spectra via Monte Carlo peak factor estimation (original) (raw)
Related papers
A statistical study of the analysis of congested spectra by total spectrum fitting
Journal of Molecular Spectroscopy, 2004
Heavily overlapped, or congested spectra often display much structure but few individual ''lines.'' Methods have been devised for analyzing such spectra through nonlinear least-squares fitting of the intensity as a function of wavelength or wavenumber. Such total spectrum fitting (TSF) methods are examined statistically for a simple diatomic model and compared with the standard ''measure-assign-fit'' (MAF) approach in use since the dawn of spectroscopy. Monte Carlo computations on typically 1000 synthetic spectra at a time verify that the predictions of the variance-covariance matrix are reliable under many circumstances. However in regions where the P and R branches double up, the predicted standard errors in the key spectroscopic constants rise sharply and greatly exceed estimates from the Monte Carlo ensemble statistics. In the same regions, the MAF method actually gives better precision. However, for imperfectly overlapped R and P branches, the MAF standard errors are typically three times larger than the TSF values; moreover, the MAF statistical errors are dwarfed by bias. The TSF approach, while clearly superior in these tests, has a practical drawback: it, too, can give significant bias if the spectra are analyzed with an incorrect model, as illustrated here through calculations employing the wrong function to describe the spectral lineshape.
2014
Development and in situ application of stir bar sorptive extraction for the determination of agricultural pesticides in surface water Summary Passive sampling has recently been developed as an alternative to grab or average automated sampling, in order to obtain at lower cost, more realistic estimates of the average concentrations of organic contaminants in surface waters. The aim of this study was to develop and validate the in situ application of stir bar sorptive extraction (SBSE) as a passive sampling technique for the monitoring of 16 pesticides in a small river of an agricultural watershed located in the Beaujolais region. Stir bars hal-00871684, version 1-(Twister ® ) were deployed for several periods of one or two weeks during two one-month campaigns in 2010 and in 2011. With prior in-lab calibration, the in situ application of SBSE allowed the integration of a quick concentration peak and the determination at lower cost of average concentrations of the target pesticides sim...
Based on the concept of Big Data Modeling, the errors in determining peak parameters of noisy Gaussian quartets using nonlinear least squares curve fitting have been evaluated. 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 found. Obtained results showed that small fitting error does not guarantee that the fitting algorithm does converge to the correct peak parameters. It was found that the mean probability is a useful measure of the effectiveness of the curve fitting procedure.
Spectral data QC procedures - Manual:002 Version:0.4.1
2018
Important disclaimer CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used Patrice's changes and updated | i | ii
Based on the concept of Big Data Modeling, the errors in determining peak parameters of noisy Gaussian quartets using nonlinear least squares curve fitting have been evaluated. 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 found. Obtained results showed that small fitting error does not guarantee that the fitting algorithm does converge to the correct peak parameters. It was found that the mean probability is a useful measure of the effectiveness of the curve fitting procedure.
PIXEF: the Livermore PIXE spectrum analysis package
Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 1994
This is a preprint ors paper intended for publication in a journal or proceedinsL Since changes may be made before publication, this preprint is made available with the understanding that it will not b_ cited or reproduced withmst the permission of the •author. ] IASTEB OiS"iRIi_tUI'ION OF [HIS DOC.UME.I',IT 15 UNI,.IMtl_ z '.... i i DISCLAIMER This document was prepared as an account of work spomored by an agency of the United States GovernmenL Neither the United States Government nor the University of California nor any of thdr employees, makes any warranty, express or implied, or ass umes any legal liability or responsibility for the accu racy, completeness, or usefulness of any information, apparatus, product, or process disdosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or the University o4"California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or the University of California, and shaJI not be used for advertising or product endorsement purposes.
SpectralAnalysis: Software for the Masses
Analytical Chemistry, 2016
Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.