| 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. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 2 | 13% |
| Student > Bachelor | 2 | 13% |
| Lecturer | 1 | 6% |
| Professor | 1 | 6% |
| Student > Ph. D. Student | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 8 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 13% |
| Chemistry | 2 | 13% |
| Agricultural and Biological Sciences | 2 | 13% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Unknown | 9 | 56% |
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.
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#22,876,107
of 25,508,813 outputs
Outputs from Journal of Analytical Science and Technology
#59
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#392,194
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Outputs of similar age from Journal of Analytical Science and Technology
#3
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