Mean centering 
The selected data items (layers, spectra, or all data) are scaled in such a way that the mean of each item becomes zero. 
Standardisation 
The selected data items are scaled to a zero mean and a standard deviation of 1.0. Please note that the standardisation of data may be problematic if the data contains unusually large outliers. In this case qnormalisation is a better alternative. Note: the standardisation applied to spectra is also called "standard normal variate" (SNV).

Constant sum 
The selected items are scaled to a constant sum defined by the parameter A. 
Constant sum of squares 
The selected items are scaled to a constant sum of squares. The sum is specified by the parameter A. 
Maximum amplitude 
The selected items are scaled in such a way that the maximum absolute value of each item becomes A. 
Range 
The selected data values are scaled to cover a range between A and B. 
Q Normalisation 
The selected data range is scaled to zero median and a difference between the median and the qpercentile of 1.0, with q (in %) given by the parameter A. Q normalisation is largely insensitive to outliers and should be used whenever you are expecting severe outliers. 
Squashing Function 
The selected data range is compressed by applying a sigmoid function ("squashing function") to the interval [1,+1]. The parameter A specifies the origin (offset) of the squashing function, the parameter B defines the slope of the function.
