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Accreditation and Quality Assurance: Journal for Quality, Comparability and Reliability in Chemical Measurement (v.15, #5)


Development and validation of a differential scanning calorimetry purity determination method for polycyclic aromatic hydrocarbons by Vikram Kestens; Gert Roebben; Thomas Linsinger (pp. 269-281).
Based on a standard test method for purity by differential scanning calorimetry (DSC), ASTM E 928, a purity determination method for highly pure polycyclic aromatic hydrocarbons (PAHs) has been developed and validated. The robustness of the developed method was investigated by determining, under varying measurement conditions, the purity of two PAH certified reference materials (CRMs), benzo[c]phenanthrene and dibenzo[a,h]anthracene. The repeatability and intermediate precision of the developed method was determined by analysing the purity of benzo[c]phenanthrene and dibenzo[a,h]anthracene and PAH candidate CRMs indeno[1,2,3-c,d]pyrene, 6-methylchrysene and benzo[a]pyrene. The trueness of the method was studied using the same (candidate) CRMs and a series of 42 other PAH CRMs. For each of the five (candidate) CRMs, a full measurement uncertainty budget was developed. Also for PAH materials for which the DSC purity determination method has not been explicitly validated, the relative expanded measurement uncertainty was estimated.

Keywords: Certified reference materials; Differential scanning calorimetry; Measurement uncertainty; Polycyclic aromatic hydrocarbons; Purity; Trueness; Validation


Investigating out-of-specification test results of chemical composition based on metrological concepts by Ilya Kuselman; Francesca Pennecchi; Cathy Burns; Aleš Fajgelj; Paolo de Zorzi (pp. 283-288).
A metrological background for investigating out-of-specification (OOS) test results of chemical composition is discussed. When an OOS test result is identified, it is important to determine its root causes and to avoid reoccurrence of such results. An investigation of the root causes based on metrological concepts would be beneficial. It includes (1) assessment of validation data of the measurement process, (2) evaluation of the measurement uncertainty contributions, and (3) assessment of metrological traceability chains critical for measurement parameters and environmental conditions influencing the test results. The questions, how can the validation data be applied for this investigation, and how can measurement uncertainty contributions and/or metrological traceability chains change a probability of OOS test results, are analyzed.

Keywords: Out-of-specification test results; Validation; Measurement uncertainty; Metrological traceability; Probability


The impact of measurement uncertainty on the producer’s and user’s risks, on classification and conformity assessment: an example based on tests on some construction products by Wilfried Hinrichs (pp. 289-296).
The reliability of test results and subsequent classification statements or product certification depend on the variability of the product’s properties and on the validity of the test procedures used. With an emphasis on measurement uncertainty, producer’s and user’s risks as well as probabilities of conformance, conformity and classification are calculated exemplarily for two requirements for mineral aggregates used in construction. An important methodological basis is an international draft document on measurement uncertainty in conformity assessment. The mathematical instruments given are applied and further developed to a risk scenario for product classification. The results from a classification point of view show that the reliability of test results for acid-soluble sulphates is mostly acceptable and both the producer’s and user’s risks are quite small. In contrast, the magnesium sulphate index test produces results which are hardly usable for classification and certification purposes or for risk management in production. Product certification bodies should generally have an appropriate approach when dealing with results where precision data are poor.

Keywords: Measurement uncertainty; Product certification; Classification; Risk; Compliance


Quality assurance in research: incorporating ISO9001:2000 into a GMP quality management system in a pharmaceutical R+D+I center by Anna Fàbregas-Fernández; Encarna García-Montoya; Pilar Pérez-Lozano; Josep M. Suñé-Negre; Josep Ramon Ticó; Montserrat Miñarro (pp. 297-304).
There is currently no universal or standardized quality system for the recognition of excellence of a research center. Knowledge and competence may not be enough in the current rapidly changing world in which high productivity and continuous improvement are essential. The purpose of this study was to assess the impacts of implementing the ISO9001:2000:2000 standard in an academic research center dedicated to R+D+I (research, development and innovation) in the pharmaceutical industry. The article describes the stages we followed to implement the ISO9001:2000 system, which was achieved by integrating it into the previous regulatory system of GMP (Good Manufacturing Practices). As a result of implementing ISO9001:2000, the center has seen distinct improvements, such as fewer errors in project documentation, improved assessment of customer satisfaction, and the effective implementation of periodic plans, e.g., in calibration, preventive maintenance, and investments. Overall, ISO9001:2000 implementation has been beneficial for the organization and could be applied to other research centers.

Keywords: ISO9001:2000; Quality; University; Research; GMP; Certification


Dependence of the treatment of systematic error in interlaboratory comparisons on different classes of standards by Franco Pavese (pp. 305-315).
A few years ago the question arose whether different types of measurands can be the consequence of the different intrinsic nature of different types of standards, which prompted an analysis resulting in the proposal of two distinct ‘classes’ of standards be considered. They correspond to different situations depending upon whether the aim is to compare laboratory artefact standards (Class 1), or, instead, to compare standards whose values are a measure of the value of the same unequivocally defined physical or chemical condition (Class 2). This distinction is relevant to the statistical treatment of the comparison data, in particular, with respect to the issue of the correct modelling of the data and of the correct choice of the summary statistics for populations of non-repeated data. This paper, after a summary of the rational of making a distinction of standard in “classes”, and discussing the general issue of bias, is discussing some implications of the above distinction, concentrating on cases where the effect of the systematic error is dominating the experimental results, as it commonly happens in several metrology fields. A preferred way to tackle the problem for standards of Class 2 is presented.

Keywords: Classes of standards; Systematic effects; Inter-comparisons; Mixture distribution
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