Objective
The EU finfish industry should deliver high quality and safe products to the marketplace to meet the expectation of consumers. Appearance, nutrition, texture, freshness, and spoilage are the most important quality and safety attributes of fish products. Quality inspection of fish is typically carried out manually on candling tables by human visual detection, which is very labour intensive, and many quality and safety attributes are invisible to the inspectors. It is also subjective and dependent on the experience and criteria of the inspectors. Available traditional instrumental measurements are destructive, expensive, time-consuming and inefficient. Moreover, they cannot provide spatial distribution information of desired attributes, critical for evaluating the quality and safety of fish products.
In this project, hyperspectral imaging (HSI) technology will be applied for quantitative and non-invasive measurement and visualisation of quality and safety of finfish fillets. Combining the main advantages of spectroscopy and computer vision, HSI can simultaneously acquire spectral and spatial information in one single system, enabling it to measure both external physical and morphological characteristics and internal quality attributes from a finfish product. A reliable HSI system with numerous quantitative models will be trained, established, and validated for rapid and non-invasive measurement of quality and safety attributes of finfish fillets based on the most critical image features extracted from visible and invisible (near infrared) hyperspectral images of finfish fillets and their reference attribute values, which will be measured by using traditional instruments and sensory analysis.
This development brings a major advance to the EU finfish industry for ensuring the safe processing of finfish products and the correct labelling of products in relation to quality, safety, authenticity and compliance.
Fields of science
Topic(s)
Call for proposal
FP7-SME-2013
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Funding Scheme
BSG-SME - Research for SMEsCoordinator
08940 Cornella De Llobregat
Spain
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Participants (7)
9850 Hirtshals
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TF2 7QZ Telford
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36202 Vigo Pontevedra
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28015 Madrid
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90570 Oulu
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4 Dublin
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28006 Madrid
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