Multiplicative scattering correction

Artifact removal and/or linearization. A common form of artifact removal is baseline correction of a spectrum or chromatogram. Common linearizations are the conversion of spectral transmittance into spectral absorbance and the multiplicative scatter correction for diffuse reflectance spectra. We must be very careful when attempting to remove artifacts. If we do not remove them correctly, we can actually introduce other artifacts that are worse than the ones we are trying to remove. But, for every artifact that we can correctly remove from the data, we make available additional degrees-of-freedom that the model can use to fit the relationship between the concentrations and the absorbances. This translates into greater precision and robustness of the calibration. Thus, if we can do it properly, it is always better to remove an artifact than to rely on the calibration to fit it. Similar reasoning applies to data linearization. 

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. 

We would like to thank the beamline scientists of ID 15 of the ESRF. T. Buslaps, V. Honkimaki, A. Shukla and P. Suortti for their valuable help with the measurements and F. Maniawski and J. Kwiatkowska for their help with the sample preparation. We are also grateful to J. Felsteiner for sending us his Monte-Carlo simulation program for the multiple scattering correction. 

Helland, I. S., Naes, T., Isaksson, T. Chemom. Intell. Lab. Syst. 29, 1995, 233-241. Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data. 

MSC multiplicative scatter correction, bootstrap error-adjusted single-... 

Figure 3.21. Near-infrared reflectance spectra of solid polymer samples before a) and after (b) preprocessing using multiplicative scatter correction.

Martin et al. [102] reported a study in which LIBS was applied for the first time to wood-based materials where preservatives containing metals had to be determined. They applied PLS-1 block and PLS-2 block (because of the interdependence of the analytes) to multiplicative scattered-corrected data (a data pretreatment option of most use when diffuse radiation is employed to obtain spectra). They authors studied the loadings of a PCA decomposition to identify the main chemical features that grouped samples. Unfortunately, they did not extend the study to the PLS factors. However, they analysed the regression coefficients to determine the most important variables for some predictive models. 

NIR of the PE powder was carried out before compounding with Irganox 1010 and Irgafos 168. It was observed that the identification and selection of specific bands or unique spectral features in the spectra is difficult. The variation in baselines is due to differences in scattering properties of the analytes. Multiplicative scattering correction (MSC) or derivation can eliminate these variations [117, 118]. 

Compared to spectra obtained in the mid-infrared region, NIR spectra contain fewer, less resolved, peaks. Due to scattering and other effects, a set of NIR spectra on similar samples often exhibits constant baseline offsets from one to the next. To eliminate these baseline offset differences, reduce (but not eliminate) scattering effects, and increase the resolution of neighboring peaks, first- or second-deriva-tization is often applied to NIR spectra prior to their use in calculations. Other preprocessing techniques, such as standard normal variate (SNV) or multiplicative scatter correction (MSC), may be applied to more effectively reduce scattering effects that arise from particle size differences among samples.36... 

Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transforms... 

Two closely related methods — multiplicative scatter correction (MSC) [9] and standard normal variate (SNV) transforms [10] — are discussed in this section. MSC... 

The MSC has been shown to work well in several empirical studies [9, 10], which showed an improvement in the performance of multivariate calibrations and a reduction in the number of factors in PCA. For example, NIR reflectance spectra of 20 powder samples of microcrystalline cellulose are shown in Figure 4.8a. Due to differences in particles size from sample to sample, there are significantly different baseline offsets. The same spectra are shown in Figure 4.8b after multiplicative scatter correction. The different baseline offsets observed in Figure 4.8a are so large that they mask important differences in the water content of these samples. These differences are revealed in the water absorption band at 1940 nm after the baseline offsets have been removed by MSC. 

FIGURE 4.8 Illustration of multiplicative scatter correction (MSC). (a) NIR reflectance spectra of 20 powdered samples of microcrystalline cellulose, (b) Same NIR reflectance spectra after multiplicative scatter correction, revealing differences in moisture content. 

Because a diffuse reflectance spectrum is scatter-dependent, information on mean particle sizes is also obtainable, which is a parameter of great importance in powder technology. An approach using near-infrared spectrometry combined with multivariate calibration has been presented by Ilary, Martens, and Isaksson. Included was a spectrum standardization by multiplicative scatter correction (see later). 

Figure 2 Multiplicative scatter corrected near-IR spectra of suspension-layered pellets and individual ingredients diltiazem HC1 (dash-dot trace), non-pareil seeds (dot trace), and suspension-layered pellets containing 80-100% of theoretical potency (solid traces).

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