Analytical curves are normally obtained from discrete data by least squares regression. The least squares regression of data involving significant error in both x and y values should not be implemented by ordinary least squares (OLS). In this work, the use of orthogonal distance regression (ODR) is discussed as an alternative approach in order to take into account the error in the x variable. Four examples are presented to illustrate deviation between the results from both regression methods. The examples studied show that, in some situations, ODR coefficients must substitute for those of OLS, and, in other situations, the difference is not significant.

Traditionally, in the cigarettes industry, the determination of ammonium ion in the mainstream smoke is performed by ion chromatography. This work studies this determination and compares the results of this technique with the use of external and internal standard calibration. A reference cigarette sample presented measurement uncertainty of 2.0 μg/cigarette and 1.5 μg/cigarette, with external and internal standard, respectively. It is observed that the greatest source of uncertainty is the bias correction factor and that it is even more significant when using external standard, confirming thus the importance of internal standardization for this correction.

The evaluation of uncertainty associated with an analytic result is an essential part of the measurement process. Recently, several approaches to evaluate the uncertainty in measurement have been developed. Here, the gas chromatography assay uncertainty for natural gas is compared by some of these approaches: the guide to the expression of uncertainty in measurement (GUM) approach, top-down approach (reproducibility estimate from an inter-laboratory study), Barwick & Ellison (data from validation), study of variability and fuzzy approach. The comparison shows that GUM, Barwick & Ellison and fuzzy approaches lead to comparable uncertainty evaluations, which does not happen with the top-down approach and study of variability by the absence of data normality.

The most widespread literature for the evaluation of uncertainty - GUM and Eurachem - does not describe explicitly how to deal with uncertainty of the concentration coming from non-linear calibration curves. This work had the objective of describing and validating a methodology, as recommended by the recent GUM Supplement approach, to evaluate the uncertainty through polynomial models of the second order. In the uncertainty determination of the concentration of benzatone (C) by chromatography, it is observed that the uncertainty of measurement between the methodology proposed and Monte Carlo Simulation, does not diverge by more than 0.0005 unit, thus validating the model proposed for one significant digit.