SDPREP - data preprocessing and feature computationP=SDPREP(DATA,METHOD,OPTIONS) INPUTS DATA - data set METHOD - method name OUTPUTS P - preprocessing pipeline METHODS 'submean' - subtract sample mean 'divsum' - divide by sum (for each sample) 'divmean' - divide by mean (for each sample) 'divband',B - divide by value of specific feature (band B) for each sample 'smooth' - smooth 1D spectra (default sigma=1.0) 'sigma',S - smoothing parameter (neighborhood defined by +/- 3*sigma) 'der' - apply Gaussian derivative filter (def: sigma=1) 'kernel',K - convolve 1D spectra with user-defined kernel 'dark-white',D,W - perform dark/white correction by provided single spectra of dark current and white background Computations based on specific features (bands). A,B are feature indices. 'a-b',A,B 'a/b',A,B '(a-b)/(a+b)',A,B '(a+b)/(a-b)',A,B 'a/(b*c)',A,B,C 'a/(b-c)',A,B,C 'add' - copy all input features and add the computed feature to the end EXAMPLES >> p=sdprep(data,'der','sigma',2); data2=data*p Add spectral index: >> p=sdprep(data,'(a-b)/(a+b)',53,107,'add'); data2=data*p