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An Advanced Statistical Approach Using Weighted Linear Regression in Electroanalytical Method Development for

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|December 15, 2020
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Weighted linear regression improved electrochemical analysis of epinephrine, ascorbic acid, and uric acid. This method enhances accuracy, especially at low concentrations, offering a faster, cost-effective alternative to chromatography.

Keywords:
adrenalineascorbic acidepinephrineglassy carbon electrodeheteroscedasticitysquare-wave voltammetryuric acidweighted linear regression

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Area of Science:

  • Analytical Chemistry
  • Electrochemistry
  • Statistical Modeling

Background:

  • Electrochemical methods are crucial for analyzing biomolecules like epinephrine (EP), ascorbic acid (AA), and uric acid (UA).
  • Traditional linear regression can be limited by data heteroscedasticity, affecting analytical accuracy, particularly at low concentrations.
  • Development of robust and accurate electroanalytical methods is essential for real-world sample analysis.

Purpose of the Study:

  • To develop and validate novel electrochemical methods for determining EP, AA, and UA.
  • To investigate the application and benefits of weighted linear regression in electroanalytical method development.
  • To assess the accuracy, precision, and efficiency of the developed methods compared to existing techniques.

Main Methods:

  • Square-wave voltammetry (SWV) using a glassy carbon electrode was employed for analyte detection.
  • Weighted linear regression was applied to address observed heteroscedasticity in the data.
  • Method validation included determination of LOD, LOQ, linear range, accuracy, and precision, with statistical tests for data normality and heteroscedasticity.
  • Six weighting factors were evaluated to identify the optimal model.

Main Results:

  • Heteroscedastic behavior was confirmed for all analytes, validating the use of weighted linear regression.
  • The optimized weighted linear regression models significantly improved analytical accuracy, especially for low analyte concentrations.
  • Real sample analysis demonstrated high accuracy (95.21-113.23% recovery) and precision (0.85-7.98% RSD) for EP, AA, and UA.
  • SWV measurements were rapid (approx. 40 s) and solvent-free.

Conclusions:

  • Weighted linear regression is a valuable statistical tool for enhancing the accuracy of electrochemical methods for EP, AA, and UA determination.
  • The developed SWV methods are accurate, precise, rapid, and cost-effective, presenting a viable alternative to chromatographic techniques.
  • These methods are suitable for the analysis of EP, AA, and UA in complex real-world samples.