Chromatographic Data Processing via Partial Regression Segmentation
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Solution Overview
Problem
Existing chromatography techniques are susceptible to noise, which affects the accuracy of virtual curves and characteristic points, and require a long time to determine appropriate regression curves.
Innovation Solution
A chromatographic data processing device that performs data processing by obtaining partial regression curves from combined time-series data, allowing for the quick acquisition of peak characteristic points with reduced noise susceptibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual curves and characteristic points are obtained using conventional regression methods, then characteristic points can be determined, but the results are greatly affected by noise and require long processing time for curve selection
Solution Approach 1:
The patent divides the measured time-series data into multiple data groups, each containing a predetermined number of data points. Partial regression curves are calculated for each data group independently, and partial characteristic points are obtained for each partial regression curve. This segmentation approach reduces the impact of noise on the overall characteristic point determination while maintaining processing efficiency.
2Measurement precision
If conventional regression curve methods are used, then characteristic points can be obtained, but the process requires determining appropriate curve types and time ranges which takes long time
Solution Approach 1:
The patent segments the data into multiple groups and calculates partial regression curves for each segment independently. This eliminates the need to determine the entire curve type and time range beforehand, as each segment is processed with a standard regression method. The segmented approach significantly reduces the time required for curve selection and characteristic point determination.
Solution Approach 2:
The patent employs an automatic determination process where the system self-selects appropriate processing parameters for each data group without requiring manual intervention for curve type selection or time range determination. The partial regression curves are automatically calculated for predetermined data groups, and characteristic points are extracted automatically, making the process self-service and highly efficient.
3Measurement precision
If full-range regression is performed on all measured data, then comprehensive characteristic points can be obtained, but the processing time increases significantly
Solution Approach 1:
The patent divides the full measured data range into multiple data groups with a predetermined number of points each. Partial regression curves are calculated for each segment rather than performing regression on the entire dataset. This segmentation maintains the accuracy of characteristic point determination while significantly reducing processing time, as each segment is processed independently and more efficiently.
Solution Approach 2:
The patent applies partial regression action by calculating regression curves only for predetermined segments of the data rather than performing exhaustive regression on all data. This partial action approach achieves sufficient accuracy for peak characteristic points while dramatically improving processing speed and productivity.
Data Source
AI summary
Disclosed is a chromatographic data processing device to easily obtain peak characteristic points in a short period of time with less susceptibility to noise. The chromatographic data processing device includes a partial regression curve calculation unit configured to obtain a partial regression curve for each of a plurality of data groups in which a predetermined number of measured time-series data are combined, a partial characteristic point acquisition unit configured to obtain a partial characteristic point for each of the obtained partial regression curves, and a peak characteristic point acquisition unit configured to obtain a peak characteristic point in the measured time-series data pieces on the basis of the partial characteristic points.