Neural Network Ion Interference Compensation in Electronic Tongue Sensors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing ion concentrations in liquids, such as water quality, are time-consuming, costly, and lack real-time capabilities due to the limitations of classical analytical instruments and Ion Selective Electrodes (ISEs) in handling mixed ion solutions with interference from similar analytes.
Innovation Solution
A method and device utilizing a neural network algorithm to process signals from multiple electrodes, compensating for ion and electrode interference, allowing for accurate assessment of analyte ions in mixed ion solutions through an Electronic Tongue system with pre-processing techniques like Principal Component Analysis and Independent Component Analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Ion Selective Electrodes (ISEs) are used to provide rapid and quantitative analysis of ions in solution, then analysis speed is improved, but measurement precision deteriorates due to co-reactivity and interference from similar analytes
Solution Approach 1:
The invention divides the analysis task into multiple segments by using an array of multiple ISEs, each selective for different ions. Instead of relying on a single electrode that may suffer from interference, the system segments the detection function across multiple specialized sensors, allowing simultaneous measurement of multiple ions while maintaining rapid analysis speed
Solution Approach 2:
The invention introduces computational methods and data processing algorithms as intermediaries between the raw electrode signals and the final concentration readings. These computational tools process the signals from multiple electrodes, identify and correct for interferences, and calculate accurate ion concentrations, thereby resolving the precision problem while maintaining the rapid analysis capability of ISEs
2Measurement precision
If chemical precipitations are used to remove interfering analytes from the solution, then measurement precision is improved, but loss of substance occurs and further interference is introduced
Solution Approach 1:
The invention extracts the interfering substances computationally rather than chemically. By using data processing algorithms to identify and separate the signal contributions of different ions, the system removes interference effects without physically extracting or precipitating any substances, thereby avoiding analyte loss and additional chemical interferences
Solution Approach 2:
The invention replaces the mechanical/chemical separation process (precipitation) with a computational/electronic system. Instead of using chemical reagents to precipitate interfering ions, the system uses signal processing and mathematical algorithms to distinguish and quantify ions based on their unique electrochemical responses, eliminating the need for substance removal
3Loss of time
If computational methods are used to analyze co-reactive electrode sensor array data directly, then loss of time is reduced, but measurement precision deteriorates due to inability to accurately determine multiple chemical compositions
Solution Approach 1:
The invention performs preliminary action by pre-processing the electrode signals through filtering, normalization, and feature extraction before the main computational analysis. This preliminary processing enhances the quality of input data for the algorithms, enabling accurate simultaneous determination of multiple ions while maintaining rapid analysis speed
Solution Approach 2:
The invention changes the parameters of the computational analysis by using advanced algorithms that can handle non-linear relationships and interactions between multiple ions. By adjusting the computational approach to account for complex interferences and co-reactivity, the system achieves both rapid analysis and high precision in determining multiple chemical compositions
4Measurement precision
If classical analytical instruments such as HPLC, GC, CE, and PAES are used to assess ion concentrations, then measurement precision is improved, but loss of time and device complexity increase
Solution Approach 1:
The invention uses disposable or easily replaceable ISEs instead of expensive, complex classical analytical instruments. While individual ISEs have limited lifetimes and require periodic replacement, they provide sufficient measurement precision for rapid analysis without the high cost, complexity, and lengthy preparation requirements of instruments like HPLC or GC
Solution Approach 2:
The invention replaces complex mechanical and chemical separation systems (HPLC, GC, CE) with a simpler electrochemical sensing system coupled with computational analysis. This substitution maintains adequate measurement precision while dramatically reducing preparation time, instrument complexity, and operational requirements
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid, reliable, and accurate determination of ion concentrations in real-time, suitable for in-situ water quality monitoring and nutrient analysis, reducing the need for laboratory samples and improving prediction accuracy.
Implementation Method 1
each of which is configured to generate a signal in response to sensing a selected ion in the liquid
Data Source
AI summary
A method of assessing concentration of analyte ion(s) in a liquid can include contacting the liquid with a plurality of electrodes, each of which is configured to generate a signal in response to sensing a selected ion in the liquid. The signal received from each of the electrodes can be processed using a neural network algorithm trained to calculate ion interference between the selected ion and other ions in the liquid sensed at one of the electrodes and/or electrode interference between ones of the electrodes sensing a same selected ion based on a result of a comparison of training data. The ion interference and/or electrode interference can be compensated for, and the concentration of the analyte ion(s) in the liquid can be assessed on the basis of a compensated output from the neural network algorithm.