Real-time detection of flame-retardant additives in polymers and polymer blends with NIR imaging spectroscopy

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7312, Issue , 2009, Pages

  Leitner, Raimund ^a   Mcgunnigle, Gerald ^a   Kraft, Martin ^a   Biasio, Martin De ^a   Rehrmann, Volker ^b   Balthasar, Dirk ^b  

^a CTR CARINTHIAN TECH RESEARCH AG   (Austria)

^b TiTech GmbH   (Germany)

Author keywords

Flame retardant; Imaging spectroscopy; NIR; Polymer; Real time

Indexed keywords

CHEMOMETRIC MODELS; FLAME-RETARDANT; FLAME-RETARDANT ADDITIVES; FTIR; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; HYPERSPECTRAL IMAGING; IMAGING SPECTROSCOPY; NIR; NIR IMAGE; NIR IMAGING SPECTROSCOPY; REAL-TIME; REAL-TIME DETECTION; SAMPLE SETS; SPECTRAL FEATURE;

FLAMMABILITY; FOURIER TRANSFORM INFRARED SPECTROSCOPY; INFRARED DEVICES; OPTOELECTRONIC DEVICES; POLYMER BLENDS; POLYMERS; SIGNAL DETECTION; SPECTROMETERS; SPECTROMETRY;

FLAME RETARDANTS;

EID: 69649098802     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.818540     Document Type: Conference Paper

References (19)

1. A. Kulcke, C. Gurschler, G. Spöck, 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).
2. F. van der Meer, S. M. De John (Eds.); Imaging Spectrometry: Basic Principles and Prospective Applications, Kluwer Academic Publishers (2002).
3. M. F. Carlsohn, B. H. Menze, M. Kelm, F. A. Hamprecht, A. Kercek, R. Leitner, G. Polder, Spectral Imaging and Applications in R. Lukac, K. N. Plataniotis, Color Image Processing, Springer (2006).
4. G. H. Bearmann, R. M. Levenson, D. Cabib (Eds); Spectral Imaging: Basic Principles and Prospective Applications, Kluwer Academic Publishers (2002).
5. N. Gat, Spectrometer Apparatus, US Pat. 5166755 (1992).
6. T. Hyvarinen, E. Herrala, A. Dall'Ava; Proc SPIE, 3302, p. 165 (1998).
7. 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).
8. R. Leitner, I. Ibraheem, A. Kercek. Spectral Imaging as a Modern Tool for Medical Diagnostics. in R. Leitner, Ed., Spectral Imaging (Proc. Int. Workshop on Spectral Imaging), Austrian Computer Society, Vienna, pages 31-34, April 2003.
9. M. F. Hopkins, Four-color pyrometry for metal emissivity characterization, Proc. SPIE, 2599, p. 294 (1995).
10. N. Gat; Proc. SPIE, Imaging spectroscopy using tunable filters: a review, 4056, p. 50 (2000).
11. A. Harvey, I. Abboud, A. Gorman, A. McNaught, S. Ramachandran, E. Theofanidou, Spectral Imaging of the Retina, SPIE Vol. 6047, 2006.
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. K. C. Lawrence, W. R. Windham, B. Park, R. J. Buhr; Hyperspectral Imaging for Poultry Contaminant Detection, NIR News 12(5) (2001).
14. C. Gurschler, G. Serafino, G. Spöck, 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).
15. I. E. Frank, J. H. Friedmann, A statistical view of chemometrics regression tools, Technometrics, 35, p 109-148 (1993).
16. R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, Wiley-Interscience, 2000.
17. E. Pekalska, 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.
18. H. Drucker, C. J.C. Burges, L. Kaufman, A. Smola V.Vapnik, Support Vector Regression Machines, Advances in Neural Information Processing Systems 9, NIPS 1996, 155-161, MIT Press.
19. Q. Z. Jackson, D. Landgrebe, Design Of An Adaptive Classification Procedure For The Analysis Of High- Dimensional Data With Limited Training Samples, PhD Thesis and School of Electrical & Computer Engineering Technical Report TR-ECE 01-5, December 2001.