Normalize is used to scale
the intensity values of a 2D or 3D data object within user defined limits.
Usually this function is useful for visual comparison of two or more 2D
data objects. It is also required as pre-processing function for many
other mathematical functions even in chemometric
analysis or colorimetric
analysis. Data might be normalized to an absolute user defined intensity
interval or the current y-offset is kept. The latter is called relative
normalization.
All intensities of the 2D or 3D data object will be corrected using
a scaling factor f to fit data
to the user defined borders of the new intensity interval. The scaling
factor f will be derived from
the current global minimum Imin and maximum
Imax
Intensities and the new user defined minimum Iâmin and maximum
Iâmax
intensities of the 2D data object as follows:
Each intensity Ii
of the 2D or 3D data object is then recalculated to fit the new interval
using a linear transformation:
The following normalize parameters might be adjusted:
Minimum Y
The Minimum Y parameter is only
used, if the Normalize Minimum
flag is set true. It holds the new lower bound of the intensity interval.
After normalization, the lowest intensity within the 2D or 3D data object
is scaled to the Minimum Y value.
Maximum Y
The Maximum Y parameter is always
applied. It holds the new upper bound of the intensity interval. After
normalization, the highest intensity within the 2D or 3D data object is
scaled to the Maximum Y value.
Normalize minimum
Two types of normalizations can be applied:
Absolute normalization
Data is scaled within the user defined interval between Minimum
Y and Maximum Y values.
After normalization, the lowest intensity value in the 2D or 3D data object
is equal to the Minimum Y value
and the highest observed intensity value is equal to Maximum
Y.
Relative normalization
This procedure considers the current y-offset of the 2D or 3D data
object that will be kept. Data is scaled between a user defined interval
starting with the lowest available intensity in the spectrum and ending
with Maximum Y value. The Minimum Y value is ignored in this case.