Accreditation and Quality Assurance (v.16, #7)

Test and Measurement Conference 2010 by Angelique Botha; Edward Peter Tarnow (337-337).

Total CO2 measurements in horses: where to draw the line by D. Brynn Hibbert; Nicholas Armstrong; John H. Vine (339-345).
Racehorses given a slurry of sodium bicarbonate (known colloquially as a ‘milkshake’) before a race have elevated concentrations of carbon dioxide in their blood. Racing administrators have reacted to this attempt to enhance the performance of the animal by setting limits to ‘total carbon dioxide’ (TCO2, the sum of carbon dioxide, carbonic acid, carbonate and bicarbonate) in prerace samples of plasma. The threshold limit for TCO2 in the rules of racing is an amount concentration of 36.0 mmol/L, with further action ensuing if the reported concentration is greater than an action limit that is calculated from the knowledge of the measurement uncertainty. At present in Australia, the action limit is 37.0 mmol/L, which is based on a combined standard measurement uncertainty of 0.22 mmol/L. From data obtained in a 1997 study in 515 normal racehorses, we have established the distribution (as a probability density function, PDF) of TCO2. This is combined with data from Australian laboratories of 126 horses that were tested following a positive screen, out of which 78 were confirmed positive. We employ the maximum entropy method to establish the PDFs and then apply Bayes Theorem to answer the question ‘given the measured TCO2 concentration what is the probability that a horse has been administered bicarbonate’? The distributions are not normal, which precludes simple approaches that calculate standard deviations from the data. For an action level of 37.0 mmol/L, there is a chance of only 1 in 2 020 000 that a nondoped horse will be judged to be doped, which implies this present threshold is unlikely to lead to conviction of an innocent trainer.
Keywords: Equine plasma; Carbon dioxide analysis; TCO2; Horseracing; Sports drug testing; Electrochemical gas analyser; Bayes theorem; Maximum entropy; Bayes model selection

Measurement error models and variance estimation in the presence of rounding error effects by T. Burr; M. S. Hamada; T. Cremers; B. P. Weaver; J. Howell; S. Croft; S. B. Vardeman (347-359).
An approach to estimating measurement error variances for any instrument having round-off effects that might also have instrument bias is presented. Recently finite instrument resolution effects on error variances have been studied, but negligible instrument bias was assumed and the contexts were different than considered here. Our intent is to use repeated measurements on several standards to estimate the instrument’s random and systematic error variances. Recognizing that rounding impacts item bias and variance in a manner that depends on the true value, an approach is presented to estimate random error variance and instrument systematic error variance. The key finding is that item-specific bias can interfere with the estimation of overall instrument bias unless appropriate error modeling and associated inference steps are taken.
Keywords: Bayesian methods; Instrument resolution; Item-specific bias; Likelihood

Multi-component out-of-specification test results: a case study of concentration of pesticide residues in tomatoes by Ilya Kuselman; Paulina Goldshlag; Francesca Pennecchi; Cathy Burns (361-367).
A metrological approach is used for investigating multi-component out-of-specification (OOS) test results of pesticide residues concentration in tomatoes. As a case study, 169 test results were obtained in Israel in 2009. Five of the test results were OOS test results exceeding the national legal maximum residue limits (MRL). Only one of them was classified definitely (with more than 0.99 confidence) as caused by a farmer’s/producer’s problem. The other four OOS test results were probably metrologically related, i.e., compatible with MRL when considering the measurement uncertainty associated with the test results. A new parameter—the ratio of a test result to MRL—was proposed for analysis of tomatoes monitoring multi-residue data as a common statistical sample from the same population for different pesticide residues. Weibull distribution was found adequate for modeling the empirical distribution of the parameter values. Probability of future OOS test results was estimated, and global risks of farmer/producer and consumer/buyer were evaluated. Acceptance limits for the test results, such as “warning and action lines” in quality control charts, were calculated by taking into account the measurement uncertainties.
Keywords: Pesticide residues; Tomatoes; Out-of-specification test results; Measurement uncertainty; Producer’s and consumer’s risks; Acceptance limits

An evaluation of analytical quality for selected PAH measurements in a fuel-contaminated soil by S. García-Alonso; R. M. Pérez-Pastor; F. J. García-Frutos (369-377).
The measurements of polycyclic aromatic hydrocarbons (PAHs) in soil require optimized analytical methods that assess reliable mass fraction results. This is particularly important for analysing very complex matrix such as contaminated soils with crude fuels. The main objectives of this work were focused to minimize analytical effort and assess result reliability in analysis of PAH by high-performance liquid chromatography with fluorescence detection (HPLC/FD). First, analysis of soil samples with/without fuel contamination was well established by sonication (US) and pressurized fluid extraction (ASE) using minimal amount of sample and minimal treatment of sample. On the other hand, an extensive study with spiked and field soil samples was performed by checking proportional and constant bias for analytical validation. The major components for estimating uncertainty contributions were evaluated on the basis of intermediate precision with two fuel matrix, PAH mass fraction and dates of analyses.
Keywords: PAHs; Fuel; Contaminated soil; HPLC/FD; Uncertainty evaluation

Evaluation of bias, precision, and systematic errors in proficiency testing of Cl and Cu concentration in water by A. B. Chelani; C. A. Moghe; S. Nimsadkar; K. Gandhi; G. L. Bodhe; S. M. Dhopte; N. P. Thacker (379-382).
The proficiency testing exercise was conducted to assess the quality of water testing in several laboratories in India. The 11 participants from all over India gathered at one place and attended the workshop organized at NEERI, Nagpur. The test samples were analyzed for Cl (Chloride) and Cu (Copper) concentration in water. The objective of the study was to determine bias and precision among participants for the analysis of Cl and Cu concentration. Statistical analysis indicated that most of the measurement results were overestimating the Cl concentration and underestimating the Cu concentration. The presence of systematic error identified the need for further improvement in determining the Cu concentration in water by the participants.
Keywords: Bias; Precision; Systematic error

About ‘measurement result’ and ‘measured value’ by Xavier Fuentes-Arderiu; Aída Porras-Caicedo (387-388).