Raman Spectrogram Identification via Peak Extraction
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Solution Overview
Problem
Existing Raman spectrogram analysis methods have low recognition rates and high computing times when matching detected substances with standard spectra, failing to effectively utilize the fingerprint characteristics of molecules.
Innovation Solution
A method involving peak information extraction and comparison, including ordering peaks, calculating absolute differences, and using a penalty function to determine matching, followed by correlation coefficient analysis to identify matches between measured and standard spectrograms, reducing computational load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If direct data modeling and matching of Raman spectrograms is performed, then the recognition process is simple, but the recognition rate is low and computing time is high
Solution Approach 1:
The patent segments the Raman spectrogram into multiple peaks and extracts key characteristics (peak position, intensity, area) from each peak. This segmentation transforms the continuous spectral data into discrete feature points, enabling more efficient comparison and matching while improving recognition accuracy by focusing on characteristic peaks rather than processing the entire spectrum at once.
2Device complexity
If direct data modeling and matching of Raman spectrograms is performed, then the process is straightforward, but the computing time is high
Solution Approach 1:
The patent extracts key peak information (position, intensity, area) from the Raman spectrogram, separating the essential diagnostic features from the complete spectral data. This extraction reduces the data volume significantly, allowing for faster comparison and matching operations while retaining the critical information needed for accurate substance identification.
3Reliability
If peak information extraction and comparison is performed, then recognition accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent applies local quality by focusing analysis on specific peak regions rather than treating the entire spectrum uniformly. Each peak is analyzed for its characteristic properties (position, intensity, area), and these local features are then integrated for overall matching. This approach improves accuracy by emphasizing diagnostically important regions while managing complexity through localized processing.
4Reliability
If peak information extraction and comparison is performed, then recognition accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent extracts only the essential peak characteristics (position, intensity, area) from the full Raman spectrum, discarding redundant information. This selective extraction reduces the computational burden by focusing processing resources on the most diagnostically relevant features, thereby improving recognition accuracy while minimizing energy consumption for computation.
Data Source
AI summary
The disclosure provides a method for identifying a Raman spectrum and an electronic apparatus. The method includes steps of: measuring a Raman spectrum of a substance to be detected so as to obtain a measured spectrogram, the measured spectrogram including a series of data; extracting peak information of the measured spectrogram, the peak information including a peak intensity, a peak position and a peak area; comparing the peak information of the measured spectrogram with peak information of a prestored standard spectrogram so as to identify whether or not the peak information of the measured spectrogram matches the peak information of the standard spectrogram; and comparing, when identifying that the peak information of the measured spectrogram matches the peak information of the standard spectrogram, data of the measured spectrogram with data of the prestored standard spectrogram, so as to further identify whether or not the measured spectrogram matches the standard spectrogram.