Article
The purity of laboratory chemicals with regard to measurement uncertainty
Fluka Production GmbH, Buchs, Switzerland.
The Analyst (Impact Factor: 4.11). 06/2002; 127(6):8259. DOI: 10.1039/B107958C Source: PubMed
ABSTRACT
The purity P of laboratory chemicals is often declared in the form P > or = xy% (e.g., P > or = 97%). With a randomly chosen set of 40 compounds we found that their purity is generally closer to 100% than to the lower limit. The distribution of the purity data as found in the laboratory depends on the analytical technique used. Whereas purities determined by chromatography do not exceed 100% (because the sum of all observed peak areas is set to 100%), the purities obtained by titration can exceed 100% (because the functionality of the compound is measured). Therefore, the data for these two groups need to be dealt with in different ways. For purities based on titration we propose to use a rectangular distribution with a range from Pmin to 101%, an expected purity value which is the mean and a standard uncertainty of the purity u(P) of 29% of the range. Purities determined by chromatography can be described with a triangular distribution (ramp function). One leg of the triangle represents the range from Pmin to 100% and the rightangle is located at 100%. The expected value is the median and the uncertainty u(P) is 24% of the range. These proposals match the experimental data well.

 "A general problem for many commercially available chemicals, is the fact that their purity is often only stated as a minimum guaranteed purity (e.g., purity ≥97%) or as an indicative purity value (e.g., purity ∼99%). Usually, little or even no information is given regarding the uncertainty of the stated purity value and the methods used to determine the assigned purity value [4]. If a calibration solution is prepared from a chemical with an inaccurate or insufficiently stated purity value, one cannot use the solution to establish metrological traceability to the SI. "
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ABSTRACT: In this study the validity and suitability of differential scanning calorimetry (DSC) to determine the purity of selected polycyclic aromatic hydrocarbons and chloramphenicol has been investigated. The study materials were two candidate certified reference materials (CRMs), 6methylchrysene and benzo[a]pyrene, and two different batches of commercially available highly pure chloramphenicol. The DSC results were compared with those obtained by other methods, namely gas and liquid chromatography with mass spectrometric detection, liquid chromatography with diode array detection, and quantitative nuclear magnetic resonance. The purity results obtained by these different analytical methods confirm the wellknown challenges of comparing results of different methoddefined measurands. In comparison with other methods, DSC has a much narrower working range. This limits the applicability of DSC as purity determination method, for instance during the assignment of the purity value of a CRM. Nevertheless, this study showed that DSC can be a powerful technique to detect impurities that are structurally very similar to the main purity component. From this point of view, and because of its good repeatability, DSC can be considered as a valuable technique to investigate the homogeneity and stability of candidate purity CRMs.  [Show abstract] [Hide abstract]
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ABSTRACT: Ishikawa, or causeandeffect diagrams, help to visualize the parameters that influence a chromatographic analysis. Therefore, they facilitate the set up of the uncertainty budget of the analysis, which can then be expressed in mathematical form. If the uncertainty is calculated as the Gaussian sum of all uncertainty parameters, it is necessary to quantitate them all, a task that is usually not practical. The other possible approach is to use the intermediate precision as a base for the uncertainty calculation. In this case, it is at least necessary to consider the uncertainty of the purity of the reference material in addition to the precision data. The Ishikawa diagram is then very simple, and so is the uncertainty calculation. This advantage is given by the loss of information about the parameters that influence the measurement uncertainty.
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