Standard addition method with cumulative additions: Monte Carlo uncertainty evaluation

Application of the adaptive Monte Carlo method for uncertainty evaluation in the determination of total testosterone in human serum by triple isotope dilution mass spectrometry

The measurement uncertainty is a crucial quantitative parameter for assessing the reliability of the result. The study aimed to propose a new budget for uncertainty evaluation of a reference measurement procedure for the determination of total testosterone in human serum. The adaptive Monte Carlo method (aMCM) was used for the propagation of probability distributions assigned to various input quantities to determine the uncertainty of the testosterone concentration. The basic principles of the propagation and the statistical analysis were described based on the experimental results of the quality control serum sample. The analysis of the number of Monte Carlo trials was discussed. The procedure of validation of the GUM uncertainty framework using the aMCM was also provided. The number of Monte Carlo trials was 2.974 × 106 when the results had stabilized. The total testosterone concentration was 16.02 nmol/L, and the standard uncertainty was 0.30 nmol/L. The coverage interval at coverage probability of 95% was 15.45 to 16.62 nmol/L, while the probability distribution for testosterone concentration was approximately described by a Gaussian distribution. The validation of results was not passed as the expanded uncertainty result obtained by the aMCM was slightly lower, about 7%, than that by the GUM uncertainty framework with consistent results of the concentration.

Evaluation and validation of detailed and simplified models of the uncertainty of unified [Formula: see text] measurements in aqueous solutions

The use of the unified pH concept, [Formula: see text] , applicable to aqueous and non-aqueous solutions, which allows interpreting and comparison of the acidity of different types of solutions, requires reliable and objective determination. The [Formula: see text] can be determined by a single differential potentiometry measurement referenced to an aqueous reference buffer or by a ladder of differential potentiometric measurements that allows minimisation of inconsistencies of various determinations. This work describes and assesses bottom-up evaluations of the uncertainty of these measurements, where uncertainty components are combined by the Monte Carlo Method (MCM) or Taylor Series Approximation (TSM). The MCM allows a detailed simulation of the measurements, including an iterative process involving in minimising ladder deviations. On the other hand, the TSM requires the approximate determination of minimisation uncertainty. The uncertainty evaluation was successfully applied to measuring aqueous buffers with pH of 2.00, 4.00, 7.00, and 10.00, with a standard uncertainty of 0.01. The reference and estimated values from both approaches are metrologically compatible for a 95% confidence level even when a negligible contribution of liquid junction potential uncertainty is assumed. The MCM estimated pH values with an expanded uncertainty, for the 95% confidence level, between 0.26 and 0.51, depending on the pH value and ladder inconsistencies. The minimisation uncertainty is negligible or responsible for up to 87% of the measurement uncertainty. The TSM quantified measurement uncertainties on average only 0.05 units larger than the MCM estimated ones. Additional experimental tests should be performed to test these uncertainty models for analysis performed in other laboratories and on non-aqueous solutions.

Application of Measurement Uncertainty on Conformity Assessment in Pharmaceutical Drug Products

BACKGROUND

Conformity assessment in pharmaceutical drug products usually are based on analytical results. An analytical result always is associated with an uncertainty which must be considered in a compliance assessment.

OBJECTIVE

The objective of the present project aims to estimate the measurement uncertainty associated with the results of average weight, assay, uniformity of content, and dissolution testing in ranitidine tablets manufactured by two different companies (X and Y).

METHODS

The uncertainty was evaluated using the Monte Carlo method. The particular and total risks of false acceptance decisions (consumers' risks) were also estimated by the Monte Carlo method.

RESULTS

The results for ranitidine tablets manufactured by company X were (359.2 ± 2.7) mg of average weight, (108.6 ± 1.6)% of assay, (11.4 ± 2.8) of acceptance value for uniformity of content, and (89.4 ± 1.3)% for dissolution testing. The results for company Y were (312.5 ± 0.9) mg, (107.0 ± 1.4)%, (7.2 ± 1.7), and (93.6 ± 1.3)%, respectively. A 95% confidence level was adopted. According to results obtained from the Monte Carlo simulations, the results of average weight, assay of ranitidine, and uniformity of content are significantly correlated due to shared analytical steps. The correlation between values significantly affected the risk of false decisions.

CONCLUSIONS

Consumers' risks were estimated for both ranitidine tablets and their results ensure compliance for ranitidine tablets manufactured by companies X and Y.

HIGHLIGHTS

The use of measurement uncertainty is an important issue regarding the conformity/non-conformity assessment of pharmaceutical products.

Detection of l-Methamphetamine and l-Amphetamine as Selegiline Metabolites

Selegiline (SE) is a selective, irreversible monoamine oxidase-B inhibitor, used for reducing symptoms in early-stage Parkinson's disease. The metabolites of SE include l-methamphetamine, l-amphetamine and desmethylselegiline (DSE). The stereoisomers of SE metabolites, d-methamphetamine and d-amphetamine are highly addictive psychostimulants and some of the most abused drugs in South Korea. In order to differentiate medical SE users form illicit methamphetamine abusers, it is important to distinguish between the l-isomers and d-isomers in urine samples. A 52-year-old male, seemingly under the influence of intoxication and demonstrating abnormal behavior, was reported to the police. The initial urine test using a methamphetamine detection kit demonstrated a positive result. Given the initial results, the police officer requested a further analysis of the urine sample. The urine sample was screened using headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Both methamphetamine and amphetamine were detected, in addition to SE and DSE. To quantitate methamphetamine and amphetamine by HS-SPME-GC-MS, we performed a standard addition method due to the matrix effect of the case sample. Consistent with previous studies, our results indicated that the ratio of amphetamine to methamphetamine was 0.27, which was in the range of SE ingestion. Furthermore, we confirmed l-methamphetamine and l-amphetamine by chiral derivatization using (R)-(-)-α-methoxy-α-(trifluoromethyl) phenylacetyl chloride.

Monte Carlo bottom-up evaluation of global instrumental quantification uncertainty: Flexible and user-friendly computational tool

Many instrumental methods of analysis require the daily collection of calibrator signals to calibrate their response. The quality of quantifications based on these calibrations depends on calibrators quality, instrumental signal performance and regression model fitness. Linear Ordinary Least Squares (LOLS), Linear Weighted Least Squares (LWLS) or Linear Bivariate Least Squares (LBLS) regression models can be used to calibrate and evaluate the uncertainty from instrumental quantifications, but require the fulfilment of some assumptions, namely, constant signal variance (LOLS), high calibrators quality (LOLS and LWLS) and linear variation of instrumental signal with calibrator values. The LBLS is flexible regarding calibrator values uncertainty and correlation but requires the determination of calibrator values and signals covariances. This work developed a computational tool for the bottom-up evaluation of global instrumental quantifications uncertainty which simulates calibrator values correlations from entered calibrators preparation procedure and simulates calibrators and samples signals precision from prior precision data, allowing accurate uncertainty evaluation from a few replicate signals of the daily calibration. The used signal precision models were built from previously observed repeatability variation throughout the calibration interval adjusted to daily precision condition from a residual standard deviation adjustment factor. This approach was implemented in a user-friendly MS-Excel file and was successfully applied to the analysis of As, Cd, Ni and Pb in marine sediment extracts by Absorption Spectroscopy. Evaluations were tested by the metrological compatibility of estimated and reference values of control standards for confidence levels of 95% and 99%. The success rates of the compatibility tests were statistically equivalent to the confidence level (p-value>0.01).