Rapid and Nondestructive Identification of Different Pure Yarn Fabrics Based on near Infrared Spectroscopy

Abstract:

Near infrared spectroscopy combined with chemometrics analysis were investigated as an emerging method for the identification of textiles, this method allowed straightforward and rapid testing of textiles without destroying their integrity compared to traditional testing methods such as dissolution method, combustion method and microscope observation. In this work, different pretreating algorithms coupled with the Soft Independent Modelling by Class Analogy (SIMCA) have been studied to achieve the best recognition model to identify different pure yarn fabrics (cotton, linen, silk, wool and polyester). Results showed that little difference between different data points smoothing. Percentages of recognition and rejection of 100% were obtained of silk, wool and polyester by pretreating with Savitzky-Golay 7 data points smoothing, Savitzky-Golay second derivative and mean centering. The percentages of recognition were 93.75% and 96.25% for cotton and linen respectively by pretreating with Savitzky-Golay 7 data points smoothing, Savitzky-Golay first derivative and mean centering, nevertheless, the percentages of rejection was low for linen with 83.75%. The results from this paper suggested that near infrared spectroscopy in combination with SIMCA could be applied to the identification of pure yarn fabrics, whereas further study should be made to improve the rejection rate of linen.

• Periodical: Key Engineering Materials (Volume 671)
• Pages: 363-368
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.671.363
• Online since: November 2015
• Authors: Cai Hong Wang, Hua Yong Liu, Xiong Ying Wu, Xue Mei Ding*
• Keywords: Near Infrared Spectroscopy, Nondestructive Identification, Pure Yarn Fabrics, Rapid Identification, SIMCA

References

[1] Ciurczak, E.W., Handbook of Near-Infrared Analysis, Marcel Dekker, SA, New York, (1992).

[2] Moron, A., et al., Application of near infrared reflectance spectroscopy for the analysis of organic C, total N and pH in soils of Uruguay. Journal of near infrared spectroscopy, 2002. 10. (3): pp.215-222.

DOI: 10.1255/jnirs.338

[3] Teye, E., et al., Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis. Food Chemistry, 2015. 176: pp.403-410.

DOI: 10.1016/j.foodchem.2014.12.042

[4] Alves, J.C., et al., Biodiesel content determination in diesel fuel blends using near infrared (NIR) spectroscopy and support vector machines (SVM). Talanta, 2013. 104: pp.155-161.

DOI: 10.1016/j.talanta.2012.11.033

[5] Galvão, R.K.H., et al., An application of subagging for the improvement of prediction accuracy of multivariate calibration models. Chemometrics and Intelligent laboratory systems, 2006. 81(1): pp.60-67.

DOI: 10.1016/j.chemolab.2005.09.005

[6] Alvarenga, L., et al., Tablet identification using near-infrared spectroscopy (NIRS) for pharmaceutical quality control. Journal of pharmaceutical and biomedical analysis. 2008. 48 (1): pp.62-69.

DOI: 10.1016/j.jpba.2008.05.007

[7] Haffner, F.B., et al., Compositional analysis of Miscanthus giganteus by near infrared spectroscopy. Cellulose, 2013. 20(4): pp.1629-1637.

DOI: 10.1007/s10570-013-9935-1

[8] Jiang, H., et al. Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy , (2015).

DOI: 10.1016/j.saa.2015.04.024

[9] Yotsuda, T., et al., Near infrared spectral imaging for water absorbency of woven fabrics. Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE. Vol. 3. IEEE, (2004).

DOI: 10.1109/imtc.2004.1351542

[10] Canaza-Cayo, A. W., et al., A feasibility study of the classification of Alpaca (Lama pacos) wool samples from different ages, sex and color by means of visible and near infrared reflectance spectroscopy. Computers and Electronics in Agriculture, 2012, 88: pp.141-147.

DOI: 10.1016/j.compag.2012.07.013

[11] Zoccola, M., et al., 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): pp.1283-1289.

DOI: 10.1007/s12221-013-1283-0

[12] Church, J. S., et al., A comparison of vibrational spectroscopic methods for analyzing wool/polyester textile blends. Textile research journal, 1999. 69(9): pp.676-684.

DOI: 10.1177/004051759906900908

[13] Durand, A., et al. Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton–viscose textiles. Analytica Chimica Acta, 2007. 595(1): pp.72-79.

DOI: 10.1016/j.aca.2007.03.024

[14] Sohn, M., et al., Fourier transform near-infrared spectroscopy for determining linen content in linen/cotton blend products. Textile research journal, 2005. 75(8): pp.583-590.

DOI: 10.1177/0040517505057167

[15] Canaza-Cayo, A. W., et al., Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy. Animal, 2013 7(7): 1219-1225.

DOI: 10.1017/s1751731113000505

[16] Blanco, M., et al. Use of near-infrared spectrometry in control analyses of acrylic fibre manufacturing processes. Analytica chimica acta. 1999. 383(3): pp.291-298.

DOI: 10.1016/s0003-2670(98)00804-6

[17] Gishen, M., et al., Feasibility study on the potential of visible and near infrared reflectance spectroscopy to measure alpaca fibre characteristics. Animal, 2007. 1. (6): pp.899-904.

DOI: 10.1017/s1751731107000146

[18] Yan, J.J., et al., Characterization of degradation of cotton cellulosic fibers through near infrared spectroscopy. Journal of Polymers and the Environment, 2013. 21(4): pp.902-909.

DOI: 10.1007/s10924-013-0605-z

[19] Chen, C.H., et al., Non–destructive near–infra–red analysis for the identification of dyes on textiles. Journal of the Society of Dyers and Colourists, 1997. 113(2): pp.51-56.

DOI: 10.1111/j.1478-4408.1997.tb01867.x

[20] Sekulic, S., et al. Nonlinear multivariate calibration methods in analytical chemistry. Analytical Chemistry, 1993. 65(19): p. 835A-845A.

DOI: 10.1021/ac00067a714

[21] Barnes, R. J., et al., Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied spectroscopy, 1989. 43(5): pp.772-777.

DOI: 10.1366/0003702894202201

[22] Isaksson,T., et al., The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Applied Spectroscopy, 1988. 42(7): pp.1273-1284.

DOI: 10.1366/0003702884429869

[23] Information on http: /en. wikipedia. org/wiki/Soft_independent_modelling_of_class_analogies.

[24] Smith, B.C., et al. Infrared spectral interpretation: a systematic approach. CRC press, (1998).

[25] Coleman, S.W., et al., Prediction of clean mohair, fiber diameter, vegetable matter, and medullated fiber with near-infrared spectroscopy. Journal of animal science, 1999. 77: pp.2594-2602.

DOI: 10.2527/1999.77102594x