Raman Peak Extraction via Improved PCA for Meat Classification
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Solution Overview
Problem
Existing methods for extracting Raman characteristic peaks from meat samples with complex components face challenges in identifying peaks due to overlap and result in models with low robustness and high complexity.
Innovation Solution
The method employs improved principal component analysis, using a confocal microscopic Raman-spectroscopic instrument to collect data, preprocess it, and create a principal component loading scatter plot to filter and extract Raman characteristic peaks based on polar diameter and angle characteristics, optimizing the analysis for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional peak detection methods (KNN, peak tree, backward interval PLS) are used to extract Raman characteristic peaks from meat samples, then the analysis can be performed, but the model complexity increases and robustness decreases due to peak overlap and excessive characteristic vectors
Solution Approach 1:
The patent segments the complex Raman spectrum into multiple regions based on characteristic peak positions, then performs principal component analysis on each region separately. This segmentation approach reduces the dimensionality of the data and simplifies the model while maintaining accurate peak identification, directly addressing the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent extracts only the most significant principal components that contain the essential information for peak identification, discarding redundant components. This extraction process reduces model complexity by focusing on key features while preserving the ability to accurately identify characteristic peaks in overlapping spectral regions
2Measurement precision
If traditional peak detection methods are used on meat samples with complex components, then the analysis can proceed, but the processing speed decreases due to the need to handle excessive characteristic vectors
Solution Approach 1:
By dividing the spectrum into regions and performing PCA on each region, the patent reduces the total number of characteristic vectors that need to be processed. This segmentation strategy maintains classification accuracy by preserving region-specific features while significantly improving processing speed through reduced computational burden
Solution Approach 2:
The patent transforms the spectral data into principal component space, changing the parameter representation from raw spectral intensities to compressed principal component scores. This parameter transformation maintains the information needed for accurate classification while reducing the dimensionality, thereby improving processing speed without sacrificing measurement precision
Data Source
AI summary
A method for extracting Raman characteristic peaks employing improved principal component analysis comprising: using a confocal microscopic Raman-spectroscopic instrument to collect Raman spectroscopic data from surfaces of pork and beef samples; and performing preprocessing on the Raman spectroscopic data, performing principal component analysis, establishing a principal component loading scatter plot, extracting dot characteristics from the principal component loading scatter plot, analyzing same, and performing filtering on the dot characteristics to obtain Raman characteristic peaks. The method for extracting Raman characteristic peaks employing improved principal component analysis is used to extract Raman characteristic peaks from pork and beef samples, and then the Raman characteristic peaks are inputted into a classifier to undergo classification, thereby achieving high accuracy and quick classification.