Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode
This work demonstrates the analysis of epinephrine (EP) and uric acid (UA) in a single drop (the volume of the test solution was only 50 µL) using a screen-printed carbon electrode (SPCE) sensor and square-wave voltammetry (SWV). The limit of detection, limit of quantification, linearity, accuracy,...
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doaj-fb381acbb93a45ee9c9989c7b3614f762021-08-26T13:34:18ZengMDPI AGBiosensors2079-63742021-08-011128528510.3390/bios11080285Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon ElectrodeDavid Majer0Matjaž Finšgar1Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, SloveniaFaculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, SloveniaThis work demonstrates the analysis of epinephrine (EP) and uric acid (UA) in a single drop (the volume of the test solution was only 50 µL) using a screen-printed carbon electrode (SPCE) sensor and square-wave voltammetry (SWV). The limit of detection, limit of quantification, linearity, accuracy, precision, and robustness were validated. The normality of the experimental data was tested and confirmed for both methods. Heteroscedasticity was checked by residual analysis followed by a statistical <i>F</i>-test. The latter was confirmed for both analytes. The low relative standard deviations (RSD) at all calibration points and repetitive slopes justified the use of a calibration curve; therefore, the standard addition methodology was avoided (the latter is common in electroanalysis, but time-consuming). Since the conditions for using an ordinary least squares (OLS) regression were not met, weighted linear regression (WLR) was used to improve the accuracy of the analytical results at low concentrations of the analytes. In this manner, the best weighted model was determined and used for the quantification. A comparison was made between the OLS and WLR methods to show the necessity of using the WLR method for EP and UA analysis. The newly developed and validated methods were also shown to be effective in the analysis of real samples. The content of EP in an EP auto-injector and UA in human urine was tested by employing the best weighted model. For EP and UA, the accuracy in terms of the average recovery value was 101.01% and 94.35%, and precision in terms of RSD was 5.65% and 2.75%, respectively. A new analytical methodology is presented that uses a low volume (a single drop), and it offers the advantage of electroanalysis for on-site analysis, where conventional chromatographic techniques cannot be easily employed. Furthermore, the developed technique has additional advantages in terms of speed, cost, and miniaturization.https://www.mdpi.com/2079-6374/11/8/285epinephrineuric acidsingle-drop analysisscreen-printed electrodeweighted linear regression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David Majer Matjaž Finšgar |
spellingShingle |
David Majer Matjaž Finšgar Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode Biosensors epinephrine uric acid single-drop analysis screen-printed electrode weighted linear regression |
author_facet |
David Majer Matjaž Finšgar |
author_sort |
David Majer |
title |
Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode |
title_short |
Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode |
title_full |
Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode |
title_fullStr |
Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode |
title_full_unstemmed |
Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode |
title_sort |
single-drop analysis of epinephrine and uric acid on a screen-printed carbon electrode |
publisher |
MDPI AG |
series |
Biosensors |
issn |
2079-6374 |
publishDate |
2021-08-01 |
description |
This work demonstrates the analysis of epinephrine (EP) and uric acid (UA) in a single drop (the volume of the test solution was only 50 µL) using a screen-printed carbon electrode (SPCE) sensor and square-wave voltammetry (SWV). The limit of detection, limit of quantification, linearity, accuracy, precision, and robustness were validated. The normality of the experimental data was tested and confirmed for both methods. Heteroscedasticity was checked by residual analysis followed by a statistical <i>F</i>-test. The latter was confirmed for both analytes. The low relative standard deviations (RSD) at all calibration points and repetitive slopes justified the use of a calibration curve; therefore, the standard addition methodology was avoided (the latter is common in electroanalysis, but time-consuming). Since the conditions for using an ordinary least squares (OLS) regression were not met, weighted linear regression (WLR) was used to improve the accuracy of the analytical results at low concentrations of the analytes. In this manner, the best weighted model was determined and used for the quantification. A comparison was made between the OLS and WLR methods to show the necessity of using the WLR method for EP and UA analysis. The newly developed and validated methods were also shown to be effective in the analysis of real samples. The content of EP in an EP auto-injector and UA in human urine was tested by employing the best weighted model. For EP and UA, the accuracy in terms of the average recovery value was 101.01% and 94.35%, and precision in terms of RSD was 5.65% and 2.75%, respectively. A new analytical methodology is presented that uses a low volume (a single drop), and it offers the advantage of electroanalysis for on-site analysis, where conventional chromatographic techniques cannot be easily employed. Furthermore, the developed technique has additional advantages in terms of speed, cost, and miniaturization. |
topic |
epinephrine uric acid single-drop analysis screen-printed electrode weighted linear regression |
url |
https://www.mdpi.com/2079-6374/11/8/285 |
work_keys_str_mv |
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