root-mean-square error of prediction of APIs in the SiPLS model was less than 0.07. The F values of glycyrrhizic acid, liquiritin, isoliquiritin and total flavonoids were 10,765, 32,431, 649 and 6080, respectively, which were larger than 6.90 (P < 0.01). The study demonstrated the feasibility of online NIR analysis in the multi-ingredient and multi-phase extraction of APIs from licorice.

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Geographical breakdown

Country Count As %
United States 1 5%
Unknown 19 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 10%
Other 2 10%
Student > Master 2 10%
Student > Bachelor 1 5%
Professor 1 5%
Other 4 20%
Unknown 8 40%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 10%
Chemistry 2 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Sports and Recreations 1 5%
Immunology and Microbiology 1 5%
Other 2 10%
Unknown 11 55%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 January 2016.
All research outputs
#20,656,161
of 25,373,627 outputs
Outputs from Chinese Medicine
#424
of 660 outputs
Outputs of similar age
#290,686
of 394,029 outputs
Outputs of similar age from Chinese Medicine
#8
of 10 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
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