|
|
|
|
|
|
An Outlier Determination Method for Near-Infrared Spectroscopy Based on the Simplified Orthogonal Distance |
MENG Dan-rui, FU Bo, XU Ke-xin, LIU Rong* |
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China |
|
|
Abstract Fast detecting and eliminating the outliers is of great significance to improve the reliability of the near-infrared(NIR) spectroscopy analysis. In this paper, the principle of outlier determination method based on orthogonal distance and robust principal component analysis was introduced firstly with the analysis of its limitations. Then an outlier determination method based on the simplified orthogonal distance was proposed, where the spectra of the samples with high concentration were employed to estimate the first robust principal component directly and the statistical parameters of the orthogonal distance were obtained with repeated measurements to detect outliers. Finally, the outliers caused by the temperature fluctuations in the NIR transmission spectra of glucose aqueous solutions and 2% Intralipid solutions, were determined by these two methods. Results showed that, for the orthogonal distance combined with robust principal component analysis method, all the outliers induced by temperature variations could be correctly determined under the collapse value of 40%, while the false negative rates for the glucose aqueous solutions and Intralipid solutions under the collapse value of 25% were 54.5% and 72.7%, respectively. Besides, all the outliers induced by temperature variations also could be recognized with the method based on the simplified orthogonal distance, which saves the need for collapse value and shortens the tine for measurement. Therefore, the outlier determination method based on the simplified orthogonal distance is more practical than the robust principal component analysis.
|
Received: 2017-05-18
Accepted: 2017-11-06
|
|
Corresponding Authors:
LIU Rong
E-mail: rongliu@tju.edu.cn
|
|
[1] TIAN Xiang, LIU Si-chen, WANG Hai-gang, et al(田 翔,刘思辰,王海岗,等). Food Science(食品科学), 2017,38(16): 1.
[2] Mclauchlin A R, Ghita O, Gahkani A. Polymer Testing, 2014, 38(18): 46.
[3] Yadav J, Rani A, Singh V, et al. Biomedical Signal Processing & Control, 2015, 18: 214.
[4] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2011. 89.
[5] Li W, Qu H. Chemometrics & Intelligent Laboratory Systems, 2016, 152: 140.
[6] Cárdenas V, Cordobés M, Blanco M, et al. Journal of Pharmaceutical & Biomedical Analysis, 2015, 114: 28.
[7] Shen W, Kong Q, Wang J, et al. Mathematical Problems in Engineering, 2015, 2015(5): 1.
[8] HAO Jian-ming, LI Zong-nan, XIE Jing(郝建明, 李宗南, 谢 静). Journal of Huazhong Agricultural University(华中农业大学学报), 2014, 33(5): 135.
[9] YU Fan, LI Ji-xin(于 帆,李纪鑫). Journal of Xi’an Technological University(西安工业大学学报), 2014, 34(1): 38.
[10] Li Z, Xu G, Wang J, et al. Chinese Journal of Analytical Chemistry, 2016, 44(2): 305.
[11] Engel J, Blanchet L, Buydens L M C, et al. Talanta, 2012, 99: 426.
[12] ZHANG Li-zhuo(张立卓). College Mathematics(大学数学), 2014,30(2): 94.
[13] Hubert M, Rousseeuw P J, Branden K V. Technometrics, 2010, 47(1): 64. |
[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] |
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. |
[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] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
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. |
[15] |
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. |
|
|
|
|