光谱学与光谱分析 |
|
|
|
|
|
Adopting the Method of Correlation Coefficient to Improve the Accuracy of the Xylene Isomer’s Prediction Model |
WU Zhong-chen1, XU Xiao-xuan1, YANG Ren-jie1, YU Gang2, ZHANG Cun-zhou1 |
1. The Photonics Center of the Physics Institute, Nankai University, Tianjin 300071,China 2. Dupont Display, California, USA; Visiting Professor of Nankai University, Tianjin 300071, China |
|
|
Abstract This paper focuses on the detailed researches on the correlation coefficient in near infrared spectrum and points out its characteristics in multi-component solution; the paper also proves the principle of obtaining more accurate results when using high correlation coefficient for model building, and presents a practical experiment to test the conclusions by using para-xylene, meta-xylene, and ortho-xylene’s mixture.
|
Received: 2003-11-19
Accepted: 2004-03-25
|
|
Corresponding Authors:
WU Zhong-chen
|
|
Cite this article: |
WU Zhong-chen,XU Xiao-xuan,YANG Ren-jie, et al. Adopting the Method of Correlation Coefficient to Improve the Accuracy of the Xylene Isomer’s Prediction Model [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2005, 25(01): 136-140.
|
|
|
|
URL: |
https://www.gpxygpfx.com/EN/Y2005/V25/I01/136 |
[1] McClure W Fred, Ranford Steve L, Hammersley Mike J, Davies A M C. Applied Spectroscopy,1991,45(1):14A. [2] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong,QIANG Dong-mei(陆婉珍, 袁洪福, 徐广通, 强东梅). The Modern Analysis Technique of Near Infrared Spectrum(近红外光谱的现代分析技术). Beijing: China Petrochemical Press(北京:中国石化出版社),2000. 167. [3] FENG Xin-lu, SHI Yong-gang(冯新泸, 史永刚). The Near Infrared Spectrum and Its Application in Petrochemical Product(近红外光谱及其在石化产品中的应用). Beijing: China Petrochemical Press(北京:中国石化出版社). 2002,179. [4] XU Yong-qun, SUN Su-qin, XU Jin-wen(徐永群, 孙素琴, 许锦文). Chinese Journal of Spectroscopy Laboratory(光谱实验室),2002,19(5):606. [5] HU Wei, TANG Wan-ying, ZHOU Shen-fan, XU Fu-ming(胡 伟,唐婉莹,周申范,徐复铭). Chinese Journal of Analytical Chemistry(分析化学),1997,25(5):614. [6] WANG Hui-wen(王惠文). Partial Least-Squares Regression Method and Applications(偏最小二乘回归方法及其应用). Beijing: National Defence Industry Press(北京:国防工业出版社),1999. 11.
|
[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] |
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718. |
[5] |
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. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[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] |
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. |
[15] |
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. |
|
|
|
|