| 光谱学与光谱分析 |
|
|
|
|
|
| Determination of Cotton Content in Cotton/Ramie Blended Fabric by NIR Spectra and Variable Selection Methods |
| SUN Tong1, GENG Xiang2，3*, LIU Mu-hua1* |
1. Optics-Electronics Application of Biomaterials Lab，Jiangxi Agricultural University，Nanchang 330045，China
2. Technical Center of Inspection and Quarantine，Jiangxi Entry-Exit Inspection and Quarantine Bureau, Nanchang 330038，China
3. Jiangxi Province Engineering Research Center of Infrared Spectroscopy Application，Nanchang 330038，China |
|
|
|
|
Abstract Rapid detection of textile fiber components is very important for production process of quality control, trading and market surveillance. The objective of this research was to assess cotton content in cotton/ramie blended fabric quickly by near infrared (NIR) spectrum technology and variable selection methods. Reflectance spectra of samples were acquired by a NIRFlex N-500 Fourier spectroscopy in the range of 4 000～10 000 cm-1, primary election of spectral range and pretreatment analysis were conducted first. Then, three variable selection methods such as UVE (uninformative variables elimination), SPA (successive projections algorithm) and CARS (competitive adaptive reweighted sampling) were used to select sensitive variables. After that, PLS (partial least squares) was used to develop calibration model for cotton content of cotton/ramie blended fabric, and the best calibration model was used to predict cotton content of samples in prediction set. The result indicates that range of 4 052～8 000 cm-1 is optimal spectral range for cotton content modeling. CARS method is an efficient method to improve model performance, the correlation coefficient and root mean square error of CARS-PLS for calibration and prediction sets are 0.903, 0.749 and 8.01%, 12.93%, respectively. So NIR spectra combined with CARS method is feasible for assessing cotton content in cotton/ramie blended fabric, and CARS method can simplify model, improve model performance.
|
|
Received: 2013-12-14
Accepted: 2014-03-26
|
|
|
|
Corresponding Authors:
GENG Xiang, LIU Mu-hua
E-mail: gengxiang2005@sina.com; suikelmh@sina.com
|
|
[1] LI Qing-shan(李青山). Handbook of Discrimination of Texitle Fibres(纺织纤维鉴别手册). Beijing: China Textile & Apparel Press(北京: 中国纺织出版社), 2003. 303.
[2] Sun T, Lin H J, Xu H R, et al. Postharvest Biology and Technology, 2009, 51(1): 86.
[3] HAN Dong-hai, CHANG Dong, SONG Shu-hui, et al(韩东海, 常 冬, 宋曙辉, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2013, 44(7): 174.
[4] Zamora-Rojas E, Pérez-Marín D, De Pedro-Sanz E, et al. Chemometrics and Intelligent Laboratory Systems, 2012, 114: 30.
[5] JIANG Bo-hai, WANG Qing, WANG Shi-sheng, et al(姜博海, 汪 晴, 王世盛, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(1): 74.
[6] Teraoka R, Abe H, Sugama T, et al. Journal of Natural Medicines, 2012, 66(2): 329.
[7] Dantas H V, Barbosa M F, Nascimento E C L, et al. Talanta, 2013, 106: 158.
[8] Srivastava A, Chowdhury M K, Sharma S, et al. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2013, 2(1): 615.
[9] CHAI Jin-chao, JIN Shang-zhong(柴金朝, 金尚忠). Journal of Textile Research(纺织学报), 2009, 30(4): 55.
[10] CHEN Bin, CUI Guang, JIN Shang-zhong, et al(陈 斌, 崔 广, 金尚忠, 等). Journal of Jiangsu University(Natural Science Edition)(江苏大学学报·自然科学版), 2007, 28(3): 185.
[11] Fortier C A, Rodgers J E, Cintron M S, et al. Textile Research Journal, 2011, 81(3): 230.
[12] Rodgers J, Montalvo J, Davidonis G, et al. Textile Research Journal, 2010, 80(9): 780.
[13] NI Zhen, HU Chang-qin, FENG Fang(尼 珍, 胡昌勤, 冯 芳). Chinese Journal of Pharmaceutical Analysis(药物分析杂志), 2008, 28(5): 824.
[14] Centner V, Massart D L, de Noord O E, et al. Analytical Chemistry, 1996, 68(21)：3851.
[15] Araújo M C U, Saldanha T C B, Galvo R K H, et al. Chemometrics and Intelligent Laboratory Systems, 2001, 57(2): 65.
[16] Galvo R K H, Araújo M C U, Fragoso W D, et al. Chemometrics and Intelligent Laboratory Systems, 2008, 92(1): 83.
[17] Li H, Liang Y, Xu Q, et al. Analytica Chimica Acta, 2009, 648(1): 77. |
| [1] |
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. |
| [2] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
| [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] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
| [5] |
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. |
| [6] |
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. |
| [7] |
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. |
| [8] |
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. |
| [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] |
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. |
| [13] |
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. |
| [14] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [15] |
LIU Bo-yang1, GAO An-ping1*, YANG Jian1, GAO Yong-liang1, BAI Peng1, Teri-gele1, MA Li-jun1, ZHAO San-jun1, LI Xue-jing1, ZHANG Hui-ping1, KANG Jun-wei1, LI Hui1, WANG Hui1, YANG Si2, LI Chen-xi2, LIU Rong2. Research on Non-Targeted Abnormal Milk Identification Method Based on Mid-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3009-3014. |
|
|
|
|
