|
|
|
|
|
|
A Mid-Infrared Wavelength Selection Method Based on the Impact Value of Variables and Population Analysis |
ZHANG Feng1, TANG Xiao-jun1*, TONG Ang-xin1, WANG Bin1, TANG Chun-rui2, WANG Jie2 |
1. State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China
2. CCTEG Chongqing Engineering (Group) Co., Ltd., Chongqing 400042, China |
|
|
Abstract The Fourier transform infrared spectra absorption peaks of alkane gases are overlapping seriously in the mid-infrared region. A wavelength selection method based on the impact value of variables and population analysis (IVPA) is proposed to select the wavelength of five alkane gases infrared spectra composed with methane, eth, propane, iso-butane and n-butane. IVPA algorithm will go through a number of iterations to select variables. In each iteration, the variables are divided into sample space and variable space. The impact value of variables is calculated in the sample space. According to the impact value, the variables are divided into elite variables and normal variables by using the weighted bootstrap sampling technology. Meanwhile, in the variable space, the frequency of each variable in the optimal model is counted. Finally, the variables with a lower frequency of normal variables are eliminated by the exponential decay function, and the root means squared error (RMSE) value obtained during each iteration is recorded. The variable subset corresponding to the minimum RMSE as the final selected variable. The proposed algorithm is tested by alkane dataset, and the results are compared with stability competitive adaptive reweighted sampling (SCARS) and iteratively variable subset optimization (IVSO) variable selection method proposed in recent years. Taking iso-butane analysis results as an example, the minimum cross-sensitivity of IVSO, IVPA and IVPA to the other four gases was 0.67%, 0.56% and 0.11%, respectively. The maximum cross sensitivity was 1.69%, 1.49% and 1.02%, respectively. The relative errors of iso-butane prediction were 1.94%, 1.65% and 0.51%, respectively. The number of selected variables by the above three methods is 52, 17 and 13, respectively. The results show that the IVPA method selected the least variables, only 0.36% of the original spectral data, obtained the lowest cross sensitivity for the other four gases, and got the most accurate prediction for iso-butane, which shows that the proposed wavelength selection method can be applied to the absorption overlapping spectra, and can improve the prediction accuracy and efficiency of the analytical model.
|
Received: 2020-06-18
Accepted: 2020-10-08
|
|
Corresponding Authors:
TANG Xiao-jun
E-mail: xiaojun_tang@xjtu.edu.cn
|
|
[1] Tao L, Lin Z,Chen J, et al. Journal of Pharmaceutical & Biomedical Analysis, 2017, 145: 1.
[2] Shen X C, Xu L, Ye S B, et al. Optics Express, 2018, 26(10): A609.
[3] Jiang W, Lu C, Zhang Y, et al. Analytical Methods, 2019, 11: 3108.
[4] Tang X J, Li Y J, Zhu L J, et al. Chemometrics and Intelligent Laboratory Systems, 2015, 146: 371.
[5] ZHANG Feng, TANG Xiao-jun, TONG Ang-xin, et al(张 峰, 汤晓君, 仝昂鑫, 等). J. Infrared Millim. Waves(红外与毫米学报),2020, 39(3): 318.
[6] Tian H, Li M, Wang Y, et al. Infrared Physics & Technology, 2017, 86: 98.
[7] Xia Z, Sun Y, Cai C, et al. Sensors, 2019, 19: 1981.
[8] Chen J, Yang C, Zhu H, et al. Chemometrics and Intelligent Laboratory Systems, 2018, 182: 188.
[9] Xu D, Liu S, Cai Y, et al. Applied Optics, 2019, 58(14): 3913.
[10] Han Q J, Wu H L, Cai C B, et al. Analytica Chimica Acta, 2008, 612: 121.
[11] LIU Guo-hai, XIA Rong-sheng, JIANG Hui, et al(刘国海,夏荣盛,江 辉,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2014, 34(8): 2094.
[12] Zheng K, Li Q, Wang J, et al. Chemometrics & Intelligent Laboratory Systems, 2012, 112: 48.
[13] Wang W, Yun Y, Deng B, et al. RSC Advances, 2015, 5: 95771.
[14] Deng B C, Yun Y H, Cao D S, et al. Analytica Chimica Acta, 2016, 908: 63.
[15] Zhang F, Tang X, Tong A, et al. Sensors, 2020, 20(7): 2015. |
[1] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[2] |
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. |
[3] |
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. |
[4] |
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. |
[5] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[6] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[7] |
TIAN Ze-qi1, WANG Zhi-yong1, YAO Jian-guo1, GUO Xu1, LI Hong-dou1, GUO Wen-mu1, SHI Zhi-xiang2, ZHAO Cun-liang1, LIU Bang-jun1*. Quantitative FTIR Characterization of Chemical Structures of Highly Metamorphic Coals in a Magma Contact Zone[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2747-2754. |
[8] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
[9] |
LIU Rui-min, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Extraction of 3D Fluorescence Feature Information Based on Multivariate Statistical Analysis Coupled With Wavelet Packet Energy for Monitoring Quality Change of Cucumber During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2967-2973. |
[10] |
ZHANG Xiao-xu1, LIN Xiao-xian3, ZHANG Dan2, ZHANG Qi1, YIN Xue-feng2, YIN Jia-lu3, 4, ZHANG Wei-yue4, LI Yi-xuan1, WANG Dong-liang3, 4*, SUN Ya-nan1*. Study on the Analysis of the Relationship Between Functional Factors and Intestinal Flora in Freshly Stewed Bird's Nest Based on Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2452-2457. |
[11] |
YANG Dong-feng1, HU Jun2*. Accurate Identification of Maize Varieties Based on Feature Fusion of Near Infrared Spectrum and Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2588-2595. |
[12] |
LUO Dong-jie, WANG Meng, ZHANG Xiao-shuan, XIAO Xin-qing*. Vis/NIR Based Spectral Sensing for SSC of Table Grapes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2146-2152. |
[13] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
[14] |
WANG Bin1, 2, ZHENG Shao-feng2, GAN Jiu-lin1, LIU Shu3, LI Wei-cai2, YANG Zhong-min1, SONG Wu-yuan4*. Plastic Reference Material (PRM) Combined With Partial Least Square (PLS) in Laser-Induced Breakdown Spectroscopy (LIBS) in the Field of Quantitative Elemental Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2124-2131. |
[15] |
MA Zhong-kai1, LI Mao-gang2, YAN Chun-hua1, LIU Hao-sen1, TAO Shu-hao1, TANG Hong-sheng2, ZHANG Tian-long2*, LI Hua1, 2*. Application of Raman Spectroscopy Combined With Partial Least Squares Method in Rapid Quantitative Analysis of Diesel n-Butanol[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2153-2157. |
|
|
|
|