Foreign Object Detection in Multispectral X-ray Images of Food Items Using Sparse Discriminant Analysis Gudmundur Einarsson 1(B ) , Janus N. Jensen 1 , Rasmus R. Paulsen 1 , Hildur Einarsdottir 1 , Bjarne K. Ersbøll 1 , Anders B. Dahl 1 , and Lars Bager Christensen 2 1 DTU Compute, Technical University of Denmark, Richard Petersens Plads, Building 324, 2800 Kongens Lyngby, Denmark {guei,jnje,rapa,hildr,bker,abda}@dtu.dk 2 Teknologisk Institut, Gregersensvej 9, 2630 Taastrup, Denmark lbc@teknologisk.dk http://www.compute.dtu.dk/english http://www.teknologisk.dk Abstract. Non-invasive food inspection and quality assurance are becoming viable techniques in food production due to the introduction of fast and accessible multispectral X-ray scanners. However, the novel devices produce massive amount of data and there is a need for fast and accurate algorithms for processing it. We apply a sparse classifier for foreign object detection and segmentation in multispectral X-ray. Using sparse methods makes it possible to potentially use fewer variables than traditional methods and thereby reduce acquisition time, data volume and classification speed. We report our results on two datasets with for- eign objects, one set with spring rolls and one with minced meat. Our results indicate that it is possible to limit the amount of data stored to 50% of the original size without affecting classification accuracy of materials used for training. The method has attractive computational properties, which allows for fast classification of items in new images. Keywords: X-ray · Multispectral · Sparse classification · Foreign object detection 1 Introduction One of the many purposes of X-ray scanning is to provide quality control and assurance in food production industry. The usage of X-rays provides non- destructive means of examining food items and the data can be used to verify that the content is free of anomalies or foreign objects. The usage of multispectral X-ray scanning has been used successfully in detecting explosives [12], and com- pares well to an X-ray dual-energy sandwich detector [8]. Foreign objects found in food items consist mostly of insects, wood chips, stone pebbles, sand/dust c  Springer International Publishing AG 2017 P. Sharma and F.M. Bianchi (Eds.): SCIA 2017, Part I, LNCS 10269, pp. 350–361, 2017. DOI: 10.1007/978-3-319-59126-1 29