Hello and welcome to another IONTOF tutorial.
My name is Julia and today I would like to show you how to perform MVSA calculations in SurfaceLab.
Multivariate Statistical Analysis (MVSA) refers
to a set of statistical methods which examine
relationships among multiple variables at
the same time.
It is often used to reduce the degree of complexity
in a data set by reducing the number of variables
without compromising the essential information.
SurfaceLab 7 includes:
Principle Component Analysis (PCA), Maximum
Autocorrelation Factors (MAF), Multivariate
Curve Resolution (MCR).
Now let me show you how MVSA calculations
are done in SurfaceLab.
In order to perform a Calculation, open the
MVSA Calculations tab in order to address
the options.
This tab is available in each program, regardless
of the data type.
Select the data type of your data set and
select the MVSA calculation method, for example
PCA.
PCA - Principle Component Analysis - is looking
at the variance in the data set.
It will highlight whatever the largest difference
is.
Mathematically speaking it is a matrix transformation
where the matrix consists of samples
and variables.
In TOF-SIMS samples can be spectra, pixel
of an image or data points of a depth profile.
The variables are the integrated peak intensities.
PCA helps to identify the most characteristic
peaks explaining the variance.
In the source data options section you can
choose to use the dead time correction, which
is always recommended, use a lateral shift
correction, if available, or use data binning
for better statistics.
In the next section you can decide either
to use all mass intervals specified in the
peak list or use a specific subset of intervals
by pressing the "Select" button.
This is suited to exclude for example saturated
peaks.
Multiselect the mass intervals you would like
to use for the MVSA calculation and move them
with the arrow button.
Press OK to confirm the selection.
In the PCA Options section you can select
different methods for scaling and centering.
Depending on the selected data type and MVSA
method the recommended calculation options
are used by activating the "Use Defaults"
check box.
After setting up all the calculation options,
press the “Start“ button to start the
calculation.
As an intermediate result the software will
show the eigenvalue plot.
The eigenvalues measure the amount of variation
retained by each factor.
Eigenvalues are large for the first factor
and small for the subsequent factors.
We examine the eigenvalues to determine the
number of factors to be considered.
There is no well-accepted objective way to
decide how many factors are enough.
This will depend on the specific field of
application and the specific data set.
Select the number of factors being necessary
for your analysis by using the slider or the
keyboard.
Press “Finalize” in order to finish the
calculation and to generate the respective
score images.
The MVSA results are displayed as an additional
data view in the data list.
The Scores Images are displayed in the Image
Program, and the corresponding loadings are
shown in the Spectra Program.
Please note: If MCR is used as MVSA method,
a number of factors has to defined before
running the calculation.
In order to do so it is recommended to run
a PCA first.
The resulting eigenvalue plot is a good indicator.
I hope you enjoyed this video and have some
fun playing around with the new MVSA feature.
My name is Julia – thank you for watching
and goodbye.
