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Revision as of 15:35, 17 December 2018 by imported>Scott (Created page with "===Purpose=== Wrapper for baselining functions. ===Synopsis=== :[baselined_data,baselines] = baselineds(spec,options); %Calibrate and apply. : spec = baselineds(baselined_d...")
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Wrapper for baselining functions.


[baselined_data,baselines] = baselineds(spec,options); %Calibrate and apply.
spec = baselineds(baselined_data,baselines); %Undo


Wrapper for baselining functions.


  • spec = M by N matrix of data to be baslined (class "double" or "dataset").


options = a structure array with the following fields:

  • plots : [ {'none'} | 'final' ] governs plotting.
  • algorithm : [ {'wlsbaseline'} | 'baseline' | 'whittaker' | 'datafit']
wlsbaseline - Baseline subtraction using iterative asymmetric least squares algorithm.
baseline - Subtracts a polynomial baseline offset from spectra.
whittaker - Baseline subtraction using Whittaker filter.
datafit - Asymmetric least squares baselining.
  • mode: [ 1 ] dimension of data on which to calculate the minima and maxima for scaling. 1 = over rows (each row will have range [0,1]); 2 = over columns (each column will have range [0,1]). Default is 1.


  • xcorr = the scaled data (xcorr will be the same class as x)
  • mins = vector of minima for each row (or column)
  • maxs = vector of maxima for each row (or column)

See Also

normaliz, preprocess, snv