Title

IRON ORES CHARACTERIZATION - A STATISTICAL TOOL FOR ANALYTICAL METHODS COMPARISON AND VALIDATION

IRON ORES CHARACTERIZATION - A STATISTICAL TOOL FOR ANALYTICAL METHODS COMPARISON AND VALIDATION

Authorship

Pereira, Ana Maria Tonani; Brandão, Paulo Roberto Gomes

Pereira, Ana Maria Tonani

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Brandão, Paulo Roberto Gomes

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DOI

10.5151/ABM-IRONMAKING-2001-2001212

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Abstract

An X-ray fluorescence (XRF) study comparing the performance of standardless analysis in relation to the traditional standard-based analysis technique is presented. The comparison using precision line regression analyses and simultaneous confidence intervals provides reliable information about the precision and trueness that each method can achieve. This study enables a comparison of the results obtained with two different analytical approaches for iron ore samples, in terms of precision and accuracy of results, time in preparing samples, and availability of standards. In this study, as a new technique for assessing the accuracy of an analytical method using linear regression, the results of both analyses are regressed against certified reference materials (CRMs). The statistical test is based on the joint confidence interval for the slope and the intercept of the regression line, calculated taking into account the uncertainties in both axes or in both analytical methods. The slope, intercept, and variances associated with the regression coefficients are calculated with bivariate least-squares regression (BLS) instead of the traditional ordinary least squares regression (OLS). After each calibration procedure, some chosen standard samples are analyzed for statistical validation of the methods. These samples have sufficient replicates to perform the calculations and later statistical comparison between the accuracy and precision.

An X-ray fluorescence (XRF) study comparing the performance of standardless analysis in relation to the traditional standard-based analysis technique is presented. The comparison using precision line regression analyses and simultaneous confidence intervals provides reliable information about the precision and trueness that each method can achieve. This study enables a comparison of the results obtained with two different analytical approaches for iron ore samples, in terms of precision and accuracy of results, time in preparing samples, and availability of standards. In this study, as a new technique for assessing the accuracy of an analytical method using linear regression, the results of both analyses are regressed against certified reference materials (CRMs). The statistical test is based on the joint confidence interval for the slope and the intercept of the regression line, calculated taking into account the uncertainties in both axes or in both analytical methods. The slope, intercept, and variances associated with the regression coefficients are calculated with bivariate least-squares regression (BLS) instead of the traditional ordinary least squares regression (OLS). After each calibration procedure, some chosen standard samples are analyzed for statistical validation of the methods. These samples have sufficient replicates to perform the calculations and later statistical comparison between the accuracy and precision.

Keywords

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