TY - JOUR A1 - Westwood, S. A1 - Martos, G. A1 - Josephs, R. A1 - Choteau, T. A1 - Wielgosz, R. A1 - Davies, S. A1 - Moawad, M. A1 - Tarrant, G. A1 - Chan, B. A1 - Alamgir, M. A1 - de Rego, E. A1 - Wollinger, W. A1 - Garrido, B. A1 - Fernandes, J. A1 - de Sena, R. A1 - Oliveira, R. A1 - Melanson, J. A1 - Bates, J. A1 - Mai Le, P. A1 - Meija, J. A1 - Quan, C. A1 - Huang, T. A1 - Zhang, W. A1 - Ma, R. A1 - Zhang, S. A1 - Hao, Y. A1 - He, Y. A1 - Song, S. A1 - Wang, H. A1 - Su, F. A1 - Zhang, T. A1 - Li, H. A1 - Lam, W. A1 - Wong, W. A1 - Fung, W. A1 - Philipp, Rosemarie A1 - Dorgerloh, Ute A1 - Meyer, Klas A1 - Piechotta, Christian A1 - Riedel, Juliane A1 - Westphalen, Tanja A1 - Giannikopoulou, P. A1 - Alexopoulos, Ch. A1 - Kakoulides, E. A1 - Kitamaki, Y. A1 - Yamazaki, T. A1 - Shimizu, Y. A1 - Kuroe, M. A1 - Numata, M. A1 - Pérez-Castorena, A. A1 - Balderas-Escamilla, M. A1 - Garcia-Escalante, J. A1 - Krylov, A. A1 - Mikheeva, A. A1 - Beliakov, M. A1 - Palagina, M. A1 - Tkachenko, I. A1 - Spirin, S. A1 - Smirnov, V. A1 - Tang Lin, T. A1 - Pui Sze, C. A1 - Juan, W. A1 - Lingkai, W. A1 - Ting, L. A1 - Quinde, L. A1 - Yizhao, C. A1 - Lay Peng, S. A1 - Fernandes-Whaley, M. A1 - Prevoo-Franzsen, D. A1 - Quinn, L. A1 - Nhlapo, N. A1 - Mkhize, D. A1 - Marajh, D. A1 - Chamane, S. A1 - Ahn, S. A1 - Choi, K. A1 - Lee, S. A1 - Han, J. A1 - Baek, S. A1 - Kim, B. A1 - Marbumrung, S. A1 - Jongmesuk, P. A1 - Shearman, K. A1 - Boonyakong, C. A1 - Bilsel, M. A1 - Gündüz, S. A1 - Ün, I. A1 - Yilmaz, H. A1 - Bilsel, G. A1 - Gökçen, T. A1 - Clarkson, C. A1 - Warren, J. A1 - Achtar, E. T1 - Mass fraction assignment of Bisphenol-A high purity material N2 - The CCQM-K148.a comparison was coordinated by the BIPM on behalf of the CCQM Organic Analysis Working Group for NMIs and DIs which provide measurement services in organic analysis under the CIPM MRA. It was undertaken as a "Track A" comparison within the OAWG strategic plan. CCQM-K148.a demonstrates capabilities for assigning the mass fraction content of a solid organic compound having moderate molecular complexity, where the compound has a molar mass in the range (75 - 500) g/mol and is non-polar (pKow < −2), when present as the primary organic component in a neat organic solid and where the mass fraction content of the primary component in the material is in excess of 950 mg/g. Participants were required to report the mass fraction of Bisphenol A present in one supplied unit of the comparison material. Participants using a mass balance method for the assignment were also required to report their assignments of the impurity components present in the material. Methods used by the seventeen participating NMIs or DIs were predominantly based on either stand-alone mass balance (summation of impurities) or qNMR approaches, or the combination of data obtained using both methods. The results obtained using thermal methods based on freezing-point depression methods were also reported by a limited number of participants. There was excellent agreement between assignments obtained using all three approaches to assign the BPA content. The assignment of the values for the mass fraction content of BPA consistent with the KCRV was achieved by most of the comparison participants with an associated relative standard uncertainty in the assigned value in the range (0.1 - 0.5)%. KW - Bisphenol-A KW - Purity assessment KW - Interlaboratory key comparison KW - Metrology PY - 2021 DO - https://doi.org/10.1088/0026-1394/58/1A/08015 VL - 58 IS - 1A SP - 08015 PB - IOP Publishing AN - OPUS4-54188 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fricke, F. A1 - Mahmood, S. A1 - Hoffmann, J. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, S. A1 - Westerdick, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy N2 - Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control. T2 - 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) CY - Grenoble, France DA - 01.02.2021 KW - Industry 4.0, KW - Cyber-physical systems KW - Artificial neural networks KW - Mass spectrometry KW - Nuclear magnetic resonance spectroscopy PY - 2021 DO - https://doi.org/10.23919/DATE51398.2021.9473958 SP - 615 EP - 620 PB - IEEE AN - OPUS4-55360 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fricke, F. A1 - Mahmood, S. A1 - Hoffmann, J. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, Stefan A1 - Westerdicky, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy N2 - Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control. T2 - 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) CY - Online meeting DA - 01.02.2021 KW - Industry 4.0 KW - Cyber-Physical Systems KW - Artificial Neural Networks KW - Mass Spectrometry KW - Nuclear Magnetic Resonance Spectroscopy PY - 2021 UR - www.date-conference.com SN - 978-3-9819263-5-4 SP - 615 EP - 620 PB - Research Publishing AN - OPUS4-52180 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -