光谱学与光谱分析 |
|
|
|
|
|
Application of Raman Spectra Feature Extraction in Chemical Fiber Component Qualitative Identification |
QIAO Xi-ya1,DAI Lian-kui1,WU Jian-jian2 |
1. National Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China 2. Zhejiang Entry-Exit Inspection and Quarantine Bureau, Hangzhou 310012, China |
|
|
Abstract According to the characteristics of the textile fibers Raman spectra, a qualitative identification method based on Raman feature extraction is proposed. This fast method consists of spectrum measurement and spectral data processing algorithm, including spectrum preprocessing, feature extraction and matching recognition. It can be used to identify the components of fibers or fabrics, especially chemical fibers, which is an inspective difficulty in daily analytic work for its remarkable Raman feature. The authors performed an experiment to analyze 4 typical and widely used kinds of fibers as algorithm verification. They are terylene fiber, acrylic fiber, nylon fiber and rayon fiber. To identify the components of one test sample, first the authors set up feature tables of these 4 standard samples, which describe the features of their preprocessed spectra containing both position information and intensity information, then extract features of the test sample. The authors match these features with the tables and calculate the matching confidence coefficients of the results, which can be used to filter the unexpected matching results caused by accident and attain the final qualitative identification result. The experimental results confirm that this method is effective, efficient and expansible, which means it can be used to identify more actual fiber types by adding more standard spectra to the feature table database. In addition, it is a pure optical method, which needs only a small quantity of sample without any pretreatment. The whole identification process is damage-free, pollution-free and suitable for various kinds of fabrics. Compared to all existing methods, this Raman spectrum identification method can solve the limitation of efficiency, pollution, universality, and fill a gap in fabric inspection field.
|
Received: 2009-05-10
Accepted: 2009-08-20
|
|
Corresponding Authors:
QIAO Xi-ya
E-mail: lkdai@iipc.zju.edu.cn
|
|
[1] FZ/T 01057—2007 Test Method for Identification of Textile Fibers(纺织纤维鉴别试验方法). [2] ZHOU Jian-ping(周建萍). Advanced Textile Technology(现代纺织技术),2003,11(2): 32. [3] LUO Hong,LIANG Hai-bao,LIN Xiao-ping(骆 红,梁海保,林小平). China Fiber Inspection(中国纤检),2007,(6): 26. [4] WEN Yan-qing,ZHU Pu-xin,WU Da-cheng(温演庆,朱谱新,吴大诚). Progress in Textile Science & Technology(纺织科技进展),2007,(2): 1. [5] ZHOU Ying,XU Hui-rong,YING Yi-bin(周 莹,徐惠荣,应义斌). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2008,28(12): 2804. [6] HU Ji-ming,HU Jun(胡继明,胡 军). Chinese Journal of Light Scattering(光散射学报),1998,10(3): 141. [7] Lepot L, De Wael K, Gason F, et al. Journal of Raman Spectroscopy, 2005,36 (11) :1059. [8] Massonnet G, Buzzini P, Jochem G, et al. Journal of Forensic Sciences, 2005,50(5):1028. [9] LI Min-zan, HAN Dong-hai, WANG Xiu(李民赞, 韩东海, 王 秀). Spectrum Analysis Technology and Application(光谱分析技术及其应用). Beijing:Science Press(北京:科学出版社),2006. 115. [10] TIAN Gao-you,YUAN Hong-fu,LIU Hui-ying,et al(田高友,袁洪福,刘慧颖,等). Chinese Journal of Analytical Chemistry(分析化学),2004,32(9): 1125. [11] Dollish F R, Fateley W G, Bentley F F. Characteristic Raman Frequencies of Organic Compounds(有机化合物的特征拉曼频率). Beijing:China Chemistry Society(北京:中国化学会),1980. [12] DOU Yan-li,ZHANG Wan-xi,ZHANG Yu-jie,et al(窦艳丽,张万喜,张玉杰,等). Chinese Journal of Analytical Chemistry(分析化学),2006,34(11): 1656.
|
[1] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[2] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
[3] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
[4] |
WANG Xin-qiang1, 3, CHU Pei-zhu1, 3, XIONG Wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, ZHANG Wen-tao1, 3, LI Shu1, 3, WANG Fang-yuan1, 3*. Study on Monomer Simulation of Cellulose Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 164-168. |
[5] |
WANG Lan-hua1, 2, CHEN Yi-lin1*, FU Xue-hai1, JIAN Kuo3, YANG Tian-yu1, 2, ZHANG Bo1, 4, HONG Yong1, WANG Wen-feng1. Comparative Study on Maceral Composition and Raman Spectroscopy of Jet From Fushun City, Liaoning Province and Jimsar County, Xinjiang Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 292-300. |
[6] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
[7] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
[8] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[9] |
LU Wen-jing, FANG Ya-ping, LIN Tai-feng, WANG Hui-qin, ZHENG Da-wei, ZHANG Ping*. Rapid Identification of the Raman Phenotypes of Breast Cancer Cell
Derived Exosomes and the Relationship With Maternal Cells[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3840-3846. |
[10] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[11] |
GUO He-yuanxi1, LI Li-jun1*, FENG Jun1, 2*, LIN Xin1, LI Rui1. A SERS-Aptsensor for Detection of Chloramphenicol Based on DNA Hybridization Indicator and Silver Nanorod Array Chip[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3445-3451. |
[12] |
ZHU Hua-dong1, 2, 3, ZHANG Si-qi1, 2, 3, TANG Chun-jie1, 2, 3. Research and Application of On-Line Analysis of CO2 and H2S in Natural Gas Feed Gas by Laser Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3551-3558. |
[13] |
LIU Jia-ru1, SHEN Gui-yun2, HE Jian-bin2, GUO Hong1*. Research on Materials and Technology of Pingyuan Princess Tomb of Liao Dynasty[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3469-3474. |
[14] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
[15] |
ZHAO Ling-yi1, 2, YANG Xi3, WEI Yi4, YANG Rui-qin1, 2*, ZHAO Qian4, ZHANG Hong-wen4, CAI Wei-ping4. SERS Detection and Efficient Identification of Heroin and Its Metabolites Based on Au/SiO2 Composite Nanosphere Array[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3150-3157. |
|
|
|
|