This page summarizes possibilities on applying chemometric models from 3rd party software onto hyperspectral data for off-line or in-line purposes.
Chemometric modelling is a very essential step when investigating chemical or physical material properties. Typically, experts like to do this in their familiar software environment.
This is supported by the export of data (spectra, cubes) and import of models back into the Perception Studio software program. When configuring the real-time processing core (Perception Core) through the Perception Studio, a data processing system is obtained, allowing multiple predictions per spatial object pixel in industrial real-time.
When doing predictions in the industrial environment, a number of data processing steps must be considered:
The sensor data preprocessing aims for a "standardization" of the instrument.
Therefore electrically and optically disorder (like sensor noise, optically distortions, etc.) are corrected by this data processing step. Data from different instruments (same type but different batch) ought to get comparable to each other.
The hyperspectral preprocessing aims for a type of spectra best suited for further investigation and analysis.
The feature extraction aims for the extraction of a certain value from spectroscopic data. This value can be the prediction value obtained by applying a linear model onto spectral data. Since this step is typically done in parallel, per spatial object pixel multiple predictions can be obtained simultaneously.
Sometimes also feature operation is an important step. By feature operation a number of features (output of the feature extraction), like predictions, are mathematically combined with each other.
Applying models from 3rd party software is possible without any restrictions, when
Preprocessed means: sensor data preprocessing as well as hyperspectral preprocessing was applied to the spectra using the Perception Studio program (not the 3rd party software).
A linear model is described by the vector B and the scalar B0. X is a matrix of spectra (input) and Y is a vector of predictors (output).
The typically workflow on getting and applying a linear model is:
In case of real-time processing:
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