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Detection of pyrrolizidine alkaloid containing herbs using hyperspectral imaging in the short-wave infrared

Detection of pyrrolizidine alkaloids using hyperspectral imaging in the short-wave infrared
 
: Krause, Julius; Tron, Nanina; Maier, Georg; Gruna, Robin; Krähmer, Andrea

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Abstract urn:nbn:de:0011-n-6336668 (201 KByte PDF)
MD5 Fingerprint: 831bde64542d45120764732e50469f91
Created on: 9.4.2021


Beyerer, Jürgen; Längle, Thomas ; Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung -IOSB-, Karlsruhe; Karlsruher Institut für Technologie -KIT-:
OCM 2021, 5th International Conference on Optical Characterization of Materials : March 17th - 18th, 2021; Karlsruhe, Germany, Online event
Karlsruhe: KIT Scientific Publishing, 2021
ISBN: 978-3-7315-1081-9
DOI: 10.5445/KSP/1000128686
pp.45-55
International Conference on Optical Characterization of Materials (OCM) <5, 2021, Online>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
hyperspectral imaging; convolutional neural network; Pyrrolizidine alkaloids; sensor-based sorting

Abstract
Plants containing pyrrolizidine alkaloids (PA) are unwanted contaminants in consumer products such as herbal tea due to their toxicity to humans. The detection of these plants or their components using hyperspectral imaging was investigated, with focus on application in sensor-based sorting. For this, 431hyperspectral images of leafs from three common herbs (peppermint, lemon balm, stinging nettle) and the poisonous common groundsel were acquired. By using a convolutional neural network, a mean F1 score of 0.89 was obtained for the classification of all four plant products based on the individual spectra. To validate the neural network, significant wavelengths were determined and visualized in an attribution map.

: http://publica.fraunhofer.de/documents/N-633666.html