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Hyperspectral imaging - from laboratory to in-line food sorting

: Gruna, R.; Vieth, K.-U.; Michelsburg, M.; Puente Leon, F.

Postprint urn:nbn:de:0011-n-1514908 (2.4 MByte PDF)
MD5 Fingerprint: 5141c4e4ce5592289fee7dcc888fcad5
Erstellt am: 2.2.2011

2nd International Workshop on Image Analysis in Agriculture 2010. CD-ROM : CIGR Workshop, Budapest, Hungary, 26-27 August 2010
Budapest, 2010
ISBN: 978-963-503-417-8
International Workshop on Image Analysis in Agriculture <2, 2010, Budapest>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
near-infrared hyperspectral imaging; food sorting; quality inspection; feature selection; band selection; Germany; kcm

Perfect quality control of food becomes more and more a matter of course. Foreign materialslike pieces of rocks, insects, plastics, carton etc. have to be identified and sorted out. For many years, near-infrared spectroscopy has been a widely-used analytical offline method for quality inspection for small samples in the food and agriculture industry. Up to now, high-speed automatic sorting machines work usually on the basis of visual inspection, not near-infrared inspection. This article describes how an in-line food sorting system can be developed on the basis of hyperspectral imaging data. We focus on analyzing the vast amount of data to yield minimum band selection with optimal classification results required for an industrial sorter.