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

Abstract

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

Substitution and diffusion of Cr2+ and Cr3+ in synthetic forsterite and natural olivine at 1200–1500 °C and 1 bar

“…52 Cr, 53 Cr , 27 Al, 57 Fe, 25 Mg and 29 Si were all counted for 0.01-0.2 s. Data were processed using Iolite (Paton et al, 2011) with NIST SRM 610 glass as the primary and NIST SRM 612 as the secondary standard, using the values of Jochum et al (2011). Critical values (lowest detectable concentrations) for Cr using 53 Cr, calculated using the Ulianov et al (2016) formulation and assuming Gaussapproximated (excess variance) backgrounds, were typically 0.1-0.2 µg/g. High baselines for 52 Cr (possibly 36 Ar 16 O) meant that data from this isotope could not generally be used.…”

LA-ICP-MS analysis of fluid inclusions: contamination effects challenging micro-analysis of elements close to their detection limit

“…3,[33][34][35][36] Even though Au is expected to be enriched in S-rich vapor 3 37 , the reduced amount of an analyte in low-density vapor inclusions produces shorter signals, which are subject to larger uncertainties regarding representative quantification by sequential sampling of the signal 18,20 . Counting statistics is the major source of uncertainty for quantification of elements close to the analytical detection limit, requiring Poisson statistics 18,38,39 rather than a Gaussian approach 11 . Moreover, a potential bias may result in overestimation of average element concentrations in fluid inclusion assemblages, where only some inclusions provide detectable signals even if the bulk composition of all inclusions is expected to be similar.…”

Three-Dimensional Reconstruction of the Tissue-Specific Multielemental Distribution within Ceriodaphnia dubia via Multimodal Registration Using Laser Ablation ICP-Mass Spectrometry and X-ray Spectroscopic Techniques

“…Thus, the lower count rates in the mass channels corresponding to the isotopes of low abundance are influenced by Poisson process noise to a relatively higher extent . For flickering sources like the ICP, doubly stochastic Poisson distributions apply and excess variance may have to be taken into account. , The influence of frequency-independent white noise was reduced by compiling the calibrated responses of n selected isotopes of a specific element E into a single local elemental concentration V E using a linear combination (LC). Within the LC, a weight factor is introduced based on the natural abundance of an isotope a k E normalized to the cumulative natural abundances for all selected isotopes and theoretical Poisson process noise: …”

“…For count rates higher than a few counts per acquisition, the uncertainty on the signal intensity can be estimated as the square root of the number of counts registered (following the ordinary Poisson distribution in the Poisson–Gauss approximation). Thus, the lower count rates in the mass channels corresponding to the isotopes of low abundance are influenced by Poisson process noise to a relatively higher extent . For flickering sources like the ICP, doubly stochastic Poisson distributions apply and excess variance may have to be taken into account. , The influence of frequency-independent white noise was reduced by compiling the calibrated responses of n selected isotopes of a specific element E into a single local elemental concentration V E using a linear combination (LC).…”

“…29 For flickering sources like the ICP, doubly stochastic Poisson distributions apply and excess variance may have to be taken into account. 29,30 The influence of frequency-independent white noise was reduced by compiling the calibrated responses of n selected isotopes of a specific element E into a single local elemental concentration V E using a linear combination (LC). Within the LC, a weight factor is introduced based on the natural abundance of an isotope a k E normalized to the cumulative natural abundances for all selected isotopes and theoretical Poisson process noise:…”