2019
DOI: 10.1515/aut-2018-0055
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Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

Abstract: 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… Show more

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Cited by 52 publications
(25 citation statements)
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“…Unfortunately, most of the modern fibers (regenerated and synthetic) are almost identical in their morphology and thus cannot be identified reliably with microscopic visualization [2,6]. For fast analysis of textile fibers, often in (almost) non-destructive way, different vibrational spectroscopy approaches-Raman, near-infrared spectroscopy (NIR) and mid-infrared spectroscopy (IR)-have been used [1,4,5,[7][8][9][10][11]. Raman spectroscopy, while being widely used method in cultural heritage, [4,12,13] has a serious limitation when it comes to the identification of textile fibers.…”
Section: Introductionmentioning
confidence: 99%
“…Unfortunately, most of the modern fibers (regenerated and synthetic) are almost identical in their morphology and thus cannot be identified reliably with microscopic visualization [2,6]. For fast analysis of textile fibers, often in (almost) non-destructive way, different vibrational spectroscopy approaches-Raman, near-infrared spectroscopy (NIR) and mid-infrared spectroscopy (IR)-have been used [1,4,5,[7][8][9][10][11]. Raman spectroscopy, while being widely used method in cultural heritage, [4,12,13] has a serious limitation when it comes to the identification of textile fibers.…”
Section: Introductionmentioning
confidence: 99%
“…Figure 2 shows the reflectance spectra of cotton, wool, and silk in the range 800-1700 nm (12,500-5882.35 cm −1 ). The absorption bands and their proposed assignments [22,26,30,31] are summarised in Table 4. Apart from some minor variation in reflectance intensity, the spectra are reproducible and dye and ageing does not influence significantly the spectra in the range studied.…”
Section: Nir Spectramentioning
confidence: 99%
“…Multivariate classification techniques may overcome this problem by building mathematical models, based on the spectral information, and identifying classes or groups by coding their similarities [28,29]. A consistent number of publications report on NIR spectroscopy and classification techniques for identifying textile fibres, both natural and synthetic, and their blends, particularly for industrial applications [15,[30][31][32][33]. For example, Howell and Davis [34] exploited Mahalanobis distances to classify four different synthetic fibres while Jasper and Kovacs [35] neural networks for classifying 17 fibre types.…”
Section: Introductionmentioning
confidence: 99%
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“…16 used the convolutional network classification method of normalized and pixelated NIRS to automatically classify nine types waste textiles, which effectively improved the detection level and speed of fabric components; Zhou et al. 17 used NIRS technology combined with principal component analysis (PCA) and the analogy soft independent modeling method to achieve the realization of cotton, Tencel, wool, cashmere, polyethylene terephthalate, polylactic acid (PLA) and polypropylene (PP) identification; Riba et al. 18 used on attenuated total reflection Fourier transform infrared (ATR-FTIR) technology combined with PCA, canonical variate analysis (CVA) and k -nearest neighbors (k-NN) mathematical methods to effectively identify and classify seven pure component fibers, providing a technical reference for the sorting of waste textiles.…”
mentioning
confidence: 99%