Application of the Nordtest method for "real-time" uncertainty estimation of on-line field measurement.

Field sensor measurements are becoming more common for environmental monitoring. Solutions for enhancing reliability, i.e. knowledge of the measurement uncertainty of field measurements, are urgently needed. Real-time estimations of measurement uncertainty for field measurement have not previously been published, and in this paper, a novel approach to the automated turbidity measuring system with an application for "real-time" uncertainty estimation is outlined based on the Nordtest handbook's measurement uncertainty estimation principles. The term real-time is written in quotation marks, since the calculation of the uncertainty is carried out using a set of past measurement results. There are two main requirements for the estimation of real-time measurement uncertainty of online field measurement described in this paper: (1) setting up an automated measuring system that can be (preferably remotely) controlled which measures the samples (water to be investigated as well as synthetic control samples) the way the user has programmed it and stores the results in a database, (2) setting up automated data processing (software) where the measurement uncertainty is calculated from the data produced by the automated measuring system. When control samples with a known value or concentration are measured regularly, any instrumental drift can be detected. An additional benefit is that small drift can be taken into account (in real-time) as a bias value in the measurement uncertainty calculation, and if the drift is high, the measurement results of the control samples can be used for real-time recalibration of the measuring device. The procedure described in this paper is not restricted to turbidity measurements, but it will enable measurement uncertainty estimation for any kind of automated measuring system that performs sequential measurements of routine samples and control samples/reference materials in a similar way as described in this paper.

Routine internal- and external-quality control data in clinical laboratories for estimating measurement and diagnostic uncertainty using GUM principles.

Healthcare laboratories are increasingly joining into larger laboratory organizations encompassing several physical laboratories. This caters for important new opportunities for re-defining the concept of a 'laboratory' to encompass all laboratories and measurement methods measuring the same measurand for a population of patients. In order to make measurement results, comparable bias should be minimized or eliminated and measurement uncertainty properly evaluated for all methods used for a particular patient population. The measurement as well as diagnostic uncertainty can be evaluated from internal and external quality control results using GUM principles. In this paper the uncertainty evaluations are described in detail using only two main components, within-laboratory reproducibility and uncertainty of the bias component according to a Nordtest guideline. The evaluation is exemplified for the determination of creatinine in serum for a conglomerate of laboratories both expressed in absolute units (µmol/L) and relative (%). An expanded measurement uncertainty of 12 µmol/L associated with concentrations of creatinine below 120 µmol/L and of 10% associated with concentrations above 120 µmol/L was estimated. The diagnostic uncertainty encompasses both measurement uncertainty and biological variation, and can be estimated for a single value and for a difference. This diagnostic uncertainty for the difference for two samples from the same patient was determined to be 14 µmol/L associated with concentrations of creatinine below 100 µmol/L and 14 % associated with concentrations above 100 µmol/L.

Treatment of uncorrected measurement bias in uncertainty estimation for chemical measurements.

Consistent treatment of measurement bias, including the question of whether or not to correct for bias, is essential for the comparability of measurement results. The case for correcting for bias is discussed, and it is shown that instances in which bias is known or suspected, but in which a specific correction cannot be justified, are comparatively common. The ISO Guide to the Expression of Uncertainty in Measurement does not provide well for this situation. It is concluded that there is a need for guidance on handling cases of uncorrected bias. Several different published approaches to the treatment of uncorrected bias and its uncertainty are critically reviewed with regard to coverage probability and simplicity of execution. On the basis of current studies, and taking into account testing laboratory needs for a simple and consistent approach with a symmetric uncertainty interval, we conclude that for most cases with large degrees of freedom, linear addition of a bias term adjusted for exact coverage ("U(e)") as described by Synek is to be preferred. This approach does, however, become more complex if degrees of freedom are low. For modest bias and low degrees of freedom, summation of bias, bias uncertainty and observed value uncertainty in quadrature ("RSSu") provides a similar interval and is simpler to adapt to reduced degrees of freedom, at the cost of a more restricted range of application if accurate coverage is desired.

Bias in clinical chemistry.

Clinical chemistry uses automated measurement techniques and medical knowledge in the interest of patients and healthy subjects. Automation has reduced repeatability and day-to-day variation considerably. Bias has been reduced to a lesser extent by reference measurement systems. It is vital to minimize clinically important bias, in particular bias within conglomerates of laboratories that measure samples from the same patients. Small and variable bias components will over time show random error properties and conventional random-error based methods for calculating measurement uncertainty can then be applied. The present overview of bias presents the general principles of error and uncertainty concepts, terminology and analysis, and suggests methods to minimize bias and measurement uncertainty in the interest of healthcare.

Survey analysis and chemical characterization of solid inhomogeneous samples using a general homogenization procedure including acid digestion, drying, grinding and briquetting together with X-ray fluorescence.

A survey analysis and chemical characterization methodology for inhomogeneous solid waste samples of relatively large samples (typically up to 100g) using X-ray fluorescence following a general homogenization procedure is presented. By using a combination of acid digestion and grinding various materials can be homogenized e.g. pure metals, alloys, salts, ores, plastics, organics. In the homogenization step, solid material is fully or partly digested in a mixture of nitric acid and hydrochloric acid in an open vessel. The resulting mixture is then dried, grinded, and finally pressed to a wax briquette. The briquette is analyzed using wave-length dispersive X-ray fluorescence with fundamental parameters evaluation. The recovery of 55 elements were tested by preparing samples with known compositions using different alloys, pure metals or elements, oxides, salts and solutions of dissolved compounds. It was found that the methodology was applicable to 49 elements including Na, Mg, Al, Si, P, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Y, Zr, Nb, Mo, Ru, Rh, Pd, Ag, Cd, In, Sn, Sb, Te, Cs, Ba, La, Ce, Ta, W, Re, Ir, Pt, Au, Tl, Pb, Bi, and Th, that all had recoveries >0.8. 6 elements were lost by volatilization, including Br, I, Os, and Hg that were completely lost, and S and Ge that were partly lost. Since all lanthanides are chemically similar to La and Ce, all actinides are chemically similar to Th, and Hf is chemically similar to Zr, it is likely that the method is applicable to 77 elements. By using an internal standard such as strontium, added as strontium nitrate, samples containing relatively high concentrations of elements not measured by XRF (hydrogen to fluorine), e.g. samples containing plastics, can be analyzed.