Identification of Flexographic-printed Newspapers with NIR Spectral Imaging
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.
[1] Kampf ums Altpapier, Papier+Technik, 01/2008, Dr. Curt Haefner-
Verlag GmbH, Heidelberg.
[2] 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.
[3] A. Harvey, I. Abboud, A. Gorman, A. McNaught, S. Ramachandran, E.
Theofanidou, Spectral Imaging of the Retina, SPIE Vol. 6047, 2006.
[4] 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)
[5] 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.
[6] 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)
[7] F. van der Meer, S. M. De John (Eds.); Imaging Spectrometry: Basic
Principles and Prospective Applications, Kluwer Academic Publishers
(2002)
[8] G. H. Bearmann, R. M. Levenson, D. Cabib (Eds); Spectral Imaging:
Basic Principles and Prospective Applications, Kluwer Academic Publishers
(2002)
[9] D. A. Burns, E. W. Ciurczak; Handbook of Near-Infrared Analysis,
Marcel Dekker, Inc., 2nd Ed. (2001)
[10] K. C. Lawrence, W. R. Windham, B. Park, R. J. Buhr; Hyperspectral
Imaging for Poultry Contaminant Detection, NIR News 12(5) (2001)
[11] 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
[12] 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)
[13] G. W. A. M. von der Heijden, G. Polder, T. Gevers; Comparison of
multispectral images across the Internet, Proc. SPIE, 3964 (2000)
[14] N. Gat; Proc. SPIE, Imaging spectroscopy using tunable filters: a review,
4056, p. 50 (2000)
[15] R. D. Smith, M.P. Nelson, P.J. Treado, Raman chemical imaging using
flexible fiberscope technology, Proc. SPIE, 3920, p. 14 (2000)
[16] Abbott, J.A., Quality Measurements of Fruits and Vegetables; Postharvest
and biology technology, 15, 207-225 (1999)
[17] W. Wadsworth, J. P. Dybwad; Proc. SPIE, 3537, p. 54 (1999)
[18] 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)
[19] T. Hyvarinen, E. Herrala, A. Dall-Ava; Proc SPIE, 3302, p. 165 (1998)
[20] M. F. Hopkins, Four-color pyrometry for metal emissivity characterization,
Proc. SPIE, 2599, p. 294 (1995)
[21] C. L. Bennett, M. R. Carter, D. J. Fields, J. Hernandez; Imaging Fourier
transform spectrometer, Proc. SPIE, 1937, p. 191 (1993)
[22] N. Gat; Spectrometer Apparatus, US Pat. 5166755 (1992)
[1] Kampf ums Altpapier, Papier+Technik, 01/2008, Dr. Curt Haefner-
Verlag GmbH, Heidelberg.
[2] 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.
[3] A. Harvey, I. Abboud, A. Gorman, A. McNaught, S. Ramachandran, E.
Theofanidou, Spectral Imaging of the Retina, SPIE Vol. 6047, 2006.
[4] 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)
[5] 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.
[6] 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)
[7] F. van der Meer, S. M. De John (Eds.); Imaging Spectrometry: Basic
Principles and Prospective Applications, Kluwer Academic Publishers
(2002)
[8] G. H. Bearmann, R. M. Levenson, D. Cabib (Eds); Spectral Imaging:
Basic Principles and Prospective Applications, Kluwer Academic Publishers
(2002)
[9] D. A. Burns, E. W. Ciurczak; Handbook of Near-Infrared Analysis,
Marcel Dekker, Inc., 2nd Ed. (2001)
[10] K. C. Lawrence, W. R. Windham, B. Park, R. J. Buhr; Hyperspectral
Imaging for Poultry Contaminant Detection, NIR News 12(5) (2001)
[11] 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
[12] 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)
[13] G. W. A. M. von der Heijden, G. Polder, T. Gevers; Comparison of
multispectral images across the Internet, Proc. SPIE, 3964 (2000)
[14] N. Gat; Proc. SPIE, Imaging spectroscopy using tunable filters: a review,
4056, p. 50 (2000)
[15] R. D. Smith, M.P. Nelson, P.J. Treado, Raman chemical imaging using
flexible fiberscope technology, Proc. SPIE, 3920, p. 14 (2000)
[16] Abbott, J.A., Quality Measurements of Fruits and Vegetables; Postharvest
and biology technology, 15, 207-225 (1999)
[17] W. Wadsworth, J. P. Dybwad; Proc. SPIE, 3537, p. 54 (1999)
[18] 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)
[19] T. Hyvarinen, E. Herrala, A. Dall-Ava; Proc SPIE, 3302, p. 165 (1998)
[20] M. F. Hopkins, Four-color pyrometry for metal emissivity characterization,
Proc. SPIE, 2599, p. 294 (1995)
[21] C. L. Bennett, M. R. Carter, D. J. Fields, J. Hernandez; Imaging Fourier
transform spectrometer, Proc. SPIE, 1937, p. 191 (1993)
[22] N. Gat; Spectrometer Apparatus, US Pat. 5166755 (1992)
@article{"International Journal of Information, Control and Computer Sciences:60108", author = "Raimund Leitner and Susanne Rosskopf", title = "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.", keywords = "spectral imaging, imaging spectroscopy, NIR, waterbasedflexographic, flexo-printed, recovered paper, real-time classification.", volume = "2", number = "8", pages = "2770-6", }