Selectivity and limits of detection in inductively coupled plasma optical emission spectrometry using multivariate calibration

Bauer, Gottfried; Wegscheider, Wolfhard; Ortner, Hugo M.

doi:10.1016/0584-8547(92)80017-b

Cited by 26 publications

(13 citation statements)

References 22 publications

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“…Three illustrative types of estimators are discussed to some detail in Section 4. Several approaches have been advanced in the literature for the first-order classical [18,28,151,152] and inverse models [19,153,154]. (The proposal of [154] is directly applicable to higher-order data.)…”

Section: Previously Proposed Methodology In Multivariate Calibrationmentioning

confidence: 99%

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Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report)

Olivieri

Faber²,

Ferré

et al. 2006

Pure and Applied Chemistry

322

166

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This paper gives an introduction to multivariate calibration from a chemometrics perspective and reviews the various proposals to generalize the well-established univariate methodology to the multivariate domain. Univariate calibration leads to relatively simple models with a sound statistical underpinning. The associated uncertainty estimation and figures of merit are thoroughly covered in several official documents. However, univariate model predictions for unknown samples are only reliable if the signal is sufficiently selective for the analyte of interest. By contrast, multivariate calibration methods may produce valid predictions also from highly unselective data. A case in point is quantification from near-infrared (NIR) spectra. With the ever-increasing sophistication of analytical instruments inevitably comes a suite of multivariate calibration methods, each with its own underlying assumptions and statistical properties. As a result, uncertainty estimation and figures of merit for multivariate calibration methods has become a subject of active research, especially in the field of chemometrics.

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Section: Previously Proposed Methodology In Multivariate Calibrationmentioning

confidence: 99%

“…In other words, the full S must be available. Successful application has been reported for the calibration of atomic spectra, e.g., obtained by inductively coupled plasma-optical emission spectrometry (ICP-OES) [27][28][29].…”

Section: Classical Vs Inverse Modelmentioning

confidence: 99%

Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report)

Olivieri

Faber²,

Ferré

et al. 2006

Pure and Applied Chemistry

322

166

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“…with Gaussian distribution) and therefore the constant probability lines are circles and the radius (R) of these circles (which is here the standard deviation of the noise, often denoted by σ ε ) is independent from the sample composition and thus also from the actual absorbance values. (Note, however, that this assumption is not naturally valid for all analytical measurements [15,19].Definition of multichannel selectivity becomes, however, somewhat more complicated in other cases, and is not considered here for simplicity. )…”

Section: Error Inflationmentioning

confidence: 99%

Comparison of the single channel and multichannel (multivariate) concepts of selectivity in analytical chemistry

Dorkó

Verbić

Horvai

2015

Talanta

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Cite this article as: Zsanett Dorkó, Tatjana Verbić and George Horvai, Comparison of the single channel and multichannel(multivariate) concepts of selectivity in analytical chemistry, Talanta, http://dx.doi.org/10.1016/j.talanta. 2015.02.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. www.elsevier.com This accepted author manuscript is copyrighted and published by Elsevier. AbstractDifferent measures of selectivity are in use for single channel and multichannel linear analytical measurements, respectively. It is important to understand that these two measures express related but still distinctly different features of the respective measurements. These relationships are clarified by introducing new arguments. The most widely used selectivity measure of multichannel linear methods (which is based on the net analyte signal, NAS, concept) expresses the sensitivity to random errors of a determination where all bias from interferents is computationally eliminated using pure component spectra. The conventional selectivity measure of single channel linear measurements, on the other hand, helps to estimate the bias caused by an interferent in a biased measurement. In single channel methods expert knowledge about the samples is used to limit the possible range of interferent concentrations. The same kind of expert knowledge allows improved (lower mean squared error, MSE) analyte determinations also in "classical" multichannel measurements if those are intractable due to perfect collinearity or to high noise inflation. To achieve this goal bias variance tradeoff is employed, hence there remains some bias in the results and therefore the concept of single channel selectivity can be extended in a natural way to multichannel measurements. This extended definition and the resulting selectivity measure can also be applied to the so-called inverse multivariate methods like partial least squares regression (PLSR), principal component regression (PCR) and ridge regression (RR).

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“…The NPS for ICP-MS (1-3) and ICP-AES (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) have been reported and the prevailing noise types identified as: (13 white (16). Noise in the ICP from large, undesolvated wet droplets and undissociated dry particles has also been characterized extensively (17)(18)(19)(20)(21)(22).…”

Section: Introductionmentioning

confidence: 99%

Inductively coupled plasma spectrometry: Noise characteristics of aerosols, application of generalized standard additions method, and Mach disk as an emission source

Luan

1995

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Selectivity and limits of detection in inductively coupled plasma optical emission spectrometry using multivariate calibration

Cited by 26 publications

References 22 publications

Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report)

Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report)

Comparison of the single channel and multichannel (multivariate) concepts of selectivity in analytical chemistry

Inductively coupled plasma spectrometry: Noise characteristics of aerosols, application of generalized standard additions method, and Mach disk as an emission source

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