Baseline Setting Method Using Semicircles for Spectrum Analysis
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Solution Overview
Problem
Conventional baseline setting methods for spectrum measurement are complex, requiring high computational load and numerous parameters, making it difficult to accurately set baselines for spectra with varying shapes and peak structures, especially in cases like infrared transmission spectra with rough surfaces or carbon black specimens where peaks are small and hard to detect.
Innovation Solution
A method using semicircles or semi-ellipses to calculate baseline points by specifying empirical values for curvature and radius, allowing for automatic baseline setting with low computational load, applicable to any spectrum shape, including rising, falling, or wavy patterns, by comparing differences between the spectrum and the circular or elliptical figures to determine optimal baseline positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional baseline setting methods (using circles, ellipses, or other geometric figures) are used to estimate the baseline, then the baseline can be set for various spectrum shapes, but the computational load becomes high and many parameters (shape, size, position of figures) must be specified
Solution Approach 1:
The baseline estimation process is divided into multiple discrete steps: (1) dividing the spectrum into multiple regions, (2) selecting representative points in each region, (3) calculating baseline values at these points, and (4) connecting the points to form the complete baseline. This segmentation reduces computational complexity by processing smaller portions sequentially rather than analyzing the entire spectrum at once.
Solution Approach 2:
The method automatically determines baseline parameters based on the spectrum data itself without requiring external specification of geometric figure parameters. The algorithm self-adjusts by identifying peak positions and widths from the spectrum, then uses these to determine appropriate baseline positions and shapes, eliminating the need for manual parameter specification.
2Productivity
If the X radius of the semicircle or semi-ellipse is set too small, then the computational load is reduced, but the baseline is set at a high position in the peak and becomes too close to the spectrum
Solution Approach 1:
The method dynamically adjusts the radius parameter based on the actual peak width detected in the spectrum. By setting the radius to at least twice the full width at half maximum (FWHM) of the peak, the baseline position automatically adapts to the spectrum characteristics, ensuring adequate clearance from peaks while maintaining computational efficiency through a standardized calculation approach.
3Ease of operation
If simple geometric figures are used for baseline setting, then the method is easy to understand and implement, but it cannot accurately handle spectra with varying shapes (rising, falling, wavy patterns)
Solution Approach 1:
The baseline estimation method is made dynamic by allowing the semicircle or semi-ellipse to be positioned at multiple different locations across the spectrum and by adjusting the radius based on local peak characteristics. This dynamic adaptation enables the same simple geometric figure to effectively handle various spectrum shapes including rising, falling, and wavy patterns while maintaining ease of understanding and implementation.
Data Source
AI summary
The figure is fixed in a given position. If peaks are seen in the positive Y direction, the minimum value of the difference in height between the spectrum and the figure in the range where the figure is present on the X-axis. The minimum value and the height of the figure at the reference point are added. The figure is moved within a range containing the reference point, and the minimum value of the difference in height between the spectrum and the figure is added to the height of the figure at the reference point, at each point on the figure. A maximum value L(xi) of the calculated values is obtained, and the maximum value L(xi) is obtained as a baseline value at the X coordinate of the reference point.