Raman Data Processing and Interpretation
In Raman spectroscopy measurement, especially in the measurement of nanomaterials with unsatisfactory growth quality, even if the sample used for measurement has been prepared as good as possible, the spectrometer has been fine-tuned, and the appropriate measurement parameters have been selected. The original spectrum is often still the unreal spectrum, and there is often a more or less noise spectrum. Therefore, the spectrum needs to be processed and analyzed.
Methods to eliminate and reduce the noise spectrum:
- Smoothing method
- Deduction method
- Direct deduction method
- Spectrum deduction
- Weighted deduction method
Signal smoothing is the most commonly used method to eliminate noise. The basic assumption is that the noise contained in the spectrum is zero-average random white noise. If the average value of multiple measurements is taken, the noise can be reduced and the signal-to-noise ratio can be improved. Commonly used methods of smoothing include neighbor comparison method, moving average method, exponential average method and so on.
If, after analysis, we know that the original spectrum contains spectra from certain impurities, defects or cosmic rays, we can deduct them directly by deduction.
Sometimes the composition of the sample is not known exactly, or the source of the sub-spectrum is not clear. In this case, the spectrum deduction method can often be used.
Due to the different source components of the impurity spectrum and the difference in its contribution to the spectral intensity, the different sub-spectra are weighted and then deducted.
Spectrum processing process
Taking Origin software as an example, the process of peak fitting for Raman data is as follows.
- Make a line graph, select the range of fitting (abscissa axis);
- Perform FT (Fourier Transform) on the curve;
- Automatically select the points that deduct the background (points can be selected on your own);
- Manually adjust the position of the points to form a straight line;
- Deduct the background;
- Select the fitting method (Lorentz fitting or Gaussian fitting) and the number of fitting peaks, and start peak searching;
- Adjust the peak position according to your requirements;
- Adjust the height and width of each small peak;
- After peak separation, click the button to display each curve and click plot to draw a picture.
- One way to interpret Raman spectra is to identify molecular functional groups, which are different subunits of molecules. The vibrations of functional groups appear in the Raman spectra with unique Raman shifts. These characteristic changes allow unknown compounds to be associated with known classes of substances.
- A different analysis program involves looking at the fingerprint area of a particular spectrum. In addition to the vibration of specific functional groups, the molecular skeleton vibration can also be seen in the Raman spectrum. The skeleton vibrates and has a matter-specific pattern is usually seen at wavenumber below 1500 cm-1. This region is the most critical part of the recognition spectrum.
- The third method involves the use of interpretation software with spectral databases and comparison algorithms. The software generates a matching factor that ranges from 0 (mismatch) to 100 (perfect match). Users usually define a threshold to treat the test sample as an appropriate match with a known substance.
The processing of Raman data has become the task of experts, who can understand the data and its format, as well as software, programs and databases. With the support of the most advanced Raman platform and expert team, T,C&A Lab is committed to helping customers with Raman experiment design, data collection and interpretation. Welcome to contact our experts for consultation.
- Ryabchykov, O.; et al. Analyzing Raman spectroscopic data. Physical Sciences Reviews 4.2 (2019).
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Data Interpretation & Simulation
- Spectrum and Data Interpretation
- Simulation Calculation
- Softwares Applied