Hyperspectral Image Analysis of Food Quality

Résumé fourni par la source

Assessing the quality of food is a vital step in any food processing line to ensurethe best food quality and maximum profit for the farmer and food manufacturer.Traditional quality evaluation methods are often destructive and labourintensive procedures relying on wet chemistry or subjective human inspection.Near-infrared spectroscopy can address these issues by offering a fast and objectiveanalysis of the food quality. A natural extension to these single spectrumNIR systems is to include image information such that each pixel holds a NIRspectrum. This augmented image information offers several extensions to the analysis offood quality. This dissertation is concerned with hyperspectral image analysisused to assess the quality of single grain kernels. The focus is to highlight thebenefits and challenges of using hyperspectral imaging for food quality presentedin two research directions. Initially, the visualisation and interpretation of hyperspectral images are discussed.A Bayesian based unmixing method is presented as a novel approachto decompose a hyperspectral image into interpretable components. Secondly,hyperspectral imaging is applied to a dedicated application of predicting the degreeof pre-germination in single barley kernels using a customised classificationframework. Both contributions serve to illustrate the improvement of addingimage information to NIR systems in food quality assessment applications

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