TY - JOUR A1 - Pauw, Brian Richard A1 - Smales, Glen Jacob A1 - Anker, A. S. A1 - Annadurai, V. A1 - Balazs, D. M. A1 - Bienert, Ralf A1 - Bouwman, W. G. A1 - Breßler, Ingo A1 - Breternitz, J. A1 - Brok, E. S. A1 - Bryant, G. A1 - Clulow, A. J. A1 - Crater, E. R. A1 - De Geuser, F. A1 - Del Giudice, A. A1 - Deumer, J. A1 - Disch, S. A1 - Dutt, S. A1 - Frank, K. A1 - Fratini, E. A1 - Garcia, P. R. A. F. A1 - Gilbert, E. P. A1 - Hahn, Marc Benjamin A1 - Hallett, J. A1 - Hohenschutz, M. A1 - Hollamby, M. A1 - Huband, S. A1 - Ilavsky, J. A1 - Jochum, J. K. A1 - Juelsholt, M. A1 - Mansel, B. W. A1 - Penttilä, P. A1 - Pittkowski, R. K. A1 - Portale, G. A1 - Pozzo, L. D. A1 - Rochels, L. A1 - Rosalie, Julian A1 - Saloga, Patrick E. J. A1 - Seibt, S. A1 - Smith, A. J. A1 - Smith, G. N. A1 - Spiering, G. A. A1 - Stawski, Tomasz M. A1 - Taché, O. A1 - Thünemann, Andreas A1 - Toth, K. A1 - Whitten, A. E. A1 - Wuttke, J. T1 - The human factor - Results of a small-angle scattering data analysis round robin N2 - A round-robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions and two from powders. Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5% and half of the population width entries within 40%. Due to the added complexity of the structure factor, far fewer people submitted answers on the powder datasets. For those that did, half of the entries for the means and widths were within 44 and 86%, respectively. This round-robin experiment highlights several causes for the discrepancies, for which solutions are proposed. KW - Round Robin KW - Data analysis KW - Small-angle scattering KW - Nanomaterials KW - Interlaboratory comparability KW - Nanostructure quantification KW - Methodology KW - MOUSE PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-587091 DO - https://doi.org/10.1107/S1600576723008324 SN - 1600-5767 VL - 56 IS - 6 SP - 1618 EP - 1629 PB - Wiley-Blackwell CY - Oxford AN - OPUS4-58709 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fink, Friedrich A1 - Stawski, Tomasz M. A1 - Emmerling, Franziska A1 - Falkenhagen, Jana T1 - A novel machine-learning approach to unlock technical lignin classification by NIR spectroscopy - bench to handheld N2 - In this research, the utilization of near-infrared (NIR) spectroscopy in conjunction with advanced machine learning methods is investigated for categorizing technical lignins obtained from different biomass sources and industrial procedures. Technical lignins, such as kraft, organosolv and lignosulfonates, have different chemical compositions, which continue to make uniform characterization and application in sustainable sectors extremely difficult. Fast, universally accessible analytics combined with data analysis is still an open question. For the first time three distinct NIR spectrometers—a high-performance benchtop system, a mid-priced compact device, and an economical handheld unit—were utilized to record NIR spectra of 31 unique lignin samples. The spectra underwent pre-processing through standard normal variate (SNV) transformation and Savitzky-Golay derivatives to amplify spectral features and decrease noise. Principal component analysis (PCA) was employed to reduce data complexity and extract crucial characteristics for classification purposes. Subsequently, four machine learning algorithms—Support Vector Machines (SVM), Gaussian Naive Bayes (GNB), Gaussian Process Classification (GPC), and Decision Tree Classification (DTC)—were implemented for the classification of the lignin samples. The DTC model exhibited the highest accuracy among them across different spectrometers. Although the benchtop spectrometer produced the most precise outcomes, the compact NeoSpectra system also displayed potential as a cost-efficient option. Nonetheless, the restricted spectral coverage of the handheld NIRONE spectrometer resulted in reduced classification accuracy. Our discoveries highlight the capability of NIR spectroscopy, combined with robust data analysis techniques, for the swift and non-destructive classification of technical lignins, facilitating their improved utilization in sustainable fields. KW - Technical lignins KW - NIR spectroscopy KW - Classification KW - Machine-learning KW - PCA PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-634518 DO - https://doi.org/10.1016/j.chemolab.2025.105467 SN - 0169-7439 VL - 264 SP - 1 EP - 10 PB - Elsevier B.V. CY - Amsterdam AN - OPUS4-63451 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -