光谱学与光谱分析 |
|
|
|
|
|
Study of Nondestructive and Fast Identification of Fabric Fibers Using Near Infrared Spectroscopy |
YUAN Hong-fu1, CHANG Rui-xue1, TIAN Ling-ling2, SONG Chun-feng1, YUAN Xue-qin1, LI Xiao-yu1 |
1. College of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China 2. Beijing Textile Fibre Testing Institute, Beijing 100025, China |
|
|
Abstract A fast and nondestructive identification method to distinguish different types of fabric fibers is proposed in the present paper. A total of 214 fabric fiber samples, including wool, cashmere, terylene, polyamide, polyurethane, silk, flax, linen, cotton, viscose, cotton-flax blending, terylene-cotton blending, and wool-cashmere blending, were collected from Beijing Textile Fibre Inspection Institute. They contain yarns, raw wool or cashmere, and various fabric straps with different colors and different braid patterns. Sample presentation for measuring near infrared spectra of various textile fibers was tried to reduce the impact from the ununiformity of polymorphous fabric structure. Spectral data were pretreated using multiplicative signal correction (MSC) to reduce the influence of spectral noise and baseline shift. Classification of 12 kinds of fabric fibers in various braid patterns was studied using minimum spanning tree method and soft independent modeling of class analogy (SIMCA) classification based on principal component analysis of NIR spectra. The minimum spanning tree for the spectra of total samples shows that the samples in the same type fall almost into one cluster, but there are overlaps between some two different clusters of fabric fibers with very similar chemical compositions, such as wool and cashmere. Complete discrimination between cashmere and wool has been achieved using SIMCA. The results show that nondestructive and fast identification of fabric fibers using near infrared spectral technique is potentially feasible.
|
Received: 2009-05-20
Accepted: 2009-08-22
|
|
Corresponding Authors:
YUAN Hong-fu
E-mail: hfyuan@mail.buct.edu.cn
|
|
[1] James C L, Tsa L C, Yang C Y. Electrophoresis, 2006, 27: 3359. [2] Subramanian S, Karthik T. Journal of Biotechnology, 2005, 116: 153. [3] Edward G B. Handbook of Vibrational Spectroscopy, John M Chalmers, Peter R Griffiths (Editors), John Wiley & Sons Ltd, Chichester, 2002. [4] Howell H E, Davis J R. Textile Chemist and Colorist, 1991, 23(9): 69. [5] Henstock M E, et al. Advances in Recovery and Recycling: Concepts and Technology. Copenhagen: Hexagon,1993, 443. [6] Beck K. AATCC Textile Applications of NIR Technology Symposium. Asheville, North Carolina, USA, 1996. [7] Miryeong S, David S H, Danny E A, et al. Textile Research Journal, 2005, 75(8): 583. [8] Gishen M, Cozzolino D. Animal: An International Journal of Animal Bioscience, 2007, 1(6): 899. [9] LI Xiao-wei, ZHAO Huan-huan, ZHAO Long-lian, et al(李晓薇, 赵环环, 赵龙莲, 等). Journal of China Textile University(中国纺织大学学报), 2000, 26(3): 72. [10] WU Gui-fang, ZHU Deng-sheng, HE Yong(吴桂芳, 朱登胜, 何 勇). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(6): 1260. [11] James Rodgers J, Beck K. Textile Research Journal, 2009, 79(8): 675.
|
[1] |
LIU Zhen1*, LIU Li2*, FAN Shuo2, ZHAO An-ran2, LIU Si-lu2. Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 29-35. |
[2] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[3] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[4] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[8] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[9] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[10] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[11] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[12] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[13] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[14] |
XU Rong1, AO Dong-mei2*, LI Man-tian1, 2, LIU Sai1, GUO Kun1, HU Ying2, YANG Chun-mei2, XU Chang-qing1. Study on Traditional Chinese Medicine of Lonicera L. Based on Infrared Spectroscopy and Cluster Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3518-3523. |
[15] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
|
|
|
|