Being Uncertain in Chromatographic Calibration—Some Unobvious Details in Experimental Design (original) (raw)
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Journal of Chromatography A, 2001
The paper describes experiments for the evaluation of uncertainties associated with a number of chromatographic parameters. Studies of the analysis of vitamins by HPLC illustrate the estimation of the uncertainties associated with experimental ''input'' parameters such as the detector wavelength, column temperature and mobile phase flow-rate. Experimental design techniques, which allow the efficient study a number of parameters simultaneously, are described. Multiple linear regression was used to fit response surfaces to the data. The resulting equations were used in the estimation of the uncertainties. Three approaches to uncertainty calculation were compared -Kragten's spreadsheet, symmetric spreadsheet and algebraic differentiation. In cases where non-linearity in the model was significant, agreement between the uncertainty estimates was poor as the spreadsheet approaches do not include second-order uncertainty terms.
Effects of experimental design on calibration curve precision in routine analysis
The Journal of Automatic Chemistry, 1998
A computational program which compares the effciencies of different experimental designs with those of maximum precision (D-optimized designs) is described. The program produces confidence interval plots for a calibration curve and provides information about the number of standard solutions, concentration levels and suitable concentration ranges to achieve an optimum calibration. Some examples of the application of this novel computational program are given, using both simulated and real data.
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Quantitative determination in chromatographic analysis based on n-way calibration strategies
Journal of Chromatography A, 2007
Chemometric techniques for calibration with three-way signals are sufficiently developed for their use in routine analysis. The advantage of the second order property (the possibility of quantifying an analyte in the presence of interferents) together with the guarantee of the uniqueness of the decomposition, what means to extract the signal corresponding only to the analyte of interest, make these calibration techniques especially useful for the quantification and identification of analytes in complex samples. This has a particular interest in the identification and quantification of banned substances or substances with a specified maximum limit. The paper describes the theory of the calibration methodology in relation to the signal order and then focuses the analysis on the three-way techniques commonly used in calibration: n-way partial least squares, multivariate curve resolution and parallel factor analysis. The figures of merit needed for the accreditation of analytical methods are analyzed from the viewpoint of n-way calibrations in chromatography.
Usefulness of Information Criteria for the Selection of Calibration Curves
Analytical Chemistry, 2013
The reliability of analytical results obtained with quantitative analytical methods is highly dependent on the selection of the adequate model used as the calibration curve. To select the adequate response function or model the most used and known parameter is to determine the coefficient R 2. However, it is well-known that it suffers many inconveniences, such as leading to overfitting the data. A proposed solution is to use the adjusted determination coefficient R adj 2 that aims at reducing this problem. However, there is another family of criteria that exists to allow the selection of an adequate model: the information criteria AIC, AICc, and BIC. These criteria have rarely been used in analytical chemistry to select the adequate calibration curve. This works aims at assessing the performance of the statistical information criteria as well as R 2 and R adj 2 for the selection of an adequate calibration curve. They are applied to several analytical methods covering liquid chromatographic methods, as well as electrophoretic ones involved in the analysis of active substances in biological fluids or aimed at quantifying impurities in drug substances. In addition, Monte Carlo simulations are performed to assess the efficacy of these statistical criteria to select the adequate calibration curve.
Journal of Chromatography A, 2008
For minimum-variance estimation of parameters by the method of least squares, heteroscedastic data should be weighted inversely as their variance, w i ∝ 1/ 2 i . Here the instrumental data variance for a commercial high-performance liquid chromatography (HPLC) instrument is estimated from 5 to 11 replicate measurements on more than 20 samples for each of four different analytes. The samples span a range of over four orders of magnitude in concentration and HPLC peak area, over which the sampling variance estimates s 2 are well represented as a sum of a constant term and a term proportional to the square of the peak area. The latter contribution is dominant over most of the range used in routine HPLC analysis and represents approximately 0.2% of peak area for all four analytes studied here. It includes a contribution from uncertainty in the syringe injection volume, which is found to be ±0.008 L. The dominance of proportional error justifies the use of 1/x 2 or 1/y 2 weighting in routine calibration with such data; however, the constant variance term means that these weighting formulas are not correct in the low-signal limit relevant for analysis at trace levels. Least-squares methods for both direct and logarithmic fitting of variance sampling estimates are described. Since such estimates themselves have proportional uncertainty, direct fitting requires iterative adjustment of the weights, while logarithmic fitting does not.
Calibration and Validation of Analytical Methods - A Sampling of Current Approaches, 2018
Calibration curve is a regression model used to predict the unknown concentrations of analytes of interest based on the response of the instrument to the known standards. Some statistical analyses are required to choose the best model fitting to the experimental data and also evaluate the linearity and homoscedasticity of the calibration curve. Using an internal standard corrects for the loss of analyte during sample preparation and analysis provided that it is selected appropriately. After the best regression model is selected, the analytical method needs to be validated using quality control (QC) samples prepared and stored in the same temperature as intended for the study samples. Most of the international guidelines require that the parameters, including linearity, specificity, selectivity, accuracy, precision, lower limit of quantification (LLOQ), matrix effect and stability, be assessed during validation. Despite the highly regulated area, some challenges still exist regarding the validation of some analytical methods including methods when no analyte-free matrix is available.
Drug Testing and Analysis, 2010
Estimation of measurement uncertainty (MU) for quantitative results is a requirement of ISO/IEC17025. This concept is well established for chromatographic methods in doping control and forensic analysis. For non-chromatographic methods, however, very few practical methodologies have been published. In this paper, the applicability of a top-down model, established for estimating uncertainty in chromatography, was evaluated for two other methodologies with different sets of raw data as a starting point. The first case study involves the estimation of MU for the determination of haematological parameters. In this case, a large data set of quality control material and proficiency testing results was available to establish MU. The second case study involves the estimation of MU for the recently approved method for the determination of human growth hormone misuse. In this case the amount of data available to establish MU was limited to results from method validation and a basic set of analysis data. In both cases a methodology based upon long-term bias, long-term imprecision and-eventually-a correction for standard impurity is proposed. The proposed methodology can be regarded as a dynamic procedure, which allows re-evaluation of MU on a regular basis. Finally, a concept for the verification and evaluation of MU estimations using proficiency testing results is proposed.