Data analysis filtering

Figure 9. Data reduction and data analysis in EXAFS spectroscopy. (A) EXAFS spectrum x(k) versus k after background removal. (B) The solid curve is the weighted EXAFS spectrum k3x(k) versus k (after multiplying (k) by k3). The dashed curve represents an attempt to fit the data with a two-distance model by the curve-fitting (CF) technique. (C) Fourier transformation (FT) of the weighted EXAFS spectrum in momentum (k) space into the radial distribution function p3(r ) versus r in distance space. The dashed curve is the window function used to filter the major peak in Fourier filtering (FF). (D) Fourier-filtered EXAFS spectrum k3x (k) versus k (solid curve) of the major peak in (C) after back-transforming into k space. The dashed curve attempts to fit the filtered data with a single-distance model. (From Ref. 25, with permission.)...

It is sometimes difficult to totally remove (by the emission monochromator and appropriate filters) the light scattered by turbid solutions or solid samples. A subtraction algorithm can then be used in the data analysis to remove the light scattering contribution. 

An Intercomparison study of trace element determinations In simulated and real air particulate samples has been published by Camp, Van Lehn, Rhodes, and Pradzynskl ( ). This Involved twenty-two different laboratories reporting up to thirteen elements per sample. The simulated samples consisted of dried solution deposits of ten elements on Mllllpore cellulose membrane filters. In our data analysis a set of energy dispersive X-ray emission results restricted to eight laboratories reporting six elements (V, Cr, Mn, Fe, Zn, Cd) was... 

Figure 3. X-ray absorption data analysis of Fe EXAFS data for Rieske-like Fe S cluster. A) EXAFS data B) Fourier transform of EXAFS data showing peaks for Fe—S and Fe-Fe scattering. Thin vertical lines indicate filter windows for first and second shell.

Lima, A. (2008). Evaluation of geochemical background at regional and local scales by fractal filtering technique Case studies in selected Italian areas. In Environmental geochemistry Site characterization, Data analysis, Case histories (B. De Vivo, H. E. Belkin, and A. Lima, eds.). Elsevier (this volume). 

The PLS multivariate data analysis of the training set was carried out on the descriptors matrix to correlate the complete set of variables with the activity data. From a total of 710 variables, 559 active variables remained after filtering descriptors with no variability by the ALMOND program. The PLS analysis resulted in four latent variables (LVs) with / = 0.76. The cross validation of the model using the leave-one-out (LOO) method yielded values of 0.72. As shown in Table 9.2, the GRIND descriptors 11-36, 44-49, 12-28, 13-42, 14-46, 24-46 and 34-45 were found to correlate with the inhibition activity in terms of high coefficients. 

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