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Mendeley readers
Attention Score in Context
| Title |
Raman Spectroscopy for Pharmaceutical Quantitative Analysis by Low-Rank Estimation
|
|---|---|
| Published in |
Frontiers in Chemistry, September 2018
|
| DOI | 10.3389/fchem.2018.00400 |
| Pubmed ID | |
| Authors |
Xiangyun Ma, Xueqing Sun, Huijie Wang, Yang Wang, Da Chen, Qifeng Li |
| Abstract |
Raman spectroscopy has been widely used for quantitative analysis in biomedical and pharmaceutical applications. However, the signal-to-noise ratio (SNR) of Raman spectra is always poor due to weak Raman scattering. The noise in Raman spectral dataset will limit the accuracy of quantitative analysis. Because of high correlations in the spectral signatures, Raman spectra have the low-rank property, which can be used as a constraint to improve Raman spectral SNR. In this paper, a simple and feasible Raman spectroscopic analysis method by Low-Rank Estimation (LRE) is proposed. The Frank-Wolfe (FW) algorithm is applied in the LRE method to seek the optimal solution. The proposed method is used for the quantitative analysis of pharmaceutical mixtures. The accuracy and robustness of Partial Least Squares (PLS) and Support Vector Machine (SVM) chemometric models can be improved by the LRE method. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 32 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 19% |
| Student > Bachelor | 5 | 16% |
| Student > Master | 5 | 16% |
| Researcher | 5 | 16% |
| Student > Postgraduate | 2 | 6% |
| Other | 0 | 0% |
| Unknown | 9 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 4 | 13% |
| Engineering | 3 | 9% |
| Chemical Engineering | 2 | 6% |
| Biochemistry, Genetics and Molecular Biology | 2 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 6% |
| Other | 8 | 25% |
| Unknown | 11 | 34% |
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 10 September 2018.
All research outputs
#20,533,292
of 23,103,436 outputs
Outputs from Frontiers in Chemistry
#2,950
of 6,040 outputs
Outputs of similar age
#293,647
of 337,287 outputs
Outputs of similar age from Frontiers in Chemistry
#88
of 203 outputs
Altmetric has tracked 23,103,436 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 6,040 research outputs from this source. They receive a mean Attention Score of 2.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 203 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.