Preprocessing spectra is crucial for enhancing data quality by reducing noise, extracting essential features, and ensuring the reliability and interpretability of the information. These steps are vital for accurate analysis and deriving meaningful insights from the data.
Procedure to Apply the Processing Steps
- Click on the
button to add a preprocessing step. 
- Click on the dropdown under the “Preprocessing Step” column and select a preprocessing step.
- 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.
- 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.
- In the configuration window, make the necessary changes to the algorithm parameters and click “Set” to confirm the setting.
- 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.
- 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.
- Adaptive: Combines penalized least squares with doubly regularization to enhance the removal of baseline distortions and noise from spectroscopic data.
- 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.
- Percentile Filter: Reduces data noise by replacing extreme values with values at a chosen percentile threshold.
- 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.
- Log: Transform data by taking the logarithm of each value, often to stabilize variance and make the distribution more Gaussian-like, especially useful for data with skewed distributions or large ranges of values.
- 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.
- Scattering Correction: Reduction of unwanted variability caused by scattering effects in spectroscopy.
- Polynomial Detrending: Removal of baseline drift or systematic trends from data using polynomial fitting.
- SNV (Standard Normal Variate): Normalization of spectra to standardize by removing multiplicative effects, enhancing spectral features.