Filtern
Erscheinungsjahr
- 2023 (2)
Dokumenttyp
Sprache
- Englisch (1)
- Mehrsprachig (1)
Schlagworte
- Classification (1)
- Deep Learning (1)
- Fractography (1)
- Metrology (1)
- Mycotoxin Analysis (1)
- Quality Assurance (1)
- Sustainable Food Safety (1)
Organisationseinheit der BAM
In failure analysis, micro-fractographic analysis of fracture surfaces is usually performed based on practical knowledge which is gained from available studies, own comparative tests, from the literature, as well as online databases. Based on comparisons with already existing images, fracture mechanisms are determined qualitatively. These images are mostly two-dimensional and obtained by light optical and scanning electron imaging techniques. So far, quantitative assessments have been limited to macrocopically determined percentages of fracture types or to the manual measurement of fatigue striations, for example. Recently, more and more approaches relying on computer algorithms have been taken, with algorithms capable of finding and classifying differently structured fracture characteristics. For the Industrial Collective Research (Industrielle Gemeinschaftsforschung, IGF) project “iFrakto” presented in this paper, electron-optical images are obtained, from which topographic information is calculated. This topographic information is analyzed together with the conventional 2D images. Analytical algorithms and deep learning are used to analyze and evaluate fracture characteristics and are linked to information from a fractography database. The most important aim is to provide software aiding in the application of fractography for failure analysis. This paper will present some first results of the project.
Demonstrating competency and equivalence for the measurement capacity of contaminants and nutrients in primary foodstuffs is a priority of the OAWG 10-year strategy for Track A core comparisons. Such measurements have posed significant challenges for reference material producers and calibration service providers. This key comparison (KC), under the topic of “non- polar analyte in high carbohydrate food matrix: trans-Zearalenone (trans-ZEN) in maize powder” , was a sector of the model system selected to align with this class within the OAWG strategy. Evidence of successful participation in formal, relevant international comparisons is needed to demonstrate the Calibration and Measurement Capabilities (CMCs) of national metrology institutes (NMIs) and designated institutes (DIs).
17 NMIs and DIs participated in the Track A KC CCQM- 168 “non-polar analyte in high carbohydrate food matrix: trans-ZEN in maize powder” . Participants were requested to evaluate the mass fraction (μg/kg) of trans-ZEN in maize powder material. Methods like liquid-liquid extraction and SPE were applied in the pre-treatment, and HPLC-MS/MS and HPLC-FLD were used for detection by the participants. The mass fractions for trans-ZEN were in the range of (91.8 to 169) μg/kg with standard uncertainties of (1.5 to 24.7) μg/kg, and corresponding relative standard uncertainties from 1.5% to 14.6%. Two labs, INTI and BAM were excluded from the KCRV evaluation. INTI result was identified as an outlier and confirmed their method had insufficient specificity. For BAM the calibration approach they used does not meet the CIPM traceability requirements. The other 15 labs included in the calculation of the consensus KCRV all agreed within their standard uncertainties. Hierarchical Bayes was used as estimators in calculating KCRV and standard uncertainty.
Successful participation in CCQM-K168 demonstrates the measurement capabilities in determining mass fraction of organic compounds, with molecular mass of 100 g/mol to 500 g/mol, having low polarity pKow < -2, in mass fraction range from 1 μg/kg to 1000 μg/kg in a high carbohydrate food matrix.