Instrument, Leverages, Loadings or principal components – BUCHI NIRCal User Manual
Page 183: 42 instrument, 43 leverages, 44 loadings or principal components

Chemometrics
NIRCal 5.5 Manual, Version A
183
3.18.42
Instrument
Description
Contains the used instrument index.
Use
For 1D-scatter and dependency plots. Can be used for Outlier selection.
Method
PCR / PLS / Cluster (CLU) / SIMCA / MLR
Matrices ID
104
Tip
Details
Instrument index is a column vector.
Related Topic
Description
Contains the used instrument index.
Use
For 1D-scatter and dependency plots. Can be used for Outlier selection.
Method
PCR / PLS / Cluster (CLU) / SIMCA / MLR
Matrices ID
105
Tip
Details
Instrument index is a row vector.
Related Topic
3.18.43
Leverages
Description
Mahalanobis distance from the center of the score space to each spectra.
Use
To find Leverage Outliers.
Method
PCR / PLS / Cluster (CLU) / SIMCA
Matrices ID
32
Tip
Details
All primary PCs are used. The secondary PC selection has no effect on the
leverages.
Related Topic
Scores
Description
Contains the Leverages against the PCs for the different properties (Cluster).
Use
For PC selection.
Method
PCR / PLS / Cluster (CLU) / SIMCA
Matrices ID
94
Tip
Details
Related Topic
Leverages
3.18.44
Loadings or Principal Components
Description
Loadings build up the base for reconstructing the spectra together with the
scores and the
Eigenvalues
.
Use
Look for the wavelengths activity in the loadings and compare it with the
original spectra.
Method
PCR / PLS / Cluster (CLU) / SIMCA
Matrices ID
4
Tip
Mark interesting bands in the wavelengths and have a look to the spectra.
The wavelengths selection will also appear in the plot.
Details
Loadings are called also Principal Components or sometimes till called
"factors" (NIRCal 4 terminology). The maximum available number of PCs
calculated is called the primary PCs.
The selected PCs for predicting properties are called secondary PCs.
Related Topic
Scores
,
Eigenvalues
The loadings are artificial difference spectra. Only principal components with characteristic spectral
information should be used.