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HPLC-CUPRAC post-column derivatization method for the determination of antioxidants: a performance comparison between porous silica and core-shell column packing

Overview of attention for article published in Journal of Analytical Science and Technology , January 2018
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Title
HPLC-CUPRAC post-column derivatization method for the determination of antioxidants: a performance comparison between porous silica and core-shell column packing
Published in
Journal of Analytical Science and Technology , January 2018
DOI 10.1186/s40543-018-0137-1
Pubmed ID
Authors

Syed A. Haque, Socrates Jose P. Cañete

Abstract

An HPLC method employing a post-column derivatization strategy using the cupric reducing antioxidant capacity reagent (CUPRAC reagent) for the determining antioxidants in plant-based materials leverages the separation capability of regular HPLC approaches while allowing for detection specificity for antioxidants. Three different column types, namely core-shell and porous silica including two chemically different core-shell materials (namely phenyl-hexyl and C18), were evaluated to assess potential improvements that could be attained by changing from a porous silica matrix to a core-shell matrix. Tea extracts were used as sample matrices for the evaluation specifically looking at catechin and epigallocatechin gallate (EGCG). Both the C18 and phenyl-hexyl core-shell columns showed better performance compared to the C18 porous silica one in terms of separation, peak shape, and retention time. Among the two core-shell materials, the phenyl-hexyl column showed better resolving power compared to the C18 column. The CUPRAC post-column derivatization method can be improved using core-shell columns and suitable for quantifying antioxidants, exemplified by catechin and EGCG, in tea samples.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 20%
Student > Doctoral Student 2 20%
Student > Bachelor 1 10%
Professor 1 10%
Lecturer 1 10%
Other 1 10%
Unknown 2 20%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 2 20%
Biochemistry, Genetics and Molecular Biology 2 20%
Agricultural and Biological Sciences 2 20%
Chemistry 2 20%
Unknown 2 20%

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 21 January 2018.
All research outputs
#11,035,405
of 12,416,622 outputs
Outputs from Journal of Analytical Science and Technology
#20
of 34 outputs
Outputs of similar age
#286,757
of 339,905 outputs
Outputs of similar age from Journal of Analytical Science and Technology
#2
of 2 outputs
Altmetric has tracked 12,416,622 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 34 research outputs from this source. They receive a mean Attention Score of 0.8. This one scored the same or higher as 14 of them.
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