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NANOTECHNOLOGY IN THE FOOD CHAIN - Favv

NANOTECHNOLOGY IN THE FOOD CHAIN - Favv

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Nano-electronics: technological opportunities for applications in the food chain Kris Van de Voorde 1 , Steven Van Campenhout 2 , Johan Duré 2 & Klaas Tack 1 1 imec: Kapeldreef 75, B-3001 Leuven 2 Flanders’ FOOD: Kunstlaan 43, B-1040 Brussel E-mail: vdvoorde@imec.be; Steven.VanCampenhout@flandersfood.com As Europe's largest independent research centre in nano-electronics, imec has a strong contribution in the development of deep-submicron CMOS and post- CMOS processes and technologies. But, there’s more to nanotechnology than IC processing alone. Indeed imec is entering the fascinating nano world of biology. In that framework, groundbreaking research is oriented towards nano materials (particles, nanowires…) and nano devices (NEMS, image sensors…), bioelectronic applications (biosensors, neurons-on-chip…) and organic electronics. An example of these technological developments is hyperspectral imaging. In an IWT-SBO project named CHAMELEON, imec is designing the next generation vision system that exploits the benefits of spectral information. The goal is to enable and demonstrate flexible, but domain-specific hyperspectral imaging systems for relevant industrial applications in food. Traditionally, hyperspectral imaging systems make use of 2D arrays in the 400- 1000 nm range, while non-destructive quality assessment of food and agricultural products is typically done with NIR spectroscopy in the 1000-2500 nm range. Therefore, the potential of hyperspectral imaging in the NIR for rapid and non-destructive assessment of quality properties (e.g. sugar, starch and water distribution in fruit, presence of foreign substances in grain, fat content in meat,…) will be investigated. An industrial fruit grading case is currently being explored. For this application there is a great interest for quality based sorting, but a first challenge is the high image capturing and data processing speed which is required. The current throughput of commercial grading lines is 10 fruits per second per lane. Current imaging systems were found not to be suited to achieve this throughput. Hence, 131

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