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A simple method for HPLC retention time prediction: linear calibration using two reference substances

Overview of attention for article published in Chinese Medicine, June 2017
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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4 tweeters

Citations

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3 Dimensions

Readers on

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15 Mendeley
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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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 27%
Student > Postgraduate 2 13%
Student > Master 2 13%
Student > Doctoral Student 1 7%
Lecturer 1 7%
Other 2 13%
Unknown 3 20%
Readers by discipline Count As %
Chemistry 7 47%
Pharmacology, Toxicology and Pharmaceutical Science 2 13%
Unspecified 1 7%
Agricultural and Biological Sciences 1 7%
Chemical Engineering 1 7%
Other 0 0%
Unknown 3 20%

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
#6,492,796
of 11,410,025 outputs
Outputs from Chinese Medicine
#90
of 260 outputs
Outputs of similar age
#125,197
of 263,626 outputs
Outputs of similar age from Chinese Medicine
#4
of 7 outputs
Altmetric has tracked 11,410,025 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 260 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 65% 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 263,626 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.