Multiplicative scatter correction reference spectrum

Plugge and van der Vlies have discussed the conformity index (Cl) for NIR analysis of ampicillin trihydrate [12,13]. The Cl is a metric used to determine the degree of conformity of a sample or batch with standards of known and acceptable quality. To use the Cl, reference spectra are first collected and baseline-corrected using a second-derivative or multiplicative scatter correction (MSC) spectrum. At every wavelength across the spectrum, the average absorbance and standard deviation are calculated for the baseline-corrected reference spectra, resulting in an average spectrum and a standard deviation spectrum. 

Another popular form of data pre-processing with near-infrared data is the application of the Multiplicative Scatter Correction (MSC, [28]). It is well known that particle size distribution of non-homogeneous powders has an overall effect on the spectrum, raising all intensities as the average particle size increases. Individual spectra x, are approximated by a general offset plus a multiple of a reference spectrum, z. 

Two alternate approaches to normalization which have been used often with infrared spectroscopy are multiplicative scatter correction (MSC) [19], which uses a target reference spectrum (often the mean of the calibration dataset) for normalization, and standard normal variant (SNV) [27-29], which centers and normalizes to an expected variance. These have also been used in Raman spectroscopy with impressive results [29-31]. 

The simplest method which corresponds to these terms is multiplicative scatter correction (MSC). In this case, it is assumed that chemical variation is small compared to physical variation (i.e. variation introducing a constant (additive)/proportional (multiplicative) baseline effect) and thus the true signal may be replaced by a constant reference signal, usually the mean (or median) spectmm, m (it may also be a specific spectrum of the data set). The previous equation becomes... 

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