Fiber contents in cotton/terylene and cotton/wool blended textiles were tested by near infrared (NIR) spectroscopy combined with back propagation (BP) neural network. Near infrared spectra of samples were obtained in the range of 4000？cm？1~10000？cm？1. Wavelet Transform (WT) was used for noise reduction and compression of spectra data. The correction models of cotton/terylene and cotton/wool contents based on BP neural network and reconstructed spectral signals were established. The number of hidden neurons, learning rate, momentum factor, and learning times was optimized, and decomposition scale of WT was discussed. Experimental results have shown that this approach by Fourier transformation NIR based on the BP neural network to predict the fiber content of textile can satisfy the requirement of quantitative analysis and is also suitable for other fiber content measurements of blended textiles. 1. Introduction Textiles are necessaries of human life. With the development of textile industry and the people’s living standards, the kinds of pure textile, blended textile, and intertexture are increasing gradually. The requirement of analysis to fibers is also raised in the fields of production, scientific research, and trade. The various fiber content is index of textile quality, and how to measure it has an important meaning. Traditional chemical solution is a quantitative detecting method, which has long testing time, and a series of solvents should be prepared to dissolve the fiber, and lots of harmful gases would be produced and pollute the working environment [1]. NIR is referred to as the electromagnetic wave in the wavelength range of 780？nm~2526？nm, which has wide spectrum bands, weak absorption, and more information and is produced by the absorption of frequency doubling and frequency summing caused by molecular radicals’ vibration. Using stoichiometry method to solve the extraction of spectrum information and background interference, the good testing results will be obtained. Combination of NIR with stechiometry is more suitable to quantitive analysis and has been already applied to the fields of medicine, food, agriculture, chemical industry, and environment monitoring [2–8]. Applications of NIR to testing the fiber contents in textiles have been reported [9–12]. Most of these reports used the pretreatment method to preprocess spectrum data and sieve method to screening variables and then built up the model of partial least square (PLS) method. The progression is inconvenient. In our previous work, the fiber contents of cotton/terylene blended
References
[1]
Y. Zhou, H. Xu, and Y. Ying, “NIR analysis of textile natural raw material,” Spectroscopy & Spectral Analysis, vol. 28, no. 12, pp. 2804–2807, 2008.
[2]
H. Wang, W. Li, and Y. Liu, “Study on discrimination of varieties of corn using near-infrared spectroscopy based on GA and LDA,” Spectroscopy & Spectral Analysis, vol. 31, no. 3, pp. 669–672, 2011.
[3]
D. Sun, Y. Li, Q. Wang, and C. Le, “Remote sensing retrieval of CDOM concentration in Lake Taihu with hyper-spectral data and neural network model,” Geomatics & Information Science of Wuhan University, vol. 34, no. 7, pp. 851–855, 2009.
[4]
T. A. Lestander and P. Geladi, “NIR spectral information used to predict water content of pine seeds from multivariate calibration,” Canadian Journal of Forest Research, vol. 35, no. 5, pp. 1139–1148, 2005.
[5]
V. A. Saptari, K. Youcef-Toumi, and J. Zhang, “NIR measurements of glucose in synthetic biological solutions using high-throughput angle-tuned filter spectrometer,” in Proceedings of the 4th SPIE Optical Diagnostics and Sensing, vol. 5325, pp. 1–10, January 2004.
[6]
Y. Shan, X. R. Zhu, Q. S. Xu, and Y. Z. Liang, “Determining the contents of fat and protein in milk powder by using near infrared spectroscopycombined with wavelet transform and radical basis function neural networks,” Journal of Infrared & Millimeter Waves, vol. 29, no. 2, pp. 128–131, 2010.
[7]
W. Liu, Q. R. Chang, M. Guo, D. X. Xing, and Y. S. Yuan, “Detection of leaf nitrogen content of summer corn using visible/near infrared spectra,” Journal of Infrared & Millimeter Waves, vol. 30, no. 1, pp. 48–54, 2011.
[8]
Y. Zhang, L. D. Zhang, Q. L. Bai, and S. J. Chen, “Nondestructive identification of different oil content maize kernels by near-infrared spectra,” Spectroscopy & Spectral Analysis, vol. 29, no. 3, pp. 686–689, 2009.
[9]
G. F. Wu, D. S. Zhu, and Y. He, “Identification of fine wool and cashmere by using Vis/NIR spectroscopy technology,” Spectroscopy and Spectral Analysis, vol. 28, no. 6, pp. 1260–1263, 2008.
[10]
B. Chen, G. Cui, S. Z. Jin, and X. T. Wang, “Application of near infrared spectra in rapid inspection of cotton contents,” Journal of Jiangsu University, vol. 28, no. 3, pp. 185–188, 2007.
[11]
H. N. Feng, B. Gan, and S. Z. Jin, “Concentration test of textile mixture of cotton and terlene with NIR method,” Laser & Infrared, vol. 35, p. 768, 2005.
[12]
B. Chen, X. T. Wu, and K. Ni, “Application of correlation analysis in rapid detecting silk content of fabric textile by NIR,” Spectroscopy Instrument and Analysis Z, vol. 1, p. 52, 2006.
[13]
Z. X. Ge and Z. Q. Sun, Eds., The Theory of Artificial Neural Network and Its Realization in MATLAB7, Electronics Industry Press, Beijing, China, 2007.
[14]
Center for the Research and Development of FeiSi Technology, Ed., The Theory of Wavelet Analysis and Its Realization in MATLAB7, Electronics Industry Press, Beijing, China, 2005.
