Abstract
The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.
Zusammenfassung
Mit Hilfe von chemometrischen Kalibrierungsmodellen können verschiedene Qualitäts- und Reifeparameter für Agrarprodukte aus Nahinfrarotspektren geschätzt werden. Die verwendeten Trainingsdaten bestimmen dabei die Güte des chemometrischen Kalibrierungsmodells. Für das Training wird deshalb ein Datensatz benötigt, welcher Proben im gesamten Parameterraum beinhaltet. In der Regel wird ein Versuchsplan zur Probennahme erstellt, jedoch können viele Parameter in der Herstellung von Agrarprodukten nicht eingestellt werden. Daher muss in der Regel eine große Menge an Proben gesammelt werden, wobei häufig zahlreiche Proben den Informationsgehalt des Datensatzes nicht erhöhen. Des Weiteren müssen die Qualitäts- und Reifeparameter der Proben im Trainingsdatensatz aufwändig durch chemische Referenzanalysen erstellt werden. Die vorgestellten aktiven Lernansätze dienen einer optimalen Probenauswahl anhand von Nahinfrarotspektren, wodurch sich die Zahl der benötigten Proben den damit verbundenen Referenzanalysen verringert.
Funding source: Deutsche Forschungsgemeinschaft
Award Identifier / Grant number: 390732324
Funding source: Bundesministerium für Bildung und Forschung
Award Identifier / Grant number: 01|S17047
Funding statement: The authors of this work were supported by the Fraunhofer Center for Machine Learning within the Fraunhofer Cluster for Cognitive Internet Technologies (CCIT) and the PhenoRob project which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2070 – 390732324, and by the German Federal Ministry of Education and Research (BMBF) within the context of the Software Campus project SmartSpectrometer under grant No. 01|S17047.
About the authors
Julius Krause recived his M.Sc. degree in physics in 2016 from the Karlsruhe Institute of Technology (KIT). Since 2016, his research has taken place at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB in cooperation with the Vision and Fusion Laboratory at the Karlsruhe Institute of Technology. His research interests are hyperspectral signal processing and imaging for optical inspection and quality control, and machine learning.
Maurice Günder recieved his M.Sc. degree in Experimental Particle Physics at RWTH Aachen University in Aachen, Germany. Since 2020, he has been a Data Scientist at the Fraunhofer Insitute for Intelligent Analysis and Information System IAIS in Sankt Augustin, Germany, while pursuing the PhD degree at the University of Bonn, Germany. His research interests comprise time series analysis, knowledge extraction from sensorical data, and inclusion of expert knowledge in machine learning processes.
Daniel Schulz studied geography, geology and soil science at the universities of Cologne, Bonn and Gothenburg. After his graduation he began his work at the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Sankt Ausgutin. There he worked as a project manager in various large-scale projects with industry and public clients. His research focuses on Machine Learning (Informmed Machine Learning) and Artificial Intelligence. Currently, he heads the office of the Fraunhofer Research Center for Machine Learning.
Robin Gruna obtained his PhD from the Karlsruhe Institute of Technology (KIT) in the field of Machine Vision and Computational Imaging. Currently, he is the research group manager at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB in Karlsruhe. His research interests include machine learning, hyperspectral imaging and spectral sensing.
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