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Attention Score in Context
| Title |
A simple method for HPLC retention time prediction: linear calibration using two reference substances
|
|---|---|
| Published in |
Chinese Medicine, June 2017
|
| DOI | 10.1186/s13020-017-0137-x |
| Pubmed ID | |
| Authors |
Lei Sun, Hong-yu Jin, Run-tao Tian, Ming-juan Wang, Li-na Liu, Liu-ping Ye, Tian-tian Zuo, Shuang-cheng Ma |
| Abstract |
Analysis of related substances in pharmaceutical chemicals and multi-components in traditional Chinese medicines needs bulk of reference substances to identify the chromatographic peaks accurately. But the reference substances are costly. Thus, the relative retention (RR) method has been widely adopted in pharmacopoeias and literatures for characterizing HPLC behaviors of those reference substances unavailable. The problem is it is difficult to reproduce the RR on different columns due to the error between measured retention time (tR) and predicted tR in some cases. Therefore, it is useful to develop an alternative and simple method for prediction of tR accurately. In the present study, based on the thermodynamic theory of HPLC, a method named linear calibration using two reference substances (LCTRS) was proposed. The method includes three steps, procedure of two points prediction, procedure of validation by multiple points regression and sequential matching. The tR of compounds on a HPLC column can be calculated by standard retention time and linear relationship. The method was validated in two medicines on 30 columns. It was demonstrated that, LCTRS method is simple, but more accurate and more robust on different HPLC columns than RR method. Hence quality standards using LCTRS method are easy to reproduce in different laboratories with lower cost of reference substances. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 25% |
| Germany | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 42 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 7 | 17% |
| Student > Master | 4 | 10% |
| Student > Ph. D. Student | 3 | 7% |
| Student > Postgraduate | 2 | 5% |
| Lecturer | 1 | 2% |
| Other | 4 | 10% |
| Unknown | 21 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 8 | 19% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 10% |
| Biochemistry, Genetics and Molecular Biology | 2 | 5% |
| Environmental Science | 1 | 2% |
| Unspecified | 1 | 2% |
| Other | 4 | 10% |
| Unknown | 22 | 52% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 25 June 2017.
All research outputs
#15,989,045
of 25,382,440 outputs
Outputs from Chinese Medicine
#254
of 660 outputs
Outputs of similar age
#186,521
of 329,774 outputs
Outputs of similar age from Chinese Medicine
#2
of 6 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 660 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 61% 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 329,774 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.