Paper
13 May 2010 Hyperspectral imaging for diagnosis and quality control in agri-food and industrial sectors
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Abstract
Optical spectroscopy has been utilized in various fields of science, industry and medicine, since each substance is discernible from all others by its spectral properties. However, optical spectroscopy traditionally generates information on the bulk properties of the whole sample, and mainly in the agri-food industry some product properties result from the heterogeneity in its composition. This monitoring is considerably more challenging and can be successfully achieved by the so-called hyperspectral imaging technology, which allows the simultaneous determination of the optical spectrum and the spatial location of an object in a surface. In addition, it is a nonintrusive and non-contact technique which gives rise to a great potential for industrial applications and it does not require any particular preparation of the samples, which is a primary concern in food monitoring. This work illustrates an overview of approaches based on this technology to address different problems in agri-food and industrial sectors. The hyperspectral system was originally designed and tested for raw material on-line discrimination, which is a key factor in the input stages of many industrial sectors. The combination of the acquisition of the spectral information across transversal lines while materials are being transported on a conveyor belt, and appropriate image analyses have been successfully validated in the tobacco industry. Lastly, the use of imaging spectroscopy applied to online welding quality monitoring is discussed and compared with traditional spectroscopic approaches in this regard.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
P. Beatriz García-Allende, Olga M. Conde, Jesus Mirapeix, Adolfo Cobo, and Jose M. Lopez-Higuera "Hyperspectral imaging for diagnosis and quality control in agri-food and industrial sectors", Proc. SPIE 7726, Optical Sensing and Detection, 77260E (13 May 2010); https://doi.org/10.1117/12.854506
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KEYWORDS
Hyperspectral imaging

Plasma

Raw materials

Imaging spectroscopy

Imaging systems

Cameras

Principal component analysis

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