Spectral Data Processing Apparatus for Biological Sample Analysis
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Solution Overview
Problem
Current methods for analyzing biological samples using multivariate analyses, such as PCA and ICA, face challenges with increasing data dimensions and measurement points, leading to exponential calculation time and complexity, making it inefficient for processing large datasets or broader observations.
Innovation Solution
A data processing apparatus that classifies spectra into groups using second base vectors, extracts relevant data, and obtains first base vectors through multivariate analysis, reducing data dimensions and calculation time by performing PCA and ICA on reduced datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multivariate analysis (PCA/ICA) is performed on all spectra from the entire sample, then comprehensive spatial distribution information is obtained, but calculation time increases exponentially
Solution Approach 1:
The patent divides the entire sample into multiple regions of interest (ROIs) based on preliminary analysis or user selection. Instead of performing multivariate analysis on all spectra from the entire sample, the analysis is segmented and performed only on selected ROIs. This segmentation reduces the total number of spectra subjected to PCA/ICA calculation while still providing comprehensive spatial distribution information for the areas of interest, thereby resolving the contradiction between information completeness and calculation time.
Solution Approach 2:
The patent performs preliminary processing steps before the main multivariate analysis, including: (1) acquiring raw spectral data, (2) performing initial preprocessing (noise filtering, baseline correction), and (3) optionally performing a quick PCA to identify major components and select ROIs. These preliminary actions prepare the data in advance, allowing the subsequent ICA analysis to be performed more efficiently on pre-processed and selectively extracted spectra, thus reducing overall calculation time while maintaining analysis quality.
2Loss of information
If the number of measurement points and spectral dimensions increase, then more detailed component information is obtained, but the amount of calculation increases exponentially
Solution Approach 1:
The patent extracts only the necessary spectral information for the analysis. Instead of using all spectral dimensions and all measurement points, the system: (1) extracts spectra only from selected regions of interest, (2) optionally selects only the most informative spectral regions (wavenumber ranges) based on preliminary PCA or user input, and (3) extracts only the essential component information needed for the specific application. This extraction approach maintains detailed component information for the areas of interest while significantly reducing the total data volume and calculation complexity.
Solution Approach 2:
The patent dynamically adjusts analysis parameters based on the specific sample and application requirements. This includes: (1) adjusting the number of components to extract based on eigenvalue scree plots or user input, (2) selecting appropriate spectral resolution and range for the specific application, (3) adjusting the number of iterations for ICA convergence, and (4) selecting appropriate preprocessing parameters. These parameter changes allow the system to optimize the balance between information detail and calculation complexity for each specific case.
Data Source
AI summary
A data processing apparatus that processes data including a plurality of spectra includes a group setting unit, an extracted data generation unit, and a base vector obtaining unit. The group setting unit classifies the plurality of spectra into a plurality of groups. The extracted data generation unit selects at least one spectrum from each of the groups set by the group setting unit and generates extracted data including the selected spectra. The base vector obtaining unit obtains, from the extracted data generated by the extracted data generation unit, base vectors for attributing the spectra to corresponding components.