






Derivative calculation is carried out by applying the SavitzkyGolay algorithm. In this method nth order derivatives are
obtained while data are smoothed at the same time to reduce the noise. First or second order derivatives can be calculated including
5 to 25 smoothing points. Please note that derivatives are taken in the spectral domain, only. Details of the SavitzkyGolay
algorithm can be found in the literature:
A. Savitzky and M. Golay. Smoothing and
Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964 Vol 36(8):1627.
Any type of data blocks can be handled (including also derivatives). Derivative spectra are stored in a data block reserved
exclusively for derivative spectra. If this block is not empty the data are overwritten without warning when obtaining derivatives
again (see also Internal Data Organization, Table II )).
Derivative calculation is always carried out on one complete 3D spectral data block.
Select the source data block by clicking the appropriate radio button, then select the number of smoothing points and the order of
the derivative. To finally obtain the derivatives click on the 'derive' button.
Parameter used for obtaining derivative spectra can be stored within the program workspace and are accessible in the
File Info menu (File Info → File Manipulations →
derivatives). These parameters are also shown in the command line window.



The CytoSpec 'normalization' subroutine offers three different methods of spectra normalization:
 Offset correction
 MinMax normalization
 Vector normalization
 SNV (standard normal variate)
Offset correction performs are linear correction of the complete spectrum such that at least on point of the spectral
region indicated equals zero. Spectra are not scaled in this mode.
MinMax normalization indicates that all spectra of the source data block are scaled between zero and one, that is the
maximal absorbance value of the spectrum in the selected spectral region equals one, the minimum 0. You can use this normalization
method to perform a simple band normalization (e.g. for the amide I band).
Vector normalization is carried out in the following way: spectra are first meancentred, i.e. the average value of the
absorbances is calculated for the spectral region indicated. This value is then subtracted from the spectrum. Then, the spectra
are scaled such, that the sum squared deviation over the indicated wavelengths equals one.
Standard Normal Variate (SNV) A standard normal variate is a normal variate with mean μ=0 and standard deviation sigma=1.
SNV normalization is achieved by dividing meancentred spectra by the standard deviation over the spectral intensities giving the
resulting spectra a unit standard deviation of one.
To perform normalization select first the source data block by activating the appropriate radio button (see also
Internal Data Organization, Table I ). The target data
block will be specified in the gray field of the normalization window. Please note that the data of the target data block will be
overwritten without warning! Then, select the type of normalization and use the keyboard to enter the wavenumber values between
which the spectra the spectra will be normalized. To start normalization click onto the 'norm' button. If you wish to
cancel the operation press 'cancel'.
Parameters used for normalization such as spectral range are stored within the program workspace and are accessible through the
'File Info ' menu (File Info → File Manipulations →
type of data block). These parameters are also shown in the command line window.


CytoSpec's 'cut' subroutines offer two different methods to cut hyperspectral data sets:
 Cutting in the spectral domain, and
 Crop images in the spatial domains.

Cutting in the spectral (z) dimension can be used to narrow the frequency range of spectral data files. This may be useful
to free some memory before memoryconsuming calculations such as 3D Fourier self deconvolution are carried out. Define the frequency
range to be kept, then click on the 'cut' button to start the function. By pressing the 'cancel' button you can cancel the operation.

Note that the 'cut/crop' function overwrites all existing data blocks (see also
Internal Data Organization, Table IV).
The parameter used for 'cut' are stored within the program workspace and are accessible through the
'File Info' menu bar (File Info → File Manipulations →
type of data block). These parameters are also shown in the command line window.


CytoSpec's 'interpolation' routines offers two different methods of interpolation:
 interpolation in the spectral domain, and
 interpolation in the spatial domains.

Interpolation in the spectral (z) dimension changes the spacing between spectral data points. The spacing can be increased
or decreased by the 'interpolation factor', which can vary between 1/32 and 32. For example, if a factor of 4 is chosen, the number
of data points is increased by a factor of 4, i.e. one frequency interval is filled with (41) additional data points. In this case
the program performs an onedimensional interpolation of the spectra. Using a large interpolation factor (e.g. 32) the number of
data points of the new spectrum may become rather large. The actual number of data points depends on the start and end frequency
and the frequency interval of the original spectrum.
If a factor smaller than 1 is chosen, the data point spacing is decreased. For example, if a factor of 0.25 is chosen, the number
of data points is decreased by a factor of 4, i.e. four frequency intervals are merged into one wavelength interval. Consequently,
spectral information is lost. Interpolation is useful to reduce the noise or to free some memory before memoryconsuming
calculations such as 3DFourier self deconvolution (3DFSD) are carried out.

The 'interpolate' function overwrites all existing data blocks (see also
Internal Data Organization, Table III).
The parameter used for interpolate are stored within the program workspace and are accessible through
File Info menu (File Info → File Manipulations →
type of data block). These parameters are also shown in the command line window.


Smoothing: This function is used to smooth spectra, using either the SavitzkyGolay, or the average smoothing algorithm. Possible
values for smoothing points are 5 to 25. Select the source data block as usual, choose the number of smoothing points and click the 'smooth'
button to start the operation. Smoothing has a mostly cosmetic effect on the spectrum, reducing the noise at the expense of distorting the
signals.
Details of the SavitzkyGolay algorithm can be found in the literature:
A. Savitzky and M. Golay. Smoothing and
Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964 Vol 36(8):1627.
The parameters used for smoothing are stored within the program workspace and are accessible through
File Info menu (File Info → File Manipulations → type of data block). These parameters are
also shown in the command line window.

TR ↔ ABS conversion: This function performs the conversion from transmission spectra to absorbance spectra and vice versa.
Note: The function ABS ↔ TR acts on the complete data block of original spectra and overwrites the data block of original spectra.
Furthermore, all other types of data will be deleted.
For converting absorbance spectra to transmission spectra the following formula is used:
Formula, used to obtain absorbance spectra from transmission spectra:

Subtraction: This function permits subtraction of spectra from complete spectral data blocks. The function might be useful to
compensate for spectral contributions of supporting substrates in transmission type imaging data (example: subtraction of absorbance
spectra of thin films).
Spectral subtraction can be carried in two ways: by an internal, or an external spectrum:
 Internal spectrum  a spectrum that is contained in the actual spectral map.
 External spectrum  the spectrum can be loaded (ASCII format).
Using external spectrum for subtraction: in order to use this function check the appropriate radiobutton. Load the external
ASCII spectrum (details of data format are given below). Type in the scaling factor and select the source data block. After pressing
the 'subtract' button the external spectrum will be multiplied by the scaling factor and the resulting spectrum is subsequently
subtracted from all spectra of the source data block. .
Using internal spectrum for subtraction: check the radiobutton 'use internal spectrum' and choose the (x,y) pixel positions
(coordinates) of the spectrum you wish to subtract from the map. Note that upon its initialization the 'subtract' window will
read the actual (x,y) coordinates from the main gui. Press the 'subtract' button after selecting the source data block.
Source block: Here you can choose the type of the source block for the subtraction function. Please note that 3DFSD data cannot
be used as source data.
Target block: If the source data blocks are of the type original or preprocessed, the target data block will be of the type of
preprocessed data. If the source data are of the type derivative the target data block will be also of this type (existing data are
overwritten without warning, see also Internal Data Organization).
The spectral subtraction routine is always carried out on the complete 3D spectral data block.
Button load spectrum: Permits to load a double column ASCII spectrum. If the file could be successfully loaded the directory
and the file name is displayed and the button 'subtract' becomes activated.
Button subtract: Starts the subtraction routine immediately.
Button cancel: The routine is aborted.
Note: In order to be able to subtract an external spectrum one have first to produce a double column ASCII spectrum (for
details of the data format see spectra vap_cut.dat or wap_full.dat; both spectra can be found in the directory
CytoSpecRootDir/Testdata/watervap/). Upon loading the external spectrum is automatically adapted such that its data point spacing
and its frequency range fits that of the sample data:
It will be interpolated (alternative point spacing), cut (broader frequency range), and/or extrapolated (narrower range).
Extrapolation is achieved by using the closest absorbance value to fill missing data points.

Dispersion correction: This function performs the correction of dispersion artefacts from transmittance spectra (function
written by Dr. Melissa Romeo).
The correction of dispersion artefacts is carried out in the following way:
 Transmittance spectra are linearly interpolated such that they contain 2^n data points (256, 512, ... , 16384)
 These spectra are then Fouriertransformed.
 The first half of the Fourier transformed function is inversely Fouriertransformed. This operation produces a complex
function containing a real and an imaginary part.
 Negative terms in the imaginary component are compensated.
 Squares of the real and imaginary terms are taken.
 The squared terms are coadded.
 The root of the sum yields a phasecorrected spectrum.
 Transmittance spectra are backinterpolated and converted into absorbance spectra. They are stored in the data block of
preprocessed data.

Quality Test: The function 'quality test' implemented in the CytoSpec software comprises five distinct checks for spectral
quality:
 a test for spectral signs of water vapor
 the check for sample thickness (integrated intensity)
 the test of the spectral signaltonoiseratio
 a check called 'test for an additional band'
 a 'bad pixel' test (a tool to eliminate spectra from dead pixels of focal plane array detectors
Data organisation: the quality tests are performed exclusively on the data block of original spectra. Spectra that have passed
the tests are copied without modifications into the data block of preprocessed spectra. Note that existing data of this block are
overwritten without warning. If the quality test of a given spectrum is negative, the respective field in the preprocessed data
block is replaced by NaN (Not a Number). In this way, spectra tested for poor quality are excluded from further evaluations and will
appear in the hyperspectral images as black areas.
If you wish to perform a quality test on preprocessed spectra, for example a sample thickness test after baseline correction, you have
to use the Swap Data Block function of the 'Tools' menu bar. This
function enables you to overwrite the data block of original spectra by preprocessed spectra.
To enable a test, check the appropriate checkbox and specify the quality test parameters such as absorbance thresholds. Press the
'test' button to start the quality test or hit 'cancel' if the test should be canceled. The parameters of the test for spectral
quality and details of the test results can be found in the File Info
menu (File Info → File Manipulations → preprocessed). These parameters are also displayed in the command line window.
1. Test for water vapor:
Sharp water vapor absorption bands can be found in the spectral region between 1300 and 1800 1/cm, a region where many biomaterials
exhibit also strong absorption bands. It is therefore recommended to use water vapor bands above 1750 1/cm for testing. Indicate the
precise positions of two water vapor bands which should be utilized for testing and define an absorption threshold criterion. If
the absorption of one of the bands is higher than the specified criterion, the test result for the given spectrum will be negative,
and the spectrum will be eliminated.
2. Integral absorption as a measure for sample thickness:
The absorbance, integrated over a large spectral region, can be used as a rough measure of sample thickness in transmission type
measurements. As many multivariate imaging techniques such as HCA or ANN imaging require a consistent level of the SNR throughout
the map, spectra with too low absorptions have to be excluded from further multivariate analysis. On the other hand you may want to
eliminate also spectra showing intense signals. This could be the case where the BeerLambert law is not obeyed (total absorption,
nonlinear detector response, etc.)
In order to apply the 'sample thickness' criterion indicate the spectral region to be used for obtaining the integral. Next,
define a upper and a lower threshold for the integral (edit field lower/upper limit). Check the appropriate checkbox to enable the
test. A spectrum has failed the sample thickness test if an integration value is determined which is higher or lower than the
defined thresholds.
3. Signal/noise ratio (SNR):
This test allows the signalnoiseratio for individual spectra to be calculated, and to eliminate those that do not fulfill a
threshold SNR ratio. Indicate the spectral regions to be used for defining the noise and signal, respectively. For biomedical
samples, it is recommended to obtain the signal in the amide I region (1600  1700 1/cm) and the noise in the region between
18001900 1/cm. Also indicate the SNR threshold and check the checkbox for the SNR test. Spectra are rejected if the SNR is
lower than the threshold.
Noise: the standard deviation in the defined spectral range:
Signal: the maximum ordinate value in the defined wavenumber range<
4. Test for an additional band:
This test is useful to exclude spectra from the data set that contain an artifact band (example: regions of a tissue section
contaminated by tissue embedding medium). Indicate a typical band position (carbonyl esters of tissue freezing medium: 1746 1/cm)
and an absorbance threshold (edit field criterion). Spectra with a higher absorbance at this frequency will be eliminated.
5. Elimination of 'bad' pixel from FPA data:
Most of the focal plane array (FPA) detectors have socalled 'dead pixels', i.e. detector elements with zero response to
IR radiation. The spectral information at these FPA elements is usually replaced by the camera software with interpolated data
from pixel neighbors. If you wish to remove interpolated spectra from the data set, you have to create a simple text file, which
should contain the dead pixel (x,y) positions. The text file can be loaded by activating the appropriate check box. Spectra at the
given positions are then replaced by NaNs (not a number), i.e. excluded from all subsequent calculations.
Please note: Please use the function Define Spectral Regions to
define sample areas in which spectra should be excluded from further analyses.


source block: please select a data block you wish to correct (normalize) by the linear baseline (offset) correction
routine.
spectral region: the spectral region, in which the baseline function is searching for a minimum yvalue of the spectrum
which is subtracted from the spectrum.
norm: clicking on the 'norm' button corrects the baseline.
cancel: closes the application.

2. SavitzkyGolay baseline correction: This function can be used to automatically compensate for baseline effects, for instance as
a result of scattering. As it is illustrated in the figures below, spectral baseline curves are generated by SavitzkyGolay filtering using
a very high number of smoothing points (up to 999).
Baseline corrected spectra are obtained by subtracting the baselines from the original spectra.
Baseline correction can be carried out on original (absorbance/transmittance/Raman intensity) spectra and preprocessed spectra. Please
note, that in the latter case existing data are overwritten without warning. Details of CytoSpec's internal data organization can be found
in the respective chapter of the CytoSpec online help ( Internal
Data Organization).

source block: please select a data block you wish to compensate for nonlinear baseline effects.
number of smoothing points: number of smoothing points used for SavitzkyGolay smoothing.
interpolate spectral region: a spectral region, in which the slope of the baseline should be interpolated. To activate
this feature you have to check the appropriate checkbox and to indicate the wavenumber values of the spectral region you wish
to exclude from baseline calculation (in biomedical spectroscopy, this may be the amide I and II region: 15201700 1/cm).
correct: the baseline correction procedure is initiated.
cancel: closes the application.

Example: The figure below exemplary illustrates how the algorithm of SavitzkyGolay baseline correction works.
red spectra: original FTIR absorbance spectra.
blue spectra: baseline curves as obtained by integration of extensively smoothed spectra . The left part of the figure
shows baselines obtained with 99 smoothing points and the right panel with 249 points (resolution in the original spectra: 8 1/cm;
zerofillingfactor of 4; data point spacing: 2). The example to the right demonstrates additionally the effect of the option
'interpolate region' which was used to interpolate the baseline in the amide I and II regions (1520  1700 1/cm).
black spectra: red (original) minus blue (baseline) spectra. These spectra are stored in the data block of preprocessed
spectra.
Note: due to the of SavitzkyGolay algorithm, baseline correction might be ineffective in regions close to the upper and
lower wavenumber limits (UWN, LWN), particularly if a high number of smoothing points have been chosen. If the number of smoothing
points is NOP and the data point spacing is DPS, the baseline correction routine will perform a linear extrapolation of the baseline
in the spectral regions
[UWN]  [UWN(NOP1)/2*DPS] and [LWN] + [LWN(NOP1)/2*DPS].
3. Baseline correction from curve minima: The function divides the spectrum in segments, or intervals in which minimum yvalues
(absorbance, Raman intensities) are obtained. These yvalues are in the following used to generate a baseline correction curve
(by shapepreserving piecewise cubic interpolation) which is subtracted from the original spectrum.

source block: please select a data block you wish to compensate for nonlinear baseline effects. Note that this
function does not work on derivative or 3D FSD data.
number of intervals: number of intervals in which the spectrum is divided.
interpolate spectral region: a spectral region, in which the algorithm should not search for baseline points.
If this option is activated you are able to enter the wavenumber values of this spectral region.
correct: the baseline correction procedure is initiated.
cancel: closes the application.

4. Polynomial baseline correction: This function can be used to subtract a baseline from spectra. The baseline function is a
nth order polynom, which is obtained from a set of baseline points that can be defined either automatically, or manually.
source block: please select a data block you wish to compensate for nonlinear baseline effects. Note that this function does
not work on derivative or 3D FSD data.
polynom order: order of the polynom. Valid values are 210. Please try to avoid highorder polynoms.
number of baseline points: select here up 212 points which are used to obtain the polynomial baseline function. Note that
the number of points should be larger than the order of the polynom.
select spectrum: the windows to the right display normally the original spectrum with the actual baseline function (upper
panel) and the corrected spectrum in the lower panel. The spectrum is read upon initialization of the polynomial baseline function
from the main window. If you wish to check the effect of baseline correction on alternative spectra you can increase/decrease the
coordinates of the actual test spectrum by pressing one of the four buttons of this panel. The actual pixel spectrum coordinates
are displayed in the fields 'actual pixel coord.'
interpolate spectral region: a spectral region, in which the algorithm should not search for baseline points. If this option
is activated you are able to enter the wavenumber values of this spectral region.
baseline points, manual mode: allows to manually modify the position of baseline points. Check this checkbox to activate the
manual definition mode. If checked one can define baseline points either by mouseclicks in the upper central panel (shows the
original spectrum and the polynomial baseline) or by entering the wavenumber/wavelength values directly in the appropriate edit
fields to the right. Note that the field marked by the yellow color will be updated by the next mouse action.
NOTE: each time when the popupmenus 'polynom order' and 'number of baseline points' are modified the baseline
correction function updates all baseline points by a predefined algorithm. Baseline points defined earlier may be lost.
xbuttons: when one of these buttons is pressed the respective baseline point is deleted (only possible in the manual
mode of baseline point definition).
correct: starts the polynomial baseline correction procedure.
cancel: closes the application.
5. Baseline correction by asymmetric least squares: New function introduced with CytoSpec version 2.00.05. To be completed.

Water vapor compensation: This function permits to automatically subtract a water vapor spectrum from the measurement data
such that the spectral effects of water vapor are minimized.
The water vapor correction routine works as follows:
 A second derivative spectrum of a pure water vapor absorbance spectrum is obtained.
 Then, a second derivative spectrum is calculated from the sample spectrum.
 Depending on your selection, up to 4 separate yvalues at defined spectral positions are obtained for both derivative
spectra.
 The water vapor correction factor is calculated by dividing the respective yvalues of the water vapor and the sample spectrum.
If more than one yvalue was selected, the final water correction factor is the average of the ratios.
 Finally, the sample data are corrected by subtracting the original water vapor spectrum, which was weighted by the water vapor
correction factor.
water vapor correction of derivative spectra: If you wish to perform water vapor compensation on derivative spectra, you have
to make sure that spectra are 2nd derivative spectra and that derivative calculations are carried out by choosing 5 smoothing points
in the SavitzkyGolay algorithm. The algorithm described above will not work if these two preconditions are not fulfilled.
number of vapor bands: Please choose the number of water vapor bands on which the spectral compensation for water vapor bands
should be carried out.
edit fields 14: Enter the correct positions (in wavenumbers) of water vapor bands. Please note that the band positions may
slightly differ from instrument to instrument (calibration) and also as a function of the temperature.
Source block: Here you can choose the type of the source block for water vapor compensation.
load vapor file: Permits to load a double column ASCII water vapor spectrum. If the file could be successfully loaded the
directory and the file name are displayed and the button 'correct' becomes activated.
correct: Starts the spectral water vapor correction routine.
cancel: The routine is aborted.
data organization (source and target data blocks): Any type of data blocks (except 3DFSD data) can be handled (including
also derivatives). If the source block is of type of original spectra, or preprocessed spectra, the data are stored in the data
block of preprocessed spectra. If this block is not empty the data are overwritten. Water vapor compensated derivative spectra
are stored in the data block of derivative spectra (existing data are also overwritten without warning, see also
Internal Data Organization, Table II). The water compensation
is always carried out on the complete 3D spectral data block.
Please note: In order to spectrally compensate for water vapor one have first to produce a double column ASCII spectrum of water
vapor (for details of the data format see spectra vap_cut.dat or wap_full.dat; both spectra can be found in the
directory CytoSpecRootDir/Testdata/watervap/.
Upon loading the external spectrum is automatically adapted such that its data point spacing and its frequency range fits that
of the sample data:
 It will be interpolated (if the point spacing is different), cut (broader frequency range), and/or extrapolated
(narrower range).
 Extrapolation is achieved by using the closest absorbance value to fill missing data points.
In the water vapor testdata directory (CytoSpecRootDir/Testdata/watervap/) one can find a test file named 'watervap.mat'.
The first data block of this file (original data) contains the original absorbance spectra. Water vapor corrected IR absorbance
spectra are found in the second data block of preprocessed spectra. Original spectra are corrected by using the file 'vap_full.dat'.

PCA based noise reduction: This function can be used to reduce spectral noise. Noise is eliminated by performing principal component
analysis (PCA) of the image data and reassembling of spectra on the basis of a selection of principal components (low order PCs) . In this
way, higherorder principal components that are supposed to contain mainly 'noise' are omitted.
PCA based noise reduction can be carried out on the basis of original or preprocessed data sets. The target data block will be always the
data block of preprocessed data.
Important: Please carefully use this preprocessing routine! The decision which of the PCs can be omitted is
highly subjective and may cause spectral artifacts.
The algorithm has been adapted from a suggestion of Dr. Spragg R. (PerkinElmer) "Addressing Problems in Data Reduction for FTIR Images
of Biological Samples" (Oral Contribution). RISBM  Raman and IR spectroscopy in Biological Medicine. Feb 29Mar 02, 2004.
FriedrichSchillerUniversity, Jena, Germany.

Cosmic spike removal: The cosmic spike removal tool allows the user to remove cosmic ray features from the spectral (Raman) data.
The function can be chosen from the 'Spectral preprocessing' menu bar. When this function is activated a dialog box shows up which
allows the user to change parameters of the cosmic spike removal filter.
With this filter the removal of cosmic spikes is carried out in the following way:
 Smoothing of the spectral data of choice (original or preprocessed) in the spectral dimension by a 7point SavitzkyGolay
algorithm.
 A 3Darray of difference values between the unsmoothed and smoothed data is obtained.
 This array of difference values is normalized by dividing it by the mean standard deviation of the complete array.
 Cosmic spikes are now obtained by a systematic analysis in the spatial domains. For this purpose, maxima are obtained in each of
the image planes of the normalized array of spectral differences. In this context, the parameter 'sensitivity' of the cosmic
spike removal dialog box is used to define a threshold above which Raman intensity difference value supposedly indicate the
presence of cosmic rays. The higher the sensitivity the lower this threshold.
 In the next step, the spatial coordinates of spectra with cosmic spike candidates are determined. Cosmic spikes are excised by
replacing them with Raman intensities from neighboring frequencies (spectral domain). The parameter 'spikes width' of the dialog box
defines the width of the excised spike in points.
 The cosmic spike removal tool can be applied to data block of original or preprocessed data. In both cases the spikecorrected
Raman data are written into the data block of preprocessed spectra. Note that existing preprocessed data are overwritten without
warning.

source block: please select the type of data block you wish to correct
sensitivity: used to define a threshold above which Raman intensity difference values supposedly indicate cosmic
spikes. The higher the sensitivity the lower the threshold.
spikes width: defines the width of the excised spike in data points.
verbose mode: displays more details of spike removal function.
remove: starts the cosmic spike filter on the data block of choice.
cancel: closes the dialog box.


Batch preprocessing: This function permits automated preprocessing of hyperspectral data. When this option is chosen one will
be asked to indicate a predefined macro
f ile (*.cbt CytoSpec batch) which should be generated (and tested) before. CytoSpec batch files can be prepared
by simple text editors like Wordpad, or
Notepad. It is important to store the *.cbt file in a simple text format. Do not use special characters or format tags!.
CytoSpec's batch processing files contain different sections, also called blocks. Each block starts with one of the following
(capitalized) threeletter codes:
DER  Derivative
NRM  Normalize
CUT  Cut / Crop
INT  Interpolate
SMO  Smoothing
ATR  ABS → TR conversion
TRA  TR → ABS conversion
QAL  Quality tests
BAS  Baseline correction (SavGol)
BMI  Baseline correction from minima
ALS  Baseline correction by asymmetric least squares
LBS  Subtract linear baseline
WVC  Water vapor correction
SWA  Swap data blocks
CSR  Cosmic ray correction
PNR  PCAbased noise reduction
EPD  Edgepreserving denoising
FLT  Filter images
FSD  3D Fourier selfdeconvolution
Most of the blocks contain a number of parameters required for preprocessing hyperspectral imaging data (such as type of source or target
datablock, wavenumber regions, etc.). These parameters are mandatory and must be indicated by a sequence of a three letter code followed
by a numeric value and a space character for separation. It is important to note also the comments given after the '#' character at each
line. These comments contain descriptions of the preprocessing parameters and provide allowed selection values of these parameters
(usually in the following format: [5791113151719212325]). Note also that each of the blocks must be terminated by a line
containing the code 'END'.
IMPORTANT: The sequence of preprocessing steps is given by the sequence of blocks in the batch file. To omit preprocessing functions,
it is sufficient to comment out the respective block by setting the '#' character (number sign, or hash sign) at the first position of
the line containing the threeletter block code. Please refer also to the online help or to the example file that comes with CytoSpec's
installation CD / USB drive.
Example of the block 'CUT' in a CytoSpec batch (*.cbt) file:
#  CUT 
CUT
TYP 1 # type of cutting (1spectral, 2 spatial dimension)
WV1 1000 # first wavenumber for cut in spectral dimension
WV2 1800 # last wavenumber (WV2 larger than WV2!)
XD1 1 # cut, spatial dimension x : first pixel to keep
XD2 10 # cut, spatial dimension x : last pixel to keep
YD1 1 # cut, spatial dimension y : first pixel to keep
YD2 10 # cut, spatial dimension y : last pixel to keep
END
# some lines with comments may follow
next block ...
A detailed example of a CytoSpec batch file is given here: preproc.cbt
