光谱学与光谱分析 |
|
|
|
|
|
DFT Feature Analysis of Corn Varieties Based on Near Infrared Spectra |
LI Yang-peng,LI Wei-jun*,LAI Jiang-liang |
Institute of Semiconductor, Chinese Academy of Sciences, Beijing 100083, China |
|
|
Abstract The present paper develops a new approach to the analyse of corn based on discrete Fourier transform (DFT). The experiment data is of 37 varieties of corn seed with the Fourier transform near infrared spectrometer in the wave number range from 4 000 to 12 000 cm-1. Analyse of the origin data found that as the wave number increases, the data noise also increases. Firstly, the paper defines a calculation method of interspecific and intraspecific differences Qm to measure the effectiveness of feature selection. Secondly, Qm was used to analyse the original data and DFT-section data. Experimental results show that by choosing data of DFT with wave number range from 4 000 to 7 085 cm-1, the mean value and the peak value of the the Qm curve markedly improved relative to the full band original data. The mean value was enhanced from the original 4.804 9 to 8.513 8, and the max of the peak value was enhanced from the original 35.924 0 to 60.821 6, while the min of the peak value was enhanced from the original 2.891 8 to 3.741 5. Data feature points (Qm value of large point) are more concentrated than the original data after DFT. Such a result is most conducive to extracting the characteristics of corn seed.
|
Received: 2010-02-22
Accepted: 2010-07-08
|
|
Corresponding Authors:
LI Wei-jun
E-mail: wjli@semi.ac.cn
|
|
[1] LI Xiao-li, TANG Yue-ming, HE Yong, YING Xia-fang(李晓丽,唐月明,何 勇,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(3): 578. [2] 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 Near-Infrared Spectroscopy 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. [3] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍,袁洪福,徐广通,等). Modern Near Infrared Spectroscopy Analytical Technology(Second Edition)(现代近红外光谱分析技术,第2版). Beijing: China Petrochemical Press(北京: 中国石化出版社), 2007. [4] 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. [5] WANG Tie-gu, LIU Xin-xiang, KU Li-xia, et al(王铁固,刘新香,库丽霞,等). Journal of Maize Sciences(玉米科学), 2008, 16(3): 57. [6] FANG Li-min, LIN Min(方利民,林 敏). Chinese Journal of Analytical Chemistry(分析化学), 2008, 36(6): 815. [7] Baye Tesfaye M, Pearson Tom C, Settles A Mark. Journal of Cereal Science,2006, 43: 236. [8] LI Shang-yu, CHEN Yang, WANG Chun-yan, et al(李尚禹,陈 阳,王春艳,等). Journal of Molecular Science(分子科学学报),2007, 23(3): 220. [9] DING Nian-ya, LI Wei, FENG Xin-wei, et al(丁念亚,黎 薇,冯昕韡,等). Computers and Applied Chemistry(计算机与应用化学), 2008, 25(4): 499. [10] ZHAO Jie-wen, HU Huai-ping, ZOU Xiao-bo(赵杰文,呼怀平,邹小波). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2007, 23(4): 149. [11] HAO Yong, CAI Wen-sheng, SHAO Xue-guang(郝 勇,蔡文生,邵学广). Chemical Journal of Chinese Universities(高等学校化学学报), 2009, 30: 28. [12] ZHANG Hui,eu al(张 卉,等). Chinese Journal of Spectroscopy Laboratory(光谱实验室), 2007, 24(3): 380. [13] HAN Liang-liang, MAO Pei-sheng, WANG Xin-guo, et al(韩亮亮,毛培胜,王新国,等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2008, 27(2): 86.
|
[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] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
[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] |
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. |
|
|
|
|