| 光谱学与光谱分析 |
|
|
|
|
|
| Study of Selecting Characteristic Wavelengths in Qualitative Analysis of Near Infrared Spectroscopy |
| YU Jing1, WEN Ya-dong2, WANG Luo-ping2, QIAN Ying-ying2, MA Xiang2, WANG Yi2, ZHAO Long-lian1, LI Jun-hui1* |
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Hongta Tobacco (Group) Co., Ltd., Yuxi 653100, China |
|
|
|
|
Abstract The present article proposed a method of stepwise selecting characteristic wavelengths based on minimum sum of correlation coefficients (SMCC). The maximization of the ratio of inter-class Euclidean distance to the sum of inner-class Euclidean distances was used as evaluation basis in qualitative analysis of near infrared spectroscopy. Seventeen kinds of grading tobacco leaf in 2012, provided by Hongta Group, were used as experimental samples to verify the effectiveness of this new method. CO1 was selected as the reference category and ten points were selected as characteristic wavelengths. The results indicated that the average value of inner-class Euclidean distance, calculated by characteristic wavelengths, was 1.69 times as large as that calculated by all wavelengths. The average value of inter-class Euclidean distance, calculated by characteristic wavelengths, was 3.70 times as large as that calculated by all wavelengths. The average value of the ratio of inter-class Euclidean distance to the sum of inner-class Euclidean distances, calculated by characteristic wavelength, was 2.21 times as large as that calculated by all wavelengths. The ratio of characteristic wavelengths was increased. The characteristic wavelengths can express the classical differences. It was showed that SMCC was an effective way to select characteristic wavelengths in qualitative analyses of near infrared spectroscopy.
|
|
Received: 2013-01-16
Accepted: 2013-03-25
|
|
|
|
Corresponding Authors:
LI Jun-hui
E-mail: caunir@cau.edu.cn
|
|
[1] YAN Yan-lu，ZHAO Long-lian，HAN Dong-hai，et al(严衍禄，赵龙莲，韩东海，等). Foundation and Application of NIR Spectra Analysis(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京：中国轻工业出版社), 2005. 13.
[2] SHI Yong-gang(史永刚). Chinese Journal of Spectroscopy Laboratory(光谱实验室), 1999, 16(3): 237.
[3] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立, 袁洪福, 陆婉珍). Progress In Chemistry(化学进展), 2004, 16(4): 528.
[4] WANG Ke(王 坷). Bulletin of Science and Technology(科技通报), 1997, 13(4): 211.
[5] FENG Yan-chun, HU Chang-qin(冯艳春, 胡昌勤). Analytical Chemistry(分析化学), 2009, 37(12): 1825.
[6] ZOU Xiao-bo, SHI Ji-yong, ZHAO Jie-wen，et al(邹小波, 石吉勇, 赵杰文, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2011, 30(5): 458.
[7] ZOU Xiao-bo, ZHAO Jie-wen，et al(邹小波, 赵杰文，等). Acta Optica Sinica(光学学报), 2007, 27(7): 1316.
[8] ZHANG Tao, JIANG Hui, CHEN Quan-sheng(张 涛, 江 辉, 陈全胜). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(8): 368.
[9] Lü Jian-feng, DAI Lian-kui(吕剑锋, 戴连奎). Chinese Journal of Analytical Chemistry(分析化学研究报告), 2007, 35(3): 340.
[10] WANG Yi, CHEN Bin, YE Jing, et al(王 毅, 陈 斌, 叶 静, 等). Computers and Applied Chemistry(计算机与应用化学), 2010, 27(5): 686.
[11] ZHANG Lu-da, YAN Yan-lu(张录达, 严衍禄). Journal of Beijing Agricultual University(北京农业大学学报), 1990, 16: 18. |
| [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] |
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] |
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. |
| [4] |
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. |
| [5] |
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. |
| [6] |
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. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [15] |
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. |
|
|
|
|
