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Nonlinear regression in parameter estimation from polarographic signals

Pais¹, Pereira, Redinha

¹Departamento de Quimica, Universidade de Coimbra, Portugal.

Computers & Chemistry

|May 18, 2000

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This study details polarographic data curve analysis using nonlinear least-squares methods. Error estimates are rigorously verified through Monte Carlo simulation and resampling techniques for reliable parameter assessment.

Area of Science:

Analytical Chemistry
Electrochemistry

Background:

Polarographic analysis is crucial for quantitative chemical measurements.
Accurate error estimation is vital for the reliability of polarographic data interpretation.

Purpose of the Study:

To present a comprehensive methodology for the detailed treatment of polarographic data curves.
To implement robust error analysis for parameter estimation in polarographic studies.

Main Methods:

Application of nonlinear least-squares (standard and errors-in-variables models) for data fitting.
Utilization of Monte Carlo simulation and resampling techniques for error estimation validation.

Main Results:

A detailed treatment of polarographic data curves was successfully developed.

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Error estimates for model parameters were validated, confirming their reliability.

Conclusions:

The described nonlinear least-squares approach provides a robust framework for polarographic data analysis.
Monte Carlo and resampling methods effectively verify error estimates, enhancing the credibility of polarographic results.