Quantitation Data Processing Device for Ambiguous Calibration Curves
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Solution Overview
Problem
Conventional data processing devices for quantitation face challenges in deriving a valid quantitative value when a calibration curve has a curved shape approximated by a quadratic or cubic function, resulting in multiple solutions for a measurement value, leading to inaccurate quantitation results.
Innovation Solution
A data processing device that includes an extreme value acquisition part, a quantitative value region extraction part, and a quantitative value determination part to select a valid quantitation result by partitioning the quantitative value range and maximizing the number of calibration points in a region, ensuring the selected value is within the intended measurement range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a polynomial function (quadratic or cubic) is used to approximate the calibration curve, then the flexibility in fitting calibration points is improved, but the number of solutions for a given measurement value increases, leading to ambiguity in quantitation results
Solution Approach 1:
The patent segments the calibration curve into multiple monotonic sections based on extreme values (local maxima and minima). Each section is treated as a separate region where the function is monotonically increasing or decreasing. This segmentation resolves the ambiguity by identifying which specific monotonic section contains the valid quantitation result, thereby maintaining the flexibility of polynomial functions while restoring uniqueness to the measurement outcome.
2Measurement precision
If the calibration curve is constrained to be monotonic, then the uniqueness of the quantitation result is improved, but the ability to fit non-monotonic calibration data is reduced
Solution Approach 1:
The patent introduces dynamic selection of monotonic sections based on the actual calibration data characteristics. Rather than fixing the entire calibration curve as monotonic or allowing arbitrary non-monotonic behavior, the system dynamically identifies which monotonic sections are present in the data and selects the appropriate section for quantitation. This allows the system to adapt to the actual behavior of the calibration data while maintaining unique results within each monotonic section.
3Adaptability or versatility
If multiple candidates for concentration value are selected from the calibration curve, then the coverage of possible solutions is improved, but the difficulty of determining the valid candidate increases
Solution Approach 1:
The patent implements a feedback mechanism where the system first identifies all extreme values in the calibration curve, then uses these extreme values to define monotonic sections. When a measurement value is input, the system checks which monotonic section contains the corresponding concentration value and selects only candidates from that section. This feedback loop systematically reduces the number of candidates to evaluate, making the determination of valid candidates straightforward while still considering all possible solutions across different sections.
Data Source
AI summary
When there are a plurality of concentration value candidates, an extreme value of the calibration curve is detected, and the number of calibration points respectively contained in a region having a low concentration and a region having a high concentration is calculated using the extreme value as a boundary. If there is a difference between the numbers of calibration points of both regions, a concentration value candidate present in the region having a larger number of calibration points is selected as a quantitation result. When the numbers of calibration points are the same, the sign of the coefficient of the term of the second degree of the calibration curve is assessed, and a concentration value candidate present in a region in which the relationship between peak area values and concentration values is monotonically increasing is selected as a quantitation result.