Analytical Sensor Curve Classification via ANN
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Solution Overview
Problem
Existing analytical sensor systems face challenges in efficiently classifying detection curves for molecular interactions, leading to user-dependent evaluations and potential inclusion of poor-quality curves that can negatively affect kinetic analysis results.
Innovation Solution
A method involving the acquisition of detection curves, fitting a mathematical model, calculating specific features such as association and dissociation rate constants with standard errors, and using these features to classify curves into quality groups, which can be performed by either an experienced user or an artificial neural network (ANN) for objective evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of detection curves is performed by experienced users, then quality assessment can be done with understanding of algorithms, but the process becomes time-consuming and user-dependent
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated computational system. The system automatically calculates quality parameters (baseline slope, air spikes, oscillations, fitting quality) and classifies detection curves without requiring manual user intervention, thereby eliminating time consumption while maintaining or improving classification accuracy through consistent algorithmic application.
Solution Approach 2:
The system performs self-assessment of detection curve quality by automatically computing quality parameters and classifications. The analytical system itself evaluates its own data quality through built-in algorithms that calculate baseline stability, detect air spikes, measure oscillations, and assess fitting quality, eliminating dependency on external user judgment.
2Measurement precision
If manual classification by different users is performed, then quality assessment can be done with user understanding, but the evaluation becomes user-dependent and variable
Solution Approach 1:
The patent transforms the subjective quality assessment into objective parameter-based evaluation. Instead of relying on user interpretation, the system measures specific parameters (baseline slope, air spike magnitude, oscillation frequency, fitting quality metrics) and uses these quantifiable parameters to consistently classify detection curves, eliminating user-dependent variability.
Solution Approach 2:
The patent replaces human judgment with automated computational algorithms that consistently apply the same classification criteria to all detection curves. This substitution ensures that the same detection curve will receive the same classification regardless of which user or system evaluates it, thereby improving reliability and consistency.
3Measurement precision
If complex algorithms are used for quality assessment, then accurate classification can be achieved, but the method becomes difficult to fine-tune and understand
Solution Approach 1:
The patent breaks down the quality assessment into distinct, modular parameter calculations: baseline slope calculation, air spike detection, oscillation measurement, and fitting quality assessment. Each parameter is calculated independently using straightforward mathematical operations, making the overall system easier to understand and fine-tune compared to a single complex algorithm.
Solution Approach 2:
The patent uses simple, well-defined parameters (baseline slope, air spike threshold, oscillation frequency, mean square error) that are easy to calculate and interpret. These parameters can be independently adjusted and optimized without requiring understanding of complex algorithmic interactions, making the system more accessible and easier to fine-tune.
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
This approach simplifies the classification process, reduces user dependency, and ensures only high-quality curves are included in kinetic analysis, improving the accuracy and reliability of molecular interaction evaluations.
Implementation Method 1
The calculated set of features is submitted to an artificial neural network (ANN) or expert system which produces a quality classification for each detection curve in the set of detection curves.
Data Source
AI summary
Disclosed is a method for classifying monitoring results from an analytical sensor system (20) arranged to monitor molecular interactions at a sensing surface, wherein detection curves representing progress of the molecular interactions with time are produced. The method comprises steps of: acquiring (100) a set of detection curves, fitting (101) a first mathemati- cal model to the set of detection curves; calculating (102) a set of features from the set of detection curves and fitted mathematical model; based on the calculated set of features, classifying (103) each detection curve into qual- ity classification group; and based on the classification determining which detection curves to use in kinetic analysis of the monitored molecular inter- actions.