| 光谱学与光谱分析 |
|
|
|
|
|
| Comparison of the Models of Mixed Liquid Samples under Different Near-Infrared Spectral Resolutions |
| WANG Dong,YE Sheng-feng,MIN Shun-geng*,HAN Chen,HUANG Yue |
| Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China |
|
|
|
|
Abstract Models of near-infrared spectra under different resolutions, 1, 4, 16, 32 and 64 cm-1, were studied with the mixed liquid samples of 4 components. The calibration models were developed by the method of partial least square and the validations of the models were carried out by the method of full cross. The value of target function was used to estimate the models performance. For the calibration models developed by the raw spectra, the target function values of benzene and benzaldehyde reached the max value with the resolution of 1 cm-1, the target function value of toluene reached the max value with the resolution of 4 cm-1, and the target function value of chlorobenzene reached the max value with the resolution of 16 cm-1; for calibration models developed by the 1st derivative spectra, the target function values of the four components all reached the max value with the resolution of 1 cm-1. The result suggested that, first of all, the resolution of the instrument will influence the quantitative analysis result. For the component with spectrum overlapped seriously, a higher resolution is good for the quantitative analysis, while for the analyte with a broad real band width, a lower resolution can be adopted in order to assure the signal to noise ratio. In addition, the influence of the resolution of the instrument is different for different components. Furthermore, the quantitative analysis result can be affected by both the SNR of the raw spectra and the band width of different components in the analyte, and a higher resolution is good for the quantitative model when the SNR of the spectra is assured.
|
|
Received: 2008-03-26
Accepted: 2008-06-28
|
|
|
|
Corresponding Authors:
MIN Shun-geng
E-mail: minsg@263.net
|
|
[1] JIANG Su, MA Xiang, CHEN Yong-fu, et al(江苏, 马翔, 陈永福, 等). Chinese Journal of Spectroscopy Laboratory(光谱实验室), 2006, 23(3): 633.
[2] WU Yu-ping, XIA Zhen-yuan, SHAO Yan, et al(吴玉萍, 夏振远, 邵岩, 等). Chinese Journal of Analysis Laboratory(分析试验室), 2007, 26(3): 73.
[3] LIU Fei, WANG Li, HE Yong, et al(刘飞, 王莉, 何勇, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(3): 586.
[4] WANG Jing-juan, ZHANG Gui-jun, ZHOU Qun, et al(王晶娟, 张贵君, 周群, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(2): 327.
[5] REN Wei-bo, HAN Jian-guo, ZHANG Yun-wei, et al(任卫波, 韩建国, 张蕴薇, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(3): 555.
[6] LI Xiao-li, TANG Yue-ming, HE Yong, et al(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.
[7] LIU Bo-ping, QIN Hua-jun, LUO Xiang, et al(刘波平, 秦华俊, 罗香, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(10): 2005.
[8] SHAO Yong-ni, HE Yong, BAO Yi-dan(邵咏妮, 何勇, 鲍一丹). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(3): 602.
[9] ZHU Rong-guang, HAN Lu-jia, YANG Zeng-ling, et al(朱荣光, 韩鲁佳, 杨增玲, 等). J. Infrared Millim. Waves(红外与毫米波学报), 2006, 25(4): 267.
[10] Jorge Villar S E, Edwardsb H G M. Vibrational Spectroscopy, 2005, 39: 88.
[11] Stewart F Parker, Philip B Tooke. Spectrochimica Acta Part A, 1997, 53: 2245.
[12] ZHAO Li-li, ZHAO Long-lian, LI Jun-hui, et al(赵丽丽, 赵龙莲, 李军会, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(1): 41.
[13] DUAN Yan-qing, YANG Tao, KONG Xiang-yong, et al(段焰青, 杨涛, 孔祥勇, 等). Journal of Yunnan University(云南大学学报), 2006, 28(4): 340.
[14] WANG Yi-bing, WANG Hong-yu, ZHAI Hong-ju, et al(王一兵, 王红宇, 翟宏菊, 等). Chinese Journal of Analytical Chemistry(分析化学), 2006, 34(5): 699.
[15] YUAN Bo, DOU Xiao-ming(袁波, 窦晓鸣). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(11): 1319.
[16] LI Jun-xia, MIN Shun-geng, ZHANG Hong-liang, et al(李君霞, 闵顺耕, 张洪亮, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(5): 833.
|
| [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] |
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. |
| [4] |
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. |
| [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] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
| [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. |
|
|
|
|
