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An international intercomparison involving eight national metrology institutes (NMIs) was conducted to establish their current measurement capabilities for determining five selected congeners from the brominated flame retardant classes polybrominated diphenyl ethers and polybrominated biphenyls. A candidate reference material consisting of polypropylene fortified with technical mixtures of penta-, octa- and decabromo diphenyl ether and decabromo biphenyl, which was thoroughly assessed for material homogeneity and stability, was used as study material. The analytical procedures applied by the participants differed with regard to sample pre-treatment, extraction, clean-up, employed calibrants and type of calibration procedure as well as regarding analytical methods used for separation, identification and quantification of the flame retardant congeners (gas chromatography coupled to an electron capture detector (GC-ECD), gas chromatography-mass spectrometry in the electron ionisation mode (GC-EI-MS), gas chromatography-mass spectrometry in the electron capture negative ionisation mode (GC-ECNI-MS), and liquid chromatography-inductive coupled plasma-mass spectrometry (LC-ICP-MS)). The laboratory means agreed well with relative standard deviations of the mean of means of 1.9%, 4.8%, 5.5% and 5.4% for brominated diphenyl ether (BDE) 47, 183 and 209 and for the brominated biphenyl (BB) congener 209, respectively. For BDE 206, a relative standard deviation of 28.5% was obtained. For all five congeners, within-laboratory relative standard deviations of six measurements obtained under intermediate precision conditions were between 1% and 10%, and reported expanded measurements uncertainties typically ranged from 4% to 10% (8% to 14% for BDE 206). Furthermore, the results are in good agreement with those obtained in the characterization exercise for determining certified values for the flame retardant congeners in the same material. The results demonstrate the state-of-the-art measurement capabilities of NMIs for quantifying representative BDE congeners and BB 209 in a polymer. The outcome of this intercomparison (pilot study) in conjunction with possible improvements for employing exclusively calibrants with thoroughly assessed purity suggests that a key comparison aiming at underpinning calibration and measurement capability (CMC) claims of NMIs can be conducted.
The key comparison CCQM-K102: Polybrominated diphenyl ethers in sediment was coordinated by the JRC, Directorate F - Health, Consumers & Reference Materials, Geel (Belgium) under the auspices of the Organic Analysis Working Group (OAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM). Thirteen National Metrology institutes or Designated Institutes and the JRC participated. Participants were requested to report the mass fraction (on a dry mass basis) of BDE 47, 99 and 153 in the freshwater sediment study material. The sediment originated from a river in Belgium and contained PBDEs (and other pollutants) at levels commonly found in environmental samples. The comparison was designed to demonstrate participants' capability of analysing non-polar organic molecules in abiotic dried matrices (approximate range of molecular weights: 100 to 800 g/mol, polarity corresponding to pKow < −2, range of mass fraction: 1–1000 μg/kg). All participants (except one using ultrasonic extraction) applied Pressurised Liquid Extraction or Soxhlet, while the instrumental analysis was performed with GC-MS/MS, GC-MS or GC-HRMS. Isotope Dilution Mass Spectrometry approach was used for quantification (except in one case). The assigned Key Comparison Reference Values (KCRVs) were the medians of thirteen results for BDE 47 and eleven results for BDE 99 and 153, respectively. BDE 47 was assigned a KCRV of 15.60 μg/kg with a combined standard uncertainty of 0.41 μg/kg, BDE 99 was assigned a KCRV of 33.69 μg/kg with a combined standard uncertainty of 0.81 μg/kg and BDE 153 was assigned a KCRV of 6.28 μg/kg with a combined standard uncertainty of 0.28 μg/kg. The k-factor for the estimation of the expanded uncertainty of the KCRVs was chosen as k = 2.
During the last decades, X-ray (micro-)computed tomography has gained increasing attention for the description of porous skeletal and shell structures of various organism groups. However, their quantitative analysis is often hampered by the difficulty to discriminate cavities and pores within the object from the surrounding region. Herein, we test the ambient occlusion (AO) algorithm and newly implemented optimisations for the segmentation of cavities (implemented in the software Amira). The segmentation accuracy is evaluated as a function of (i) changes in the ray length input variable, and (ii) the usage of AO (scalar) field and other AO-derived (scalar) fields. The results clearly indicate that the AO field itself outperforms all other AO-derived fields in terms of segmentation accuracy and robustness against variations in the ray length input variable. The newly implemented optimisations improved the AO field-based segmentation only slightly, while the segmentations based on the AOderived fields improved considerably.
Additionally, we evaluated the potential of the AO field and AO-derived fields for the separation and classification of cavities as well as skeletal structures by comparing them with commonly used distance-map-based segmentations. For this, we tested the zooid separation within a bryozoan colony, the stereom classification of an ophiuroid tooth, the separation of bioerosion traces within a marble block and the calice (central cavity)-pore separation within a dendrophyllid coral. The obtained results clearly indicate that the ideal input field depends on the three-dimensional morphology of the object of interest. The segmentations based on the AO-derived fields often provided cavity separations and skeleton classifications that were superior to or impossible to obtain with commonly used distance-map-based segmentations. The combined usage of various AO-derived fields by supervised or unsupervised segmentation algorithms might provide a promising target for future research to further improve the results for this kind of high-end data segmentation and classification. Furthermore, the application of the developed segmentation algorithm is not restricted to X-ray (micro-)computed tomographic data but may potentially be useful for the segmentation of 3D volume data from other sources.