Interpretable Machine Learning Model-Driven Electrochemical Impedance Spectroscopic Analysis for Determination of Nitrate, Nitrite Ammonium Ions in Water

Key Points

• To develop an interpretable machine learning model for real-time detection of nitrate, nitrite, and ammonium ions in water.
• Utilized electrochemical impedance spectroscopy for nutrient analysis.
• Developed machine learning algorithms to interpret sensor data.
• Conducted experiments to validate the accuracy of the model in detecting ions.
• Achieved real-time detection capabilities for nitrate, nitrite, and ammonium ions.
• Demonstrated high accuracy and reliability in nutrient monitoring over extended periods.