Multiple analytical method comparison using maximum livelihood principal component analysis and linear regression with errors in both axes

Martı́nez, Àngel; Riu, Jordi; Rius, F. Xavier

doi:10.1016/s0003-2670(01)01032-7

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…However, an important issue should be dealt with in the multivariate case. In the official literature concerning univariate calibration, it is usually assumed that only the instrument signal carries an uncertainty, although a number of works have emphasized the importance of taking into account the errors in both axes when studying a single component [72,[127][128][129][130][131]. This leads to exact variance expressions and exact t statistics.…”

Section: Previously Proposed Methodology In Multivariate Calibrationmentioning

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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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%

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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show abstract

“…66,[116][117][118][119][120] This leads to exact variance expressions and t statistics. As in univariate calibration, prediction intervals can be constructed in the multivariate scenario from the estimated standard error of prediction (square root of a variance estimate) and the relevant t statistics.…”

Section: Prediction Intervalsmentioning

confidence: 99%

Validation and Error

Olivieri

Faber²

2009

Comprehensive Chemometrics

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“…Comparison of instruments, laboratories, or different assay methods is of frequent interest (Bland and Altman, 1999;Martinez et al, 2001). It arises, for example, in "assay round robins" in which one wishes to assess whether different centers' measurements are interchangeable, and in regulatory settings of comparing different measuring instruments.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Measurements by Multiple Methods or Instruments

Hawkins

Sharma

2010

Journal of Biopharmaceutical Statistics

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Much work has been done on comparison of one device with another, but the problem of comparing three or more devices is less well known. Most existing work has concentrated on the possibilities of a constant relative bias between the devices, or of different linear relationships with the underlying true value. These two possibilities are placed within a hierarchy of models extending them to settings with multiplicative interaction terms. These additional terms can capture departures such as outliers, variance changing with the analyte concentration, and different measurement variances between the devices.

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Multiple analytical method comparison using maximum livelihood principal component analysis and linear regression with errors in both axes

Cited by 5 publications

References 18 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)

Validation and Error

Comparison of Measurements by Multiple Methods or Instruments

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