|
|
|
|
|
|
| Theoretical Analysis and Verification of Prediction Variances and Confidence Limits for Global Temperature Compensation Modeling Approaches |
| SHI Ting, LUAN Xiao-li*, LIU Fei |
| Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education, Institute of Automation, Jiangnan University, Wuxi 214122, China |
|
|
|
|
Abstract Temperature fluctuations affect the action between hydrogen groups, which causes the changes of absorption intensity and peak position of the near infrared (NIR) spectrum，which results in less accuracy of the NIR analyzer. This article studies the prediction accuracy of a type of global temperature compensation models for NIR spectrometric analysis from the aspects of prediction variance and confidence limit respectively. In the designed experiments with continuous temperature changing, NIR spectra are collected with an equal time interval. Hence, the continuous impact on the principal compoments of the NIR spectra caused by temperature is observed and analyzed, which illustrates the mechanism of temperature effection to the model prediction results. As verified by the experimental，the measurement of viscosity of an industrial polymeric material is carried out combined with different modelling methods. According to the experimental verification, the accuracy of non-temperature compensation model and global temperature compensation model are as follows, respectively: RMSEC=0.243 0, Rc=0.871 6, RMSEP=0.243 2, Rp=0.869 3; RMSEC=0.258 2, Rc=0.870 6, RMSEP=0.265 2, Rp=0.856 0. The maximum prediction confidence intervals of these two types of models are about 1.8 and 0.9 kPa·s respectively. Therefore, it can be observed that the modeling accuracy of global temperature compensation model is slightly worse, but the predition precision is much better compared with non-temperature compensated model. Both results of theoretical analysis and experimental verification illustrate that the global temperature compensation modelling methods offer the more accurate models and better robustness and reliability.
|
|
Received: 2016-06-16
Accepted: 2016-10-29
|
|
|
|
Corresponding Authors:
LUAN Xiao-li
E-mail: xlluan@jiangnan.edu.cn
|
|
[1] Huang X Y, Teye E, Sam-Amoah L K, et al. Analytical Methods, 2014, 6(14): 5008.
[2] Kang J, Cai W S, Shao X G. Talanta, 2011, 85(1): 420.
[3] Shao X G, Kang J, Cai W S. Talanta, 2010, 82(3): 1017.
[4] Liu X, Huang C J, Han L J. Energy & Fuels, 2015, 29(10): 6450.
[5] Shan R F, Zhao Y, Fan M L, et al. Talanta, 2015, 131(131): 170.
[6] Kuda-Malwathumullage C P S, Small G W. Journal of Applied Polymer Science，2014: 131(13): 378.
[7] Yao Y, Chen H R, Xie L J, et al. Journal of Food Engineering, 2013, 119(1): 22.
[8] Magwaza L S, Opara U L, Nieuwoudt H, et al. Food and Bioprocess Technology, 2012, 5(2): 425.
[9] Wang H L, Peng J Y, Xie C Q, et al. Sensors, 2015, 15(5): 11889.
[10] Zhang X D, Chang M, Xing L J, et al. Infrared Physics & Technology, 2010, 53(3): 177.
[11] Mehmood T, Liland K H, Snipen L, et al. Chemometrics and Intelligent Laboratory Systems, 2012, 118(16): 62.
[12] Cheng Q, Tezcan J, Cheng J. Pattern Recognition Letters, 2014, 40(40): 88.
[13] Tong P J, Du Y P, Zheng K Y, et al. Chemometrics & Intelligent Laboratory Systems, 2015, 143: 40.
[14] Smolinska A, Blanchet L, Buydens L M C, et al. Analytica Chimica Acta, 2012, 750(11): 82. |
| [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 Xiao-dian1, TANG Nian1, ZHANG Man-jun1, SUN Dong-wei1, HE Shu-kai2, WANG Xian-zhong2, 3, ZENG Xiao-zhe2*, WANG Xing-hui2, LIU Xi-ya2. Infrared Spectral Characteristics and Mixing Ratio Detection Method of a New Environmentally Friendly Insulating Gas C5-PFK[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3794-3801. |
| [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. |
|
|
|
|
