光谱学与光谱分析 |
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Identification of Varieties of Textile Fibers by Using Vis/NIR Infrared Spectroscopy Technique |
WU Gui-fang1, 2, HE Yong1* |
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China 2. College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China |
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Abstract The aim of the present paper was to provide new insight into Vis/NIR spectroscopic analysis of textile fibers. In order to achieve rapid identification of the varieties of fibers, the authors selected 5 kinds of fibers of cotton, flax, wool, silk and tencel to do a study with Vis/NIR spectroscopy. Firstly, the spectra of each kind of fiber were scanned by spectrometer, and principal component analysis (PCA) method was used to analyze the characteristics of the pattern of Vis/NIR spectra. Principal component scores scatter plot (PC1×PC2×PC3) of fiber indicated the classification effect of five varieties of fibers. The former 6 principal components (PCs) were selected according to the quantity and size of PCs. The PCA classification model was optimized by using the least-squares support vector machines (LS-SVM) method. The authors used the 6 PCs extracted by PCA as the inputs of LS-SVM, and PCA-LS-SVM model was built to achieve varieties validation as well as mathematical model building and optimization analysis. Two hundred samples (40 samples for each variety of fibers) of five varieties of fibers were used for calibration of PCA-LS-SVM model, and the other 50 samples (10 samples for each variety of fibers) were used for validation. The result of validation showed that Vis/NIR spectroscopy technique based on PCA-LS-SVM had a powerful classification capability. It provides a new method for identifying varieties of fibers rapidly and real time, so it has important significance for protecting the rights of consumers, ensuring the quality of textiles,and implementing rationalization production and transaction of textile materials and its production.
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Received: 2009-02-12
Accepted: 2009-05-16
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Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
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[1] HE Lan-zhi, CHEN Li-ping, WANG Xue-mei(何兰芝,陈莉萍,王雪梅). China Fiber Inspection(中国纤检),2008, 2: 23. [2] Cleve E, Schollmeyer E. Analytica Chimica Acta, 2000, 420(2): 163. [3] LI Qing-shan(李青山). Introduction of International Textile(国际纺织导报), 2001, (4): 36. [4] DU Shu-ying(杜树莹). China Fiber Inspection(中国纤检), 1995, 10: 10. [5] WANG Jian-ping, DING Yu-mei, CAI Lu-yang(王建平, 丁玉梅, 蔡露阳). Dyeing & Finishing(印染), 2007, 33(20): 16. [6] Cardamone J M, Gould J M, Gordon S H. Textile Research Journal, 1987, 57(4): 235. [7] Lin Jer-yan, Wang Pin-ning, Shyr Tien-wei. Textile Research Journal, 2008, 78(10): 911. [8] LI Xue-jia, MIAO Ai-dong(李学佳,缪爱东). Introduction of International Textile(国际纺织导报), 2007, (10): 16. [9] LI Zhi-hong, REN Yu(李志红, 任 煜). Shanghai Textile Science & Technology(上海纺织科技),2006, 34(4): 27. [10] WU Pei-yun, MO Jing-yu(吴佩云, 莫靖昱). Shanghai Textile Science & Technology(上海纺织科技),2008, 36(3): 51. [11] Kerkhoff K, Cescutti G, Kruse L, et al. Textile Research Journal, 2009, 79(1): 69. [12] ZHU Hui-ju, ZHANG Zhuo-yong(朱惠菊, 张卓勇). Journal Instrumental Analysis(仪器分析), 2004, 23(6): 95. [13] HE Shu-hua, ZHANG Jie, QU Lian-ying(何淑华,张 洁,曲连颖). Acta Scientiarum Naturalium Universitattis Jilinensis(吉林大学自然科学学报),1999,(4):103. [14] Liu Fei, Yong He, Li Wang. Analytica Chimica Acta, 2008, 615(1): 10. [15] CHU Xiao-li, XU Yu-peng, LU Wan-zhen(褚小立,许育鹏,陆婉珍). Chinese Journal of Analytical Chemistry(分析化学), 2008, 36(5): 702. [16] XU Rong, SUN Su-qin, CHEN Jun, et al(徐 荣, 孙素琴, 陈 君, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009, 29(1): 97. [17] Bona M T, Andres J M. Analytica Chimica Acta, 2008, 624(1): 68. [18] He Yong, Feng Shuijuan, Deng Xunfei, et al. Food Research International, 2006, 39(6): 645. [19] ZHANG Quan-ming, LIU Hui-jin(张全明,刘会金). Proceedings of the CSEE(中国电机工程学报), 2008, 28(1): 106. [20] Wu Di, He Yong, Feng Shui-juan. Analytica Chimica Acta, 2008, 610(2): 232. [21] Langeron Y, Doussot M, Hewson D J, et al. Engineering Applications of Artificial Intelligence, 2007, 20(3): 415.
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