Observation of the frequency dependence of ICP radiation noise

Antanavicius, R; Serapinas, P.; Simkus, P

doi:10.1088/0022-3727/22/2/003

Cited by 10 publications

(16 citation statements)

References 11 publications

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“…It follows that using eqn (28) and (31) is the most general solution for the computation of the standard deviation of LA-ICPMS data. Moreover, for years eqn (31) has been the only mathematical solution for the estimation of standard deviations in LA-ICPMS detection calculations.…”

Section: Datamentioning

confidence: 99%

“…13, 14 The net count number 1. THE √2NB RULE AND ITS SIZE [28] Principles of size evaluation; the √2Nb rule as an example. The most basic of all decision rules for paired measurements is the √2Nb rule (eqn (18)) and a generalisation of it suitable for unequal sample/background counting times:…”

Section: About the Poisson-gauss Approximation Of Confidence Intervalsmentioning

confidence: 99%

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Detection in LA-ICPMS: construction and performance evaluation of decision rules

Ulianov

Müntener

Schaltegger

et al. 2016

J. Anal. At. Spectrom.

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Laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) is frequently employed for the analysis of minute isotope contents in the presence of a background noise. Distinguishing between the sample signal and the background noise at a given confidence level thus represents a routine challenge. For count numbers Nb and Ns collected during (equally long) background and sample measurements, respectively, the statistical significance of their net value, Ns, Nb, can be evaluated: how probable is it to obtain such value by subtracting two count number estimates coming from a common statistical distribution (i.e., when Ns and Nb represent measured estimates of the same mean activity)? If, based on the analysis of a model distribution of the net count numbers, we decide that this is probable, the signal is statistically indistinguishable from the background: the analysed isotope is not detected. If the corresponding (one-sided) probability is below some threshold, it is detected. The net signal value on the divide between the above alternatives, given in net counts or mass (content) units, is called critical level; optionally, it can be complemented by the computation of the detection limit; such values are often reported in the literature. Less discussed is the appropriateness of computational methods used to estimate these values. Troubles arise from attempts to apply Gaussian confidence intervals to small, discretely distributed count numbers contained in real LA-ICPMS acquisitions, and from a non-optimal estimation of the net count number standard deviation in some of the methods used for the computation of critical levels for paired measurements. Combined, these factors may result in uncontrolled, excessively high rates of false detections (background reported as detection of analyte in the sample). Here, we provide a review of methods, otherwise called decision rules, available for the critical level estimation and discuss how to evaluate the performances of these rules to enable an educated computation of LA-ICPMS detection capabilities, including the case of small count numbers

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Section: Datamentioning

confidence: 99%

Section: About the Poisson-gauss Approximation Of Confidence Intervalsmentioning

confidence: 99%

Detection in LA-ICPMS: construction and performance evaluation of decision rules

Ulianov

Müntener

Schaltegger

et al. 2016

J. Anal. At. Spectrom.

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“…52,65 In the late 1980's, Antanavičius and co-workers studied ICP-AES signals for gaseous samples and aerosols obtained by solution nebulisation and injection of metal oxide powders in the ICP. [66][67][68] It was shown that the power spectral density of low frequency (icker) noise obtained for aerosols increases by an order of magnitude compared to gaseous samples. Similar data (for helium ICP) were obtained by Montaser and co-workers.…”

Section: Fluctuating M Sample Introduction Process?mentioning

confidence: 99%

The ICPMS signal as a Poisson process: a review of basic concepts

Ulianov

Müntener

Schaltegger

2015

J. Anal. At. Spectrom.

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Understanding the structure of noise associated with a measurement process is interesting theoretically and has practical applications related to the quantification of detection capability, signal uncertainty and dead time. Here, we present and analyse arguments explaining the appearance of the Poisson process in the distribution of count numbers in inductively coupled plasma mass spectrometry (ICPMS) signals. We consider the Poisson distribution as a special case of the binomial distribution constrained by inefficient ion transmission from the ICP ion source to the detector. The universal form of the relevant Poisson process is doubly stochastic: the random nature of count numbers acquired per time unit is defined not only by the probabilistic selection of ions during their transport through the interface and ion channel, but also by fluctuations of the ion contents sampled by the spectrometer from the plasma and, more generally, by fluctuations of the rate of the Poisson process itself. Compared to an ordinary Poisson process, the doubly stochastic Poisson process has an excess variance that increases at higher analyte contents. The excess variance in the uncertainty of ICPMS signals is also known as flicker noise; it is an integral part of the doubly stochastic Poisson process and not a fully individual noise component. We review processes pertinent to its origin and formalisms used to describe it

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“…In ICP-AES measurements, the attainable precision is influenced by both random (white) and time-correlated noise [4][5][6][7][8][9][10][11]. The most common white noise types are shot-noise and thermal noise, which can be minimized by using long integration times and cooled detectors, respectively.…”

Section: Introductionmentioning

confidence: 99%