Step 2 guides you through the general setup of the new calibration model.
The dialog allows you to select the calibration model, the properties
to be calibrated and specific calibration parameters.

Select the desired calibration model from the drop-down box. The following
models are available:

PLS1

PLS2

SIMPLS

MLR

PCA

PCR

Univariate

Model Settings

These additional parameters allow further adjustment of the calibration
model and result.

Prediction Result Options:

Show Calibration
details in report
This option is designed to hide potentially sensitive/unwanted information
from the calibration reports when using calibration in routine analysis of unknown samples. This
flag controls if Ranges, Calibration Pre-Processing, Calibration Data
and Regression Statistics will be included in the report when unknown
spectra are evaluated via the function 'Evaluate with...' in the Quantify
Menu. This only applies to finished calibrations which are applied with
the function evaluate. Check this option to include all Calibration Ranges,
Calibration Pre-Processing, Calibration Data and Regression Statistics
in the calibration report when spectra are evaluated.

Show outlier details in prediction report
If this option is used (which is the default) the results of outlier detection according to predefined outlier tests and discrimination criteria settings are displayed in detail on the prediction result report. Otherwise such details are not displayed. See Evaluate With... feature for details.

Number
of displayed decimals
Specifies the visible number of decimals for labels and predicted values
in reports.

Number
of Report Columns
Specifies the number of columns the results will be presented in.

Result
Unit
Specifies the unit of the calibration results.

Matrix Preprocessing Options:

Number of factors to analyze
Specifies the maximum number of factors that may be used in the calibration
model.

Mean centering
This parameter controls, whether the data matrix used for calibration
is centered before calculation or not.

Variance scaling
This parameter controls the scaling of the data matrix.

Use squared leverage
correction
Specifies wether the data matrix is leverage corrected or not.

Polynomial fit (only available for the univariate model):

Polynomial order
Specifies the order of the polynomial used in the calculation

Data passes through
origin of the coordinate system
Specifies if the regression line is forced through the origin (0,0).

Discrimination Criteria Options

If the calibration contains multiple constituents, the discrimination
criteria for each constituent can be specified. Default discrimination
criteria are a factor of 2 for the warning limit and a factor of 3 for
the alarm limit. Please see below for the actual calculation formulas
of the different parameters.

Warning Limit
Specifies the low and high warning limit factor. If these limits are
exceeded a warning will be displayed.

Alarm Limit
Specifies the low and high alarm limit factor. If these limits are
exceeded an alarm will triggered.

Outlier Detection Status
Specifies the statistical basis for the detection of outliers.

Calculation details:

Parameter

Limits

Formula
for Low Limit

Formula
for High Limit

Predicted

calculated according to the actual concentration range (not the predicted
concentrations!)

Minimum(actual) - limit * SECV

Maximum(actual) + limit * SECV

Residuals

calculated according to the prediction residuals

Minimum(residuals) - limit * SECV

Maximum(residuals) + limit * SECV

Spectral Residuals

calculated according to the worst spectral residual

0.0 for both limits as a constant

Worst(spectral residual) * limit

Spectral Residual F-Ratios

calculated according to the worst spectral residual F-Ratio

By convention, the constituents/properties to be calibrated must be
located in the labels of all
data used in the calibration. Thus each data object requires the same
label which needs to hold a certain value. These values may indicate a
concentration, a fraction, color value, etc. However, it must be a quantifiable
value, which can be interpreted in a statistical manner. The application
will scan all selected data for common labels with numerical values and
will display these in the dialog.

How can I edit the labels of my calibration
data?

The most convenient way to add a new label,
change existing label or remove labels from multiple spectra at once is
using the Label
Editor dialog. New Properties can be added directly in this
step of the wizard using the New
Property button. Clicking
on this button will start the Label Editor. It is also available in step
4 of the Calibration Model Wizard. Outside of the wizard the Label Editor
can be started from the Tools
menu.

Depending on the selected calibration model, one ore more labels need
to be chosen. Multiple labels can only be selected when using PLS2, SIMPLS
and PCR as model. Select the appropriate label by left
clicking. Hold the CTRL-key to select
more than one label.

In case of a PCA calibration
there are two different options to choose for the property evaluation
settings and an additional Groups parameter will be available (see below):

Normal PCA - No Property/Groups
defined
By selecting this option a normal PCA calibration without any property/group
definition will be executed. The calibration will yield the regular PCA
results, but the software will not be able to automatically assign the
calibration samples to groups. The groups parameter selection will be
disabled when choosing this option.

Property selection
By selecting an available label/property the software will automatically
analyze the different contents of this label and display them in the Groups
parameter section. The user may choose
different assignment options for the available groups and the software
will use these settings to automatically assign the calibration samples
to the groups according to the PCA calibration results. Refer to the Groups
section below for further details.

Groups (only available for PCA)

If the PCA calibration model is chosen an additional Groups parameter
will be available. Since the PCA is a qualitative calibration, the Groups
parameter is used to display/define the possible assignment options for
the previous selected property. The following screenshot shows an example
with a selected property that has four different assignment options:

In the above example the label "Material" has been selected
which contains four different values. These values will be autodetected
and assigned to groups as shown in the screenshot. The PCA calibration
will try to assign the calibration samples to these four groups. Samples
that do not fit into any of these groups will be marked as unassigned
in the calibration results.

Navigation

Just click the Next
> button to proceed to the next step.

Clicking the Cancel button will
abort creating a new calibration.