New active learning algorithms for near-infrared spectroscopy in agricultural applications
| Autor: | Julius Krause, Daniel Schulz, Maurice Günder, Robin Gruna |
|---|---|
| Přispěvatelé: | Publica |
| Rok vydání: | 2021 |
| Předmět: |
near infrared spectroscopy
Computer science Active learning (machine learning) 010401 analytical chemistry Near-infrared spectroscopy Active Learning 02 engineering and technology sample selection aktives Lernen 021001 nanoscience & nanotechnology 01 natural sciences Versuchsplanung 0104 chemical sciences Computer Science Applications Nahinfrarotspektroskopie Control and Systems Engineering Electrical and Electronic Engineering 0210 nano-technology Remote sensing |
| Zdroj: | at - Automatisierungstechnik. 69:297-306 |
| ISSN: | 2196-677X 0178-2312 |
| DOI: | 10.1515/auto-2020-0143 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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