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Mendeley readers
Attention Score in Context
| Title |
Probing into the Molecular Requirements for Antioxidant Activity in Plant Phenolic Compounds Utilizing a Combined Strategy of PCA and ANN.
|
|---|---|
| Published in |
Combinatorial Chemistry & High Throughput Screening, January 2017
|
| DOI | 10.2174/1386207320666170102123146 |
| Pubmed ID | |
| Authors |
Snezana Agatonovic-Kustrin, David W Morton, Petar Ristivojevic |
| Abstract |
This study investigates molecular structural requirements that are responsible for the antioxidant activity in phenolic compounds. Antioxidant activity was determined with a 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical assay. Principal component analysis (PCA) was used to classify phenolic antioxidants according to the key molecular features that contribute to their antioxidant activity. Artificial neutral networks (ANNs) was used to develop a predictive QSAR model. Both models agreed that structural characteristics of phenolic compounds responsible for the antioxidant activity include: (1) number and position of alcohol groups on the aromatic ring; (2) molecular size; (3) flexibility/bulkiness; and (4) water solubility. PCA has classified data into phenolic acids and flavonoids, suggesting two distinct mechanisms of action. ANN has confirmed different mechanisms of action for flavonoids and polyphenolic acids, i.e. breaking of free radical chain reactions by donation of a hydrogen atom to neutralise a free radical and the chelating ability of polyphenolic acids. |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 1 | 8% |
| Student > Bachelor | 1 | 8% |
| Professor | 1 | 8% |
| Student > Ph. D. Student | 1 | 8% |
| Researcher | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 6 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 2 | 17% |
| Computer Science | 1 | 8% |
| Chemical Engineering | 1 | 8% |
| Unknown | 8 | 67% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 April 2017.
All research outputs
#2,330,742
of 25,382,440 outputs
Outputs from Combinatorial Chemistry & High Throughput Screening
#5
of 871 outputs
Outputs of similar age
#45,661
of 421,675 outputs
Outputs of similar age from Combinatorial Chemistry & High Throughput Screening
#1
of 15 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 871 research outputs from this source. They receive a mean Attention Score of 2.8. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,675 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.