MCR AR
MCR-AR is a commercially available extension, that features a peak detection and deconvolution based on Multivariate Curve Resolution with Alternating Regression.
MCR-AR uses as input data segments of 50 to 500 scans. Several methods to automatically determine these analysis segments are available.
The data for each analysis segment is then preprocessed with background removal, linear baseline correction and removal of mass channels that contain only noise. The latter can be adjusted through the Channel Noise Level setting. Valid values for this setting are between 0 and 10 where 0 will not remove any m/z channel.
Prior to starting the main loop, a QR decomposition of the data matrix is run to determine the maximum number of peaks that should be looked for.
The first step in the main loop of the MCR-AR algorithm is the determination of the pure spectra. In each full round of the main loop, one additional pure spectra is determined until the maximum number of peaks is reached, or in the later steps, drop out conditions occur. The drop out conditions will be explained later on. The actual algorithm to determine pure spectra is called SIMPLISMA and was published 1991 in the Journal of Analytical Chemistry by Windig and Guilment.
SIMPLISMA has a setting to reduce the influence from pure spectra that are close to the noise level. The setting Pure Noise Percent can be varied between 0.001 and 1. Small values will increase the possibility of pure components close to the noise level being proposed to integrate in the model. The effect of this depends on actual property of the data: In some cases, a small setting will allow a noisy but correct component to be integrated. In other cases, the potential component was noise and will be detected as such in the latter part of the MCR-AR algorithm.
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