Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Fibers are raw materials used for manufacturing yarns and fabrics, and their properties are closely related to the performances of their derivatives. It is indispensable to implement fiber identification in analyzing textile raw materials. In this paper, seven common fibers, including cotton, tencel, wool, cashmere, polyethylene terephthalate (PET), polylactic acid (PLA), and polypropylene (PP), were prepared. After analyzing the merits and demerits of the current methods used to identify fibers, near-infrared (NIR) spectroscopy was used owing to its significant superiorities, the foremost of which is it can capture the tiny information differences in chemical compositions and morphological features to display the characteristic spectral curve of each fiber. First, the fibers’ spectra were collected, and then, the relationships between the vibrations of characteristic chemical groups and the corresponding wavelengths were researched to organize a spectral information library that would be beneficial to achieve quick identification and classification. Finally, to achieve intelligent detection, pattern recognition approaches, including principal component analysis (PCA) (used to extract information of interest), soft independent modeling of class analogy (SIMCA), and linear discrimination analysis (LDA) (defined using two classifiers), assisted in accomplishing fiber identification. The experimental results – obtained by combining PCA and SIMCA – displayed that five of seven target fibers, namely, cotton, tencel, PP, PLA, and PET, were distributed with 100% recognition rate and 100% rejection rate, but wool and cashmere fibers yielded confusing results and led to relatively low recognition rate because of the high proportion of similarities between these two fibers. Therefore, the six spectral bands of interest unique to wool and cashmere fibers were selected, and the absorbance intensities were imported into the classifier LDA, where wool and cashmere were group-distributed in two different regions with 100% recognition rate. Consequently, the seven target fibers were accurately and quickly distinguished by the NIR method to guide the fiber identification of textile materials.
Opis fizyczny
Bibliogr. 33 poz.
  • Key Laboratory of Textile Science & Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai, China
  • School of Textile Science&Engineering, Xi’an Polytechnic University, Shaanxi Province, China
  • Key Laboratory of Textile Science & Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai, China
  • Key Laboratory of Textile Science & Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai, China
  • [1] Houck, M. M. (2010). Introduction to textile fiber identification - identification of textile fibers - 1. Australian Journal of Forensic Sciences, 42(2), 153-154.
  • [2] Gray, F. M., Smith, M. J., Silva, M. B. (2011). Identification and characterization of textile fibers by thermal analysis. Journal of Chemical Education, 88(4), 476-479.
  • [3] Stoeffler, S. F. (1996). A flowchart system for the identification of common synthetic fibers by polarized light microscopy. Journal of Forensic Sciences, 41, 297-299.
  • [4] Liu, C. (2002). Study of microscopes method identification of textile fiber, Shanghai Textile Science & Technology.
  • [5] Wortmann, F. J., Wortmann, G., Arns, W. (1989). Quantitative fiber mixture analysis by scanning electron microscopy. II. Blends of wool with angora rabbit hair. Textile Research Journal, 61(7), 371-374.
  • [6] Wang, R. W., Wu, X. Y., Wang, S. Y., (2009). Automatic identification of ramie and cotton fibers using characteristics in longitudinal view. Part II: fiber stripes analysis. Textile Research Journal, 79(17), 1547-1556.
  • [7] Wang, R. W., Wu, X. Y., Wang, S. Y. (2015). Automatic identification of ramie and cotton fibers using characteristics in longitudinal view, part I: locating capture of fiber images. Textile Research Journal, 79(14), 1251-1259.
  • [8]Campiglia, A. D., Heider, E. C., Mujumdar, N. (2015). Enhancing textile fiber identification with detergent fluorescence. Applied Spectroscopy, 69(12), 1390.
  • [9] Kirsten, K., Gabriel, C., Lothar, K. (2009). Development of a DNA-analytical method for the identification of animal hair fibers in textiles. Textile Research Journal, 79(1), 69-75.
  • [10] Ji, W., Bai, L., Ji, M. (2010). A method for quantifying mixed goat cashmere and sheep wool. Forensic Science International, 208(1-3), 139-142.
  • [11] Tang, M., Zhang, W., Zhou, H. (2014). A real-time PCR method for quantifying mixed cashmere and wool based on hair mitochondrial DNA. Textile Research Journal, 84(15), 1612-1621.
  • [12] Molloy, J. F., Naftaly, M., Andreev, Y. M., et al. (2014). Identification of textile fiber by IR and Raman spectroscopy. International Conference on Infrared, Millimeter, and Terahertz Waves, IEEE 1-2.
  • [13] Tong, M., Cao, B. H., Li, C. (2012). Identification of textile fibers by Terahertz time domain spectroscopy, photonics and optoelectronics, IEEE, 1-3.
  • [14] Wu, G. F., He, Y. (2010). Identification of varieties of textile fibers by using Vis/NIR infrared spectroscopy technique. Spectroscopy & Spectral Analysis, 30(2), 331.
  • [15] Cleve, E., Bach, E., Schollmeyer, E. (2000). Using chemometric methods and NIR spectrophotometry in the textile industry. Analytica Chimica Acta, 420(2), 163-167.
  • [16] Liu, L., Yan, L., Xie, Y. (2014). Determination of fiber contents in blended textiles by NIR combined with BP neural network. Applied Mechanics & Materials, 2013, 301-304.
  • [17] Pedro, A. M., Ferreira, M. M. (2007). Simultaneously calibrating solids, sugars and acidity of tomato products using PLS2 and NIR spectroscopy. Analytica Chimica Acta, 595(1-2), 221-227.
  • [18] Matero, S., Pajander, J., Soikkeli, A. M. (2007). Predicting the drug concentration in starch acetate matrix tablets from ATR-FTIR spectra using multi-way methods. Anal Chim Acta, 595(1-2), 190-197.
  • [19] James, R., Chanel, F., Joseph, M. (2010). Near infrared measurement of cotton fiber micronaire by portable near infrared instrumentation. Textile Research Journal, 80(15), 67-81.
  • [20] Yan, J., Villarreal, N., Xu, B. (2013). Characterization of degradation of cotton cellulosic Fibers through near infrared spectroscopy. Journal of Polymers and the Environment, 21(4), 902-909.
  • [21] Alves, J. C. L., Henriques, C. B., Poppi, R. J. (2012). Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an inline blending optimizer system, Fuel, 97(7), 710-717.
  • [22] Nielsen, M. G., Pors, A., Albrektsen, O. (2012). Efficient absorption of visible radiation by gap plasm resonators, Optics Express, 20(12), 13311-13319.
  • [23] Mizaikoff, B. (2013). Wave guide-enhanced mid-infrared chem/bio sensors. Chemical Society Reviews, 42(22), 8683-8699.
  • [24] Luypaert, J., Massart, D. L., Heyden, Y. V. (2007). Nearinfrared spectroscopy applications in pharmaceutical analysis. Talanta, 72(3), 865-883.
  • [25] Liu, Y., Sunm X., Ouyang, A. (2010). Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN, LWT - Food Science and Technology, 43(4), 602-607.
  • [26] Yang, H. Q., Kuang, B. Y., Mouazen, A. M. (2011). Selection of preprocessing parameters for PCA of soil classification affected by particle sizes based on vis/NIR spectroscopy. Key Engineering Materials, 467-469, 725-730.
  • [27] Chen, Q., Zhao, J., Liu, M. (2008). Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms. Journal of Pharmaceutical & Biomedical Analysis, 46(3), 568-573.
  • [28] Zhao, C., Qu, H. B., Cheng, Y. Y. (2004). A new approach to the fast measurement of content of amino acids in Cordyceps sinensis by ANN-NIR. Spectroscopy & Spectral Analysis, 24(1), 50.
  • [29] Liu, Y. (2009). A portable Vis-NIR spectrometer to determine soluble solids content in Gannan navel orange by LS-SVM and EWs selection. Proceedings of SPIE, 7519, 751906-751906-7.
  • [30] Todorova, M., Atanassova, S. (2016). Near infrared spectra and soft independent modelling of class analogy for discrimination of Chernozems, Luvisols and Vertisols. Journal of Near Infrared Spectroscopy, 24(3), 271-280.
  • [31] Planinc, A., Dejaegher, B., Heyden, Y. V. (2016). LC-MS analysis combined with principal component analysis and soft independent modelling by class analogy for a better detection of changes in N-glycosylation profiles of therapeutic glycoproteins. Analytical & Bioanalytical Chemistry, 1-9.
  • [32] Han, J., Chung, H., Han, S. H. (2007). Score-moment combined linear discrimination analysis (SMC-LDA) as an improved discrimination method, Analyst, 132(1), 67-74.
  • [33] Balabin, R. M., Smirnov, S. V. (2011). Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data, Analytica Chimica Acta, 692(1-2), 63.
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.