| Title |
Use of chemometrics to optimize a glucose assay on a paper microfluidic platform
|
|---|---|
| Published in |
Analytical & Bioanalytical Chemistry, February 2017
|
| DOI | 10.1007/s00216-017-0214-0 |
| Pubmed ID | |
| Authors |
Ani Avoundjian, Mehdi Jalali-Heravi, Frank A. Gomez |
| Abstract |
We describe the use of a chemometrics-based computational platform to optimize a glucose assay on a microfluidic paper-based analytical device (μPAD). Glucose is colorimetrically detected in the presence of glucose oxidase (GOx), horseradish peroxidase (HRP), and potassium iodide (KI). Using a Y-shaped paper microfluidic chip, the concentration of glucose, volume of reagents, and the length and width of the microfluidic channel were examined. The responses of the microfluidic chips were analyzed at the halfway point of the channel length. Variables affecting the response were screened by using a 2(4) factorial design, and among them, volume and concentration of the glucose were optimized by applying a rotatable central composite design (CCD). The optimum and experimental responses are 151.58 and 149.80, respectively, with an absolute error of 1.2%. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 27% |
| Student > Bachelor | 4 | 12% |
| Professor > Associate Professor | 4 | 12% |
| Professor | 3 | 9% |
| Student > Doctoral Student | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 7 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 7 | 21% |
| Engineering | 6 | 18% |
| Chemical Engineering | 3 | 9% |
| Biochemistry, Genetics and Molecular Biology | 3 | 9% |
| Agricultural and Biological Sciences | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 10 | 30% |
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 January 2018.
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#20,660,571
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Outputs from Analytical & Bioanalytical Chemistry
#6,602
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Outputs of similar age
#322,774
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Outputs of similar age from Analytical & Bioanalytical Chemistry
#69
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