In line with the conventional fitting, G3F implements concepts of subranges and data masks in each of three fitted dimensions. In addition to the layer-uniform row and column variables, G3F allows using layer-specific local fitted variables, LayRow and La圜olumn. Fitted data can be a 2D (columns) or 3D (layers) matrix with each dimension described by its own independent variable (calibration) and an optional set of zero or more fitted parametric vectors (row, column, and layer local variables). G3F handles folding of complex global and local data into a form suitable for the built-in engine. G3F uses IgorPro’s internal non-linear regression engine to recursively minimize residual error. In this case, analysis involved two orthogonal parametric vectors (frequency-dependent amplitudes vs. In more recent studies, it was used to obtain completely unknown difference spectra of a redox transition with an unknown potential over a variable polynomial background ( John & Proshlyakov, 2019 John, Swain, Hausinger, & Proshlyakov, 2019). A similar approach was later used in the analysis of Raman spectra of methane monooxygenase ( Banerjee, Proshlyakov, Lipscomb, & Proshlyakov, 2015). Also included were polynomial baselines, which varied between spectra. time 2D dataset with uniform properties of predictable vibrational bandshapes (global) and unknown speciation plots (local to each time point) allowed resolution of superimposed vibrations of two different species while improving resolution via signal sharing between spectra. The G3F package for IgorPro was initially developed to simultaneously analyze vibrational spectra of multiple isotopomers with overlapping modes in time-resolved Raman studies on enzyme TauD, where simple improvement of signal via time averaging was not possible ( Grzyska, Appelman, Hausinger, & Proshlyakov, 2010).
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