Toolbar->Tools->Preprocessing Steps

The preprocessing step(s) will be applied to the Scope spectrum after clicking the capture button. You need to add the desired preprocessing step(s) and algorithm before capturing the data.

Please follow the steps below to add a preprocessing step and select the desired algorithm:

  1. Click on the   button to add a preprocessing step.                                   
  2. Click on the dropdown under the “Preprocessing Step” column and select a preprocessing step.
  3. Once the preprocessing step is selected, you can choose a specific algorithm from the dropdown list in the “Algorithm” column. Note that some preprocessing steps only have one algorithm, so there won’t be a dropdown in the “Algorithm” column for those cases.                                   
  4. To configure the algorithm parameters, click on the  icon next to the selected algorithm. This will open a configuration popup window where you can modify the algorithm parameters. If    icon is not shown, it means that the algorithm does not have any configurable parameters.
  5. In the configuration window, make the necessary changes to the algorithm parameters and click “Set” to confirm the setting.
  6. Repeat the steps above to add all the desired preprocessing steps and algorithms. Note that the preprocessing algorithms will be applied in the order they are listed. To add a new preprocessing algorithm before the current one, click on the    button in front of the current step. To remove an algorithm, click on the  button.
  7. Finally, click on the capture button    to capture the data. This will apply all the preprocessing steps in the listed order to the Scope spectrum, and other data will be computed based on the new Scope spectrum.                                                                                                                                                                                                                                     


Preprocessing Step and Algorithm Information

  • Baseline Removal: Eliminate unwanted systematic variations and noise, allowing the extraction of meaningful spectral information
    • Polynomial: Fit and subtract a polynomial function from the spectral data
    • Unispline: Utilizes a smoothing spline function to estimate and remove the baseline from spectral data
    • ALS: Iteratively estimate and subtract a baseline from the spectral data
    • arPLS: Iteratively estimates and removes the baseline from spectroscopic data by incorporating asymmetric reweighting and penalized least squares techniques
    • drPLS: Combines penalized least squares with doubly regularization to enhance the removal of baseline distortions and noise from spectroscopic data
    • Rubberhand: Robust and flexible baseline removal technique that utilizes a combination of curve fitting and iterative outlier rejection
  • Smooth: Reduce noise and improve signal-to-noise ratio by averaging adjacent data points within a specified window
    • Savgol: Uses polynomial fitting within a sliding window to reduce noise and enhance spectral data while preserving important features
    • Whittaker: Applies a penalized least squares algorithm to effectively reduce noise, smooth spectral data
    • Flat: Averaging adjacent data points within a fixed window size to reduce noise
    • Hanning: Reduces spectral leakage and provides good frequency resolution by tapering the data points smoothly at the edges of the window
    • Hamming: Improved sidelobe suppression, minimizing spectral leakage and enhancing frequency analysis in spectroscopy
    • Bartlett: Triangular smoothing window that reduces spectral leakage by gradually tapering the data points from the center to the edges, suitable for applications where the spectrum is expected to be symmetric
    • Blackman: Smoothing window with a more complex shape, featuring excellent sidelobe suppression and minimal spectral leakage, making it suitable for precise frequency analysis and peak detection in spectroscopy
  • Normalization: Scales spectral data for meaningful comparison and analysis, ensuring consistency and preserving spectral characteristics
    • Intensity: Adjusts the spectral data to a consistent range, enhancing comparability and analysis by eliminating variations in overall intensity levels
    • Minmax: Scales the spectral data to the range of 0 to 1
  • Derivative: Enhance spectral features, identify peaks, and improve spectral resolution by quantifying the rate of change in intensity with respect to the wavelength or wave number, providing valuable information about the underlying chemical or physical processes in the sample
  • Cosmic Rays/Spikes: Identification and elimination of sudden, isolated intensity spikes or cosmic ray events in the spectral data