Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools

Area of Science:

• Analytical Chemistry
• Environmental Science
• Computational Statistics

Background:

• Ion-selective electrodes (ISEs) are crucial for chemical analysis, but their data interpretation can be complex.
• Traditional methods may not fully leverage advanced statistical approaches for ISE data, particularly concerning sensor arrays and detection limits.
• Bayesian statistical methods offer robust frameworks for data analysis, including uncertainty quantification.

Purpose of the Study:

• To introduce ISEtools, a novel R software package designed for straightforward Bayesian analysis of ISE data.
• To enhance the estimation of detection limits and associated uncertainties for sensor array measurements.
• To make advanced Bayesian techniques accessible to researchers without specialized statistical expertise.

Main Methods:

• Development of the ISEtools package in R, comprising three core functions: loadISEdata, describeISE, and analyseISE.
• Implementation of Bayesian statistical models utilizing the Nikolskii-Eisenman equation for ISE data analysis.
• Automated data structure determination, model fitting, and result extraction with publication-ready figure generation.

Main Results:

• ISEtools facilitates the simultaneous incorporation of all collected data, naturally accommodating sensor arrays.
• The Bayesian approach within ISEtools provides improved limit of detection estimates with appropriate uncertainty quantification.
• The package returns easily interpretable results, demonstrating utility through a worked environmental application.

Conclusions:

• ISEtools offers a powerful yet accessible tool for researchers to perform advanced Bayesian analysis on ion-selective electrode data.
• The software package simplifies complex statistical computations, enhancing the accuracy and reliability of analytical results.
• This contributes to more robust environmental monitoring and chemical sensing applications through improved data analysis.