Automatic simultaneous determination of Ca and Mg in natural waters with no interference separation

Ruisánchez, Itziar; Rius, A.; Larrechi, M. Soledad; Callao, M. Pilar; Rius, F. Xavier

doi:10.1016/0169-7439(94)00002-6

Cited by 21 publications

(9 citation statements)

References 13 publications

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“…Dataset 2: Method Comparison Study for Analyzing Ca 2+ in Water (Ruisánchez, Rius, Larrechi, Callao, and Rius 1994) Using Atomic Absorption Spectroscopy (AAS; Reference Method, x-axis) and Sequential Injection Analysis (SIA; New Method, y-axis). A total of 26 real samples were analyzed with the two chemical methods in comparison, and the regression line between the results of the two methods was found to evaluate for the agreement between the two chemical methods (i.e., if the intercept of this regression line did not significantly differ from 0 and simultaneously the slope of this regression line did not significantly differ from 1).…”

Section: Experimental Partmentioning

confidence: 99%

On Estimating Linear Relationships When Both Variables Are Subject to Heteroscedastic Measurement Errors

Cheng

Riu

2006

Technometrics

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This article discusses point estimation of the parameters in a linear measurement error (errors in variables) model when the variances in the measurement errors on both axes vary between observations. A compendium of existing and new regression methods is presented. Application of these methods to real data cases shows that the coefficients of the regression lines depend on the method selected. Guidelines for choosing a suitable regression method are provided.

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Section: Experimental Partmentioning

confidence: 99%

On Estimating Linear Relationships When Both Variables Are Subject to Heteroscedastic Measurement Errors

Cheng

Riu

2006

Technometrics

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“…In 1994, Ruisánchez et al 230 utilised a partial least-squares multivariate calibration to determine magnesium and calcium in natural waters after the reaction of the analytes with arsenazo III. In an extension of this work, Rius et al 231 used commercial mineral water samples as the calibration set in an attempt to minimise matrix effects and, although the calibration models worked well at low concentration levels, the predictive ability of the model for higher concentrations was limited.…”

Section: Ultraviolet and Visible Absorbancementioning

confidence: 99%

Sequential injection analysis

Lenehan

Barnett

Lewis

2002

Analyst

197

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“…This involves a method comparison study for analysing Mg 2 in natural waters, with atomic absorption spectrometry (AAS) on the x-axis and sequential injection analysis (SIA) on the yaxis [29]. The errors in AAS are derived from four replicates in the analysis.…”

Section: Data Setmentioning

confidence: 99%

Prediction intervals in linear regression taking into account errors on both axes

Río¹,

Riu²,

Rius³

2001

Journal of Chemometrics

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SUMMARYThis study reports the expressions for the variances in the prediction of the response and predictor variables calculated with the bivariate least squares (BLS) regression technique. This technique takes into account the errors on both axes. Our results are compared with those of a simulation process based on six different real data sets. The mean error in the results from the new expressions is between 4% and 5%. With weighted least squares, ordinary least squares, the constant variance ratio approach and orthogonal regression, on the other hand, mean errors can be as high as 85%, 277%, 637% and 1697% respectively. An important property of the prediction intervals calculated with BLS is that the results are not affected when the axes are switched.

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Automatic simultaneous determination of Ca and Mg in natural waters with no interference separation

Cited by 21 publications

References 13 publications

On Estimating Linear Relationships When Both Variables Are Subject to Heteroscedastic Measurement Errors

On Estimating Linear Relationships When Both Variables Are Subject to Heteroscedastic Measurement Errors

Sequential injection analysis

Prediction intervals in linear regression taking into account errors on both axes

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