Data Smoothing Method Using Variable Width for Peak Separation
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Solution Overview
Problem
Conventional data smoothing methods cause distortion near peak apexes and fail to separate closely spaced peaks, affecting reproducibility due to a fixed smoothing width that is insufficient for peaks with widths narrower than the target range.
Innovation Solution
A data smoothing method that calculates standard error at each data acquisition point to dynamically determine a variable smoothing width, reducing noise near baselines and preserving peak details by using a narrower smoothing width at points with higher standard errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a fixed smoothing width is used to suppress noise near the baseline, then noise reduction is improved, but distortion is caused in data near peak apex
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed smoothing width to a variable smoothing width that adapts to local data characteristics. The smoothing width is dynamically adjusted based on the standard error at each data point, allowing the system to optimize noise suppression in baseline regions while preserving peak integrity in high-variance regions.
Solution Approach 2:
The patent implements local quality by applying different smoothing widths to different regions of the data based on their local characteristics. Regions with high standard error (such as peak areas) receive narrower smoothing widths to preserve detail, while regions with low standard error (such as baseline areas) receive wider smoothing widths for better noise suppression.
2Object-affected harmful factors
If a fixed smoothing width is used for baseline noise suppression, then noise reduction is improved, but closely spaced peaks cannot be separated
Solution Approach 1:
The system dynamically adjusts the smoothing width based on local standard error, enabling it to maintain narrow widths in regions with closely spaced peaks to preserve their separation, while using wider widths in baseline regions for effective noise suppression.
Solution Approach 2:
Different smoothing widths are applied to different regions based on their local information content. Regions containing closely spaced peaks are identified through high standard error and receive narrower smoothing to preserve peak separation information, while baseline regions receive wider smoothing for noise reduction.
3Object-affected harmful factors
If a larger smoothing width is used to reduce noise, then noise suppression is improved, but reproducibility deteriorates due to data distortion
Solution Approach 1:
The patent employs dynamics by making the smoothing width variable rather than fixed, allowing the system to achieve consistent reproducibility across different data types and conditions. The smoothing width adapts to the local standard error, ensuring that noise suppression does not compromise the integrity of important features like peaks.
Data Source
AI summary
A data smoothing method which achieves improved reproducibility than conventional methods is provided. With the data smoothing method of the present invention, a standard error of numerical data at each data acquisition point or of data based on the numerical data is calculated, and a smoothing width is determined based on the standard error in such a way that the smoothing width becomes narrower for a data acquisition point for which the standard error is greater. The numerical data at each data acquisition point or data based on the numerical data is smoothed using the determined smoothing width.