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• W2559277780 abstract "Errors in determining peak parameters of the huge sets of Gaussian doublets using nonlinear least squares curve fitting have been evaluated. Doublets were perturbed by normal noise (additive and proportional) and by constant background. Fitting of original spectrum (the normal mode) and of its derivative (the derivative mode) was performed using Gauss-Newton with Levenberg-Marquardt modifications algorithm. The probability that the relative error in estimating each model parameter is not greater than a priory given limit for a given fitting error has been calculated. It was shown that using the derivative mode for curve fitting has advantage over the normal mode only in the case of non-modelled background. We found that the errors in determining maximum peak positions of doublet are the most sensitive to the noise. Increasing of the additive noise causes increasing of the relative number of the good-fitting models both in the normal and the derivative modes; however, the probability decreases." @default.
• W2559277780 created "2016-12-08" @default.
• W2559277780 creator A5029033313 @default.
• W2559277780 date "2015-01-01" @default.
• W2559277780 modified "2023-09-27" @default.
• W2559277780 title "Error Analysis in Determining Parameters of Overlapping Peaks Using Separation of Complex Spectra into Individual Components. Case Study: Doublets" @default.
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