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Identification of Flexographic-printed Newspapers with NIR Spectral Imaging
Abstract:Near-infrared (NIR) spectroscopy is a widely used method for material identification for laboratory and industrial applications. While standard spectrometers only allow measurements at one sampling point at a time, NIR Spectral Imaging techniques can measure, in real-time, both the size and shape of an object as well as identify the material the object is made of. The online classification and sorting of recovered paper with NIR Spectral Imaging (SI) is used with success in the paper recycling industry throughout Europe. Recently, the globalisation of the recycling material streams caused that water-based flexographic-printed newspapers mainly from UK and Italy appear also in central Europe. These flexo-printed newspapers are not sufficiently de-inkable with the standard de-inking process originally developed for offset-printed paper. This de-inking process removes the ink from recovered paper and is the fundamental processing step to produce high-quality paper from recovered paper. Thus, the flexo-printed newspapers are a growing problem for the recycling industry as they reduce the quality of the produced paper if their amount exceeds a certain limit within the recovered paper material. This paper presents the results of a research project for the development of an automated entry inspection system for recovered paper that was jointly conducted by CTR AG (Austria) and PTS Papiertechnische Stiftung (Germany). Within the project an NIR SI prototype for the identification of flexo-printed newspaper has been developed. The prototype can identify and sort out flexoprinted newspapers in real-time and achieves a detection accuracy for flexo-printed newspaper of over 95%. NIR SI, the technology the prototype is based on, allows the development of inspection systems for incoming goods in a paper production facility as well as industrial sorting systems for recovered paper in the recycling industry in the near future.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076584Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1162
 Kampf ums Altpapier, Papier+Technik, 01/2008, Dr. Curt Haefner- Verlag GmbH, Heidelberg.
 W. R. Johnson, D. W. Wilson, W. Fink, M. Humayun, G. Bearman, Snapshot hyperspectral imaging in ophthalmology, Journal of Biomedical Optics, Vol. 12 Issue 1, 014036, January/February 2007.
 A. Harvey, I. Abboud, A. Gorman, A. McNaught, S. Ramachandran, E. Theofanidou, Spectral Imaging of the Retina, SPIE Vol. 6047, 2006.
 A. Kulcke, C. Gurschler, G. Spck, R. Leitner, A. Kraft. On-line classification of synthetic polymers using near infrared spectral imaging. Journal of Near Infrared Spectroscopy, 11, p.71-81 (2003)
 R. Leitner, I. Ibraheem, A. Kercek. Spectral Imaging as a Modern Tool for Medical Diagnostics. In R. Leitner, editor, Spectral Imaging (Proc. Int.Workshop on Spectral Imaging), Austrian Computer Society, Vienna, pages 31-34, April 2003.
 C. Gurschler, G. Serafino, G. Spck, A. Del Bianco, M. Kraft and A. Kulcke. Spectral Imaging for the Classification of Natural and Artificial Turquoise Samples, Int. Conf. OPTO, p. 197, Erfurt (2002)
 F. van der Meer, S. M. De John (Eds.); Imaging Spectrometry: Basic Principles and Prospective Applications, Kluwer Academic Publishers (2002)
 G. H. Bearmann, R. M. Levenson, D. Cabib (Eds); Spectral Imaging: Basic Principles and Prospective Applications, Kluwer Academic Publishers (2002)
 D. A. Burns, E. W. Ciurczak; Handbook of Near-Infrared Analysis, Marcel Dekker, Inc., 2nd Ed. (2001)
 K. C. Lawrence, W. R. Windham, B. Park, R. J. Buhr; Hyperspectral Imaging for Poultry Contaminant Detection, NIR News 12(5) (2001)
 E. Pekalska and R.P.W. Duin, Classifiers for Dissimilarity-based Pattern Recognition, in: A. Sanfeliu, J.J. Villanueva, M. Vanrell, R. Alquezar, A.K. Jain, J. Kittler (eds.), ICPR15, Proc. 15th Int. Conference on Pattern Recognition (Barcelona, Spain, Sep.3-7), vol. 2, Pattern Recognition and Neural Networks, IEEE Computer Society Press, Los Alamitos, 2000, 12-16
 G. Polder, G. W. A. M. van der Heijden, I.T. Young; Hyperspectral Image Analysis for Measuring the Ripeness of Tomatoes, ASAE International Meeting, Paper No. 003089, Milwaukee, Wisconsin (2000)
 G. W. A. M. von der Heijden, G. Polder, T. Gevers; Comparison of multispectral images across the Internet, Proc. SPIE, 3964 (2000)
 N. Gat; Proc. SPIE, Imaging spectroscopy using tunable filters: a review, 4056, p. 50 (2000)
 R. D. Smith, M.P. Nelson, P.J. Treado, Raman chemical imaging using flexible fiberscope technology, Proc. SPIE, 3920, p. 14 (2000)
 Abbott, J.A., Quality Measurements of Fruits and Vegetables; Postharvest and biology technology, 15, 207-225 (1999)
 W. Wadsworth, J. P. Dybwad; Proc. SPIE, 3537, p. 54 (1999)
 T. Hyvarinen, E. Herrala, A. Dall-Ava; Direct sight imaging spectrograph: a unique add-on component brings spectral imaging to industrial applications, SPIE symposium on Electronic Imaging, 3302 (1998)
 T. Hyvarinen, E. Herrala, A. Dall-Ava; Proc SPIE, 3302, p. 165 (1998)
 M. F. Hopkins, Four-color pyrometry for metal emissivity characterization, Proc. SPIE, 2599, p. 294 (1995)
 C. L. Bennett, M. R. Carter, D. J. Fields, J. Hernandez; Imaging Fourier transform spectrometer, Proc. SPIE, 1937, p. 191 (1993)
 N. Gat; Spectrometer Apparatus, US Pat. 5166755 (1992)