Classification of textile fabrics by use of spectroscopy-based pattern recognition methods

References

• Gajdošík, J.; Zeman, J.; Šejnoha, M. Qualitative analysis of fiber composite microstructure: Influence of boundary conditions. Probabilistic Engineering Mechanics 2006, 21(4), 317–329.
   Web of Science ®Google Scholar
• Nicolaï, B. M.; Beullens, K.; Bobelyn, E.; Peirs, A.; Saeys, W.; Theron, K. I.; Lammertyna, J. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology 2007, 46(2), 99–118.
   Web of Science ®Google Scholar
• Blanco, M.; Villarroya, I. NIR spectroscopy: A rapid-response analytical tool. Trends in Analytical Chemistry 2002, 21(4), 240–250.
   Web of Science ®Google Scholar
• Rodgers, J. E. Influences of carpet and instrumental parameters on the identification of carpet face fiber by NIR. AATCC Review 2002, 2(6), 27–32.
   Web of Science ®Google Scholar
• Zoccola, M.; Lu, N.; Mossotti, R.; Innocenti, R.; Montarsolo, A. Identification of wool, cashmere, yak, and angora rabbit fibers and quantitative determination of wool and cashmere in blend: A near infrared spectroscopy study. Fibers and Polymers 2013, 14(8), 1283–1289.
   Web of Science ®Google Scholar
• Cleve, E.; Bach, E.; Schollmeyer, E. Using chemometric methods and NIR spectrophotometry in the textile industry. Analytica Chimica Acta 2000, 420(2), 163–167.
   Web of Science ®Google Scholar
• Blanco, M.; Coello, J.; Iturriaga, H.; Maspoch, S.; Pagès, J. Use of near-infrared spectrometry in control analyses of acrylic fibre manufacturing process. Analytica Chimica Acta 1999, 383(3), 291–298.
   Web of Science ®Google Scholar
• Fortier, C. A.; Rodgers, J. E.; Cintrón, M. S.; Cui, X.; Foulk, J. A. Identification of cotton and cotton trash components by Fourier transform near-infrared spectroscopy. Textile Research Journal 2011, 81(3), 230–238.
   Web of Science ®Google Scholar
• Langeron, L.; Doussot, M.; Hewson, D. J.; Duchêne, J. Classifying NIR spectra of textile products with kernel methods. Engineering Application of Artificial Intelligence 2007, 20(3), 415–427.
   Web of Science ®Google Scholar
• Huang, G.-B. What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle. Cognitive Computation 2015, 7, 263–278.
   Web of Science ®Google Scholar
• Kasun, L. L.C.; Zhou, H.; Huang, G.-B.; Vong, C. M. Representational learning with extreme learning machine for big data. IEEE Intelligent Systems 2013, 28(6), 31–34.
   Web of Science ®Google Scholar
• Peng, J.; Li, L.; Tang, Y. Y. Combination of activation functions in extreme learning machine for multivariate calibration. Chemometrics and Intelligent Laboratory Systems 2013, 120, 53–58.
   Web of Science ®Google Scholar
• Suykens, J. A. K.; Vandewalle, J. Least squares support vector machine classifiers. Neural Processing Letters 1999, 9(3), 293–300.
   Web of Science ®Google Scholar
• Chauchard, F.; Cogdill, R.; Roussel, S.; Roger, J. M.; Bellon-Maurel, V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: Development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems 2004, 71(2), 141–150.
   Web of Science ®Google Scholar
• Huang, G. B.; Wang, D. H.; Lan, Y. Extreme learning machine: A survey. International Journal of Machine Learning and Cybernetics 2011, 2(2), 107–112.
   Web of Science ®Google Scholar
• Zheng, W.; Fu, X.; Ying, Y. Spectroscopy-based food classification with extreme learning machine. Chemometrics and Intelligent Laboratory Systems 2014, 139, 42–47.
   Web of Science ®Google Scholar
• Ruckebusch, C.; Orhan, F.; Durand, A.; Boubellouta, T.; Huvenne, J. P. Quantitative analysis of Cotton–Polyester textile blends from near-infrared spectra. Applied Spectroscopy 2006, 60(5), 539–544.
   PubMed Web of Science ®Google Scholar
• Rodgers, J. E. Influences of carpet and instrumental parameters on the identification of carpet face fiber by NIR. AATCC Review 2002, 2(6), 27–32.
   Web of Science ®Google Scholar
• Liu, Y.; Kokot, S.; Sambi, T. A. Vibrational spectroscopy investigation of Australian cotton cellulose fibres. Part 2. A Fourier transform near-infrared preliminary study. Analyst 1998, 123(8), 1725–1728.
   Web of Science ®Google Scholar
• Thygesen, L. G.; Lundqvist, S. O. NIR measurement of moisture content in wood under unstable temperature conditions. Part 1. Thermal effects in near infrared spectra of wood. Journal of Near Infrared Spectroscopy 2000, 8(1), 183–189.
   Web of Science ®Google Scholar
• Rodgers, J.; Bech, K. NIR characterization and measurement of the cotton content of dyed blend fabrics. Textile Research Journal 2009, 79(8), 675–686.
   Web of Science ®Google Scholar
• Ruckebusch, C.; Orhan, F.; Durand, A.; Boubellouta, T.; Huvenne, J. P. Quantitative analysis of cotton-polyester textile blends from near-infrared spectra. Applied Spectroscopy 2006, 60(5), 539–544.
   PubMed Web of Science ®Google Scholar
• Li, X.; He, Y.; Fang, H. Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy. Journal of Food Engineering 2007, 81(2), 357–363.
   Web of Science ®Google Scholar
• Wu, W.; Massart, D. L. Artificial neural networks in classification of NIR spectral data: Selection of the input. Chemometrics and Intelligent Laboratory Systems 1996, 35(1), 127–135.
   Web of Science ®Google Scholar
• Chorowski, J.; Wang, J.; Zurada, J. M. Review and performance comparison of SVM- and ELM-based classifiers. Neurocomputing 2014, 128, 507–516.
   Web of Science ®Google Scholar
• Zhong, H.; Miao, C.; Shen, Z.; Feng, Y. Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings. Neurocomputing 2014, 128, 285–295.
   Web of Science ®Google Scholar
• Blanco, M.; Coello, J.; Iturriaga, H.; Maspoch, S.; Pagès, J. NIR calibration in non-linear systems: Different PLS approaches and artificial neural networks. Chemometrics and Intelligent Laboratory Systems 2000, 50, 75–82.
   Web of Science ®Google Scholar
• Chen, Q.; Zhao, J.; Zhang, H.; Wang, X. Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration. Analytica Chimica Acta 2006, 572, 77–84.
   PubMed Web of Science ®Google Scholar
• Noori, R.; Karbassi, A. R.; Moghaddamnia, A.; Han, D.; Zokaei-Ashtiani, M. H.; Farokhnia, A.; Gousheh, M. G. Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. Journal of Hydrology 2011, 401(3–4), 177–189.
   Web of Science ®Google Scholar
• Zhang, Y.; Li, H.; Wang, Z.; Zhang, W.; Li, J. A preliminary study on time series forecast of fair-weather atmospheric electric field with WT-LSSVM method. Journal of Electrostatics 2015, 75, 85–89.
   Web of Science ®Google Scholar
• Li, Z.; Li, G.; Yan, W.; Lin, L. Classification of diabetes and measurement of blood glucose concentration noninvasively using near infrared spectroscopy. Infrared Physics & Technology 2014, 67, 574–582.
   Web of Science ®Google Scholar
• Li, S.; Dai, L. Classification of gasoline brand and origin by Raman spectroscopy and a novel R-weighted LSSVM algorithm. Fuel 2012, 96, 146–152.
   Web of Science ®Google Scholar
• Huang, G.; Huang, G. B.; Song, S.; You, K. Trends in extreme learning machines: A review. Neural Networks 2015, 61, 32–48.
   PubMed Web of Science ®Google Scholar