SERDS Raman Spectroscopy Reconstruction Algorithm
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Solution Overview
Problem
Existing Raman spectroscopy methods face challenges in generating high-quality spectra due to fluorescence and scattering backgrounds, particularly when samples are not prepared, and current algorithms for shifted-excitation Raman difference spectroscopy (SERDS) are not automated, qualitative, or quantitative, leading to artifacts and requiring prior knowledge of the sample.
Innovation Solution
A method involving time-shifted excitation radiation with two wavelengths, normalization, and a novel reconstruction algorithm to calculate Raman spectra, allowing for automated and quantitative SERDS without prior sample knowledge, using a DFB diode laser driven by different currents to generate varying wavelengths and a data processing device to eliminate background and reconstruct spectra.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shifted-excitation Raman difference spectroscopy (SERDS) is used to eliminate broadband background, then the signal-to-background ratio is improved, but the reconstruction algorithms require prior knowledge of the sample and produce artifacts
Solution Approach 1:
The patent changes the spectral parameters by recording Raman spectra at two different excitation wavelengths (λ1 and λ2). By systematically varying the excitation wavelength and recording corresponding difference spectra, the method transforms the spectral data to enable artifact-free reconstruction without requiring prior sample knowledge. The key parameter change is the excitation wavelength, which when shifted, causes the Raman peaks to shift differently from the broadband background, enabling their separation.
Solution Approach 2:
The patent segments the spectral information by recording separate spectra at different excitation wavelengths. The total spectrum is divided into multiple measurements taken at λ1 and λ2, which are then processed through difference spectroscopy. This segmentation allows the Raman signal to be isolated from the background by exploiting the differential response to wavelength changes, eliminating the need for complex single-step reconstruction algorithms.
2Measurement precision
If manual fitting algorithms are used to reconstruct Raman spectra from difference spectra, then spectral information can be recovered, but the process cannot be automated and requires user intervention
Solution Approach 1:
The patent implements self-service by creating a reconstruction method that automatically processes the difference spectra without requiring manual user intervention. The systematic approach of recording multiple difference spectra at sequentially shifted excitation wavelengths allows the system to self-correct and reconstruct the Raman spectrum algorithmically. The method inherently contains all necessary information for reconstruction, eliminating the need for external user knowledge or manual fitting operations.
Solution Approach 2:
The patent performs preliminary action by systematically recording multiple difference spectra at pre-planned excitation wavelength shifts before reconstruction is needed. By collecting the complete set of difference spectra in advance with known wavelength increments, the system prepares all necessary data for automatic reconstruction. This preliminary data collection at structured wavelength intervals enables subsequent automated processing without requiring real-time user decisions.
3Productivity
If DFB laser wavelength is modulated by injection current for fast switching, then on-line analysis speed is improved, but wavelength stability may be compromised
Solution Approach 1:
The patent applies periodic action by modulating the DFB laser injection current in a systematic sequence to cycle through multiple excitation wavelengths (λ1, λ2, λ3, etc.). The current is varied periodically according to a predetermined pattern, causing the laser wavelength to shift in a controlled periodic manner. This periodic modulation enables fast switching between wavelengths while maintaining reliability through the systematic and repeatable nature of the current variation, allowing the system to return to reference wavelengths for stabilization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method enables automated, qualitative, and quantitative SERDS, improving the signal-to-background ratio and preserving quantitative information, making it suitable for on-line and in-situ applications, and can be applied universally to various Raman apparatuses, reducing noise and increasing signal with repeated applications.
Implementation Method 1
a first excitation radiation having a first wavelength and to a second excitation radiation having a second wavelength... a DFB diode laser driven by different currents to generate varying wavelengths
Implementation Method 2
spectral analysis of the first excitation radiation scattered by the medium to be analyzed
Data Source
AI summary
A method and a device for generating and for detecting a Raman spectrum enables an automated, or automatable, and at the same time quantitative SERD spectroscopy (for example concentration measurement series). To this end, during the SERD spectroscopy, a first spectrum and a second spectrum are standardized in relation to one another in terms of intensity values and a first difference spectrum is subsequently calculated, a second difference spectrum is calculated, the first difference spectrum is converted into a first transformation spectrum, the second difference spectrum is converted into a second transformation spectrum, and the Raman spectrum is calculated by adding the first transformation spectrum and the second transformation spectrum.