Application of neural network calibrations to an halide ISE array.

Area of Science:

• Analytical Chemistry
• Electrochemistry
• Computational Chemistry

Background:

• Ion-selective electrode (ISE) arrays are crucial for chemical analysis.
• Interference between ions like bromide and chloride complicates ISE calibration.
• Traditional calibration methods often rely on complex theoretical models, such as the Nikolskii-Eisenman equation.

Purpose of the Study:

• To evaluate the effectiveness of Neural Network (NN) technique for calibrating an ISE array.
• To assess the NN method's performance in handling ion interference, specifically bromide and chloride.
• To compare the NN approach against traditional linear multivariate calibration methods.

Main Methods:

• Application of a Neural Network (NN) model for ISE array calibration.
• Utilizing synthetic mixture solutions containing varying concentrations of chlorides and bromides.
• Focusing on the determination of bromide concentration as a key performance indicator.

Main Results:

• The NN method successfully calibrated the ISE array, even with significant ion interference.
• Bromide determination using the NN approach yielded superior results compared to linear methods.
• The NN technique eliminated the need for estimating complex coefficients in the Nikolskii-Eisenman theoretical relation.

Conclusions:

• Neural Networks offer a robust and effective alternative for ISE array calibration, particularly in the presence of interfering ions.
• The NN method simplifies the calibration process and enhances accuracy for specific ion determination, such as bromide.
• This study highlights the potential of machine learning in advancing electrochemical sensing and analytical chemistry.