Bayesian analysis of inductively coupled plasma mass spectra in the range 46–88 Daltons derived from biological materials

Sharp, Barry L.; Bashammakh, Abdulaziz S.; Thung, Choon Mong; Skilling, John; Baxter, Malcolm

doi:10.1039/b110408j

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…where R true and R obs are the true and observed ratios of the isotopes i and j. Eqn. ( 1) is derived from the original equation 10 simply by dividing both sides by the isotopic signal for isotope j.…”

Section: Commentary On Current Modelsmentioning

confidence: 99%

Instrument response functions, mass bias and matrix effects in isotope ratio measurements and semi-quantitative analysis by single and multi-collector ICP-MS

Ingle

Sharp

Horstwood³

et al. 2003

J. Anal. At. Spectrom.

Self Cite

104

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Section: Commentary On Current Modelsmentioning

confidence: 99%

Instrument response functions, mass bias and matrix effects in isotope ratio measurements and semi-quantitative analysis by single and multi-collector ICP-MS

Ingle

Sharp

Horstwood³

et al. 2003

J. Anal. At. Spectrom.

Self Cite

104

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“…The use of Bayesian analysis for the deconvolution of ICP-MS spectra, over the mass range m/z 46-88, provided information on both the nature of the species in the observed spectrum and the magnitude of their individual contributions. 156 Various tests were applied to determine the goodness of fit between the actual and predicted spectrum. It was pointed out that Bayesian deconvolution was not a calibration technique, but that the data derived from it were used for calibrations and subsequently to carry out quantitative analyses of the RM.…”

Section: Analytical Methodologymentioning

confidence: 99%

Atomic spectrometry update. Atomic mass spectrometry

Bacon¹,

Greenwood²,

Vaeck

et al. 2003

J. Anal. At. Spectrom.

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“…As an example, they include: multi-component spectral fitting (which is incorporated in several commercial instrument softwares); 61 matrix projection, which avoids measurement of background species; 75,76 generalised standard addition 77 and Bayesian analysis. 78…”

Section: Inductively Coupled Plasmamentioning

confidence: 99%

A tutorial on multivariate calibration in atomic spectrometry techniques

Cal-Prieto

Gómez-Carracedo

Carlosena

et al. 2008

J. Anal. At. Spectrom.

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Coupling multivariate regression methods to atomic spectrometry is an emerging field from which important advantages can be obtained. These include lower workloads, increased laboratory turnarounds, economy, higher efficiency in method development, and relatively simple ways to take account of complex interferences. In this paper four typical regression methods (ordinary multiple linear regression, principal components regression, partial least squares and artificial neural networks) are presented in a practice-oriented way. The main emphasis is placed on explaining their advantages, drawbacks, how to solve the latter and how atomic spectrometry can benefit from multivariate regression. Finally, a retrospective review considering the last sixteen years is made to present practical applications on: flame-, hydride generation-, electrothermalatomic absorption spectrometry; inductively coupled plasma spectrometry and laser-induced breakdown spectrometry.

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Bayesian analysis of inductively coupled plasma mass spectra in the range 46–88 Daltons derived from biological materials

Cited by 10 publications

References 22 publications

Instrument response functions, mass bias and matrix effects in isotope ratio measurements and semi-quantitative analysis by single and multi-collector ICP-MS

Instrument response functions, mass bias and matrix effects in isotope ratio measurements and semi-quantitative analysis by single and multi-collector ICP-MS

Atomic spectrometry update. Atomic mass spectrometry

A tutorial on multivariate calibration in atomic spectrometry techniques

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