Modeling of oil near-infrared spectroscopy based on similarity and transfer learning algorithm
Yifei Wang1,2, Kai Wang1,2, Zhao Zhou1,2(), Wenli Du1,2()
1. Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education), East China University of Science and Technology, Shanghai 200237, China 2. School of information science and engineering, East China University of Science and Technology, Shanghai 200237, China
Near-infrared spectroscopy mainly reflects the frequency-doubled and total-frequency absorption information of hydrogen-containing groups (O‒H, C‒H, N‒H, S‒H) in organic molecules for near-infrared lights with different wavelengths, so it is applicable to testing of most raw materials and products in the field of petrochemicals. However, the modeling process needs to collect a large number of laboratory analysis data. There are many oil sources in China, and oil properties change frequently. Modeling of each raw material is not only unfeasible but also will affect its engineering application efficiency. In order to achieve rapid modeling of near-infrared spectroscopy and based on historical data of different crude oils under different detection conditions, this paper discusses about the feasibility of the application of transfer learning algorithm and makes it possible that transfer learning can assist in rapid modeling using certain historical data under similar distributions under a small quantity of new data. In consideration of the requirement of transfer learning for certain similarity of different datasets, a transfer learning method based on local similarity feature selection is proposed. The simulation verification of spectral data of 13 crude oils measured by three different probe detection methods is performed. The effectiveness and application scope of the transfer modeling method under different similarity conditions are analyzed.
. [J]. Frontiers of Chemical Science and Engineering, 2019, 13(3): 599-607.
Yifei Wang, Kai Wang, Zhao Zhou, Wenli Du. Modeling of oil near-infrared spectroscopy based on similarity and transfer learning algorithm. Front. Chem. Sci. Eng., 2019, 13(3): 599-607.
Y L Yan. The Basis and Application of Near Infrared Spectroscopy. Beijing: China Light Industry Press, 2005, 286–564 (in Chinese)
2
W Z Lu. Modern Near Infrared Spectroscopy Analysis Technology. Beijing: China Petrochemical Press, 2007, 14–26 (in Chinese)
3
J Workman Jr. A brief review of near infrared in petroleum product analysis. Journal of Near Infrared Spectroscopy, 1996, 4(1): 69 https://doi.org/10.1255/jnirs.77
4
H Oja. Multivariate Linear Regression. New York: Springer, 2010, 183–200
5
N Tormod, M Harald. Principal component regression in NIR analysis: Viewpoint, background details and selection of components. Journal of Chemometrics, 1988, 2(2): 155–167 https://doi.org/10.1002/cem.1180020207
6
P Geladi, B R Kowalski. Partial least-squares regression: a tutorial. Analytica Chimica Acta, 1985, 185(86): 1–17
7
Y He, X Li, X Deng. Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model. Journal of Food Engineering, 2007, 79(4): 1238–1242 https://doi.org/10.1016/j.jfoodeng.2006.04.042
8
H Shimodaira. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 2000, 90(2): 227–244 https://doi.org/10.1016/S0378-3758(00)00115-4
9
Y He. Modelling of near-infrared spectroscopy based on semi-supervised learning and transfer learning. Dissertation for the Doctor Degree. Shandong: Ocean University of China, 2012
10
S J Pan, Q Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345–1359 https://doi.org/10.1109/TKDE.2009.191
B Tan, Y Song, E Zhong, Q Yang. Transitive transfer learning. Acm Sigkdd International Conference on Knowledge Discovery & Data Mining, 2015, 1155–1164
13
B Tan, Y Zhang, S J Pan, Q Yang. Distant domain transfer learning. Association for the Advance of Artificial Intelligence, 2017, 2604–2610
14
J Gao. The application of near infrared spectroscopy in oil quality analysis. Dissertation for the Master Degree. Jiangsu: Nanjing Tech University, 2005, 11–12
15
T V Karstang, K Valheim. Multivariate prediction and background correction using local modeling and derivative spectroscopy. Analytical Chemistry, 1996, 63(8): 767–772 https://doi.org/10.1021/ac00008a006
16
C H Zhao, M H Tian, J W Li. Research progress on spectral similarity metrics. Journal of Harbin Engineering University, 2017, 38(8): 1179–1189 (in Chinese)
17
C Wang, M Gong, M Zhang, Y Chan. Unsupervised hyperspectral image band selection via column subset selection. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7): 1411–1415 https://doi.org/10.1109/LGRS.2015.2404772
18
A Schlamm, D Messinger. Improved detection clustering of hyperspectral image date by preprocessing with a euclidean distance transformation. WHISPERS, 2011, 1(2): 1–4
19
Y Zhong, X Lin, L Zhang. A support vector conditional random fields classifier with a Mahalanobis distance boundary constraint for high spatial resolution remote sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(4): 1314–1330 https://doi.org/10.1109/JSTARS.2013.2290296
20
F A Kruse, A B Lefkoff, J W Boardman, K B Heidebrecht, A T Shapiro, P J Barloon. The spectral image processing systems (SIPS)-interactive visualization and analysis of imaging spectrometer data. Aip Conference, 1993, 283(1): 192–201
21
C I Chang. Spectral information divergence for hyperspectral image analysis. IEEE International Geoscience & Remote Sensing Symposium, 1999, 509–511
22
S J Pan, J T Kwok, Q Yang, J J Pan. Adaptive localization in a dynamic WiFi environment through multi-view learning. Association for the Advance of Artificial Intelligence, 2007, 1108–1113
23
J C Granahan, J N Sweet. An evaluation of atmospheric correction techniques using the spectral similarity scale. IEEE International Geoscience & Remote Sensing Symposium, 2001, 2022–2024
24
S J Pan, I W Tsang, J T Kwok, Q Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 2011, 22(2): 199–210 https://doi.org/10.1109/TNN.2010.2091281
W Y Dai, Q Yang, G R Xue, Y Yu. Boosting for transfer learning. International Conference on Machine Learning, Corvalis, 2007, 238(6): 193–200
27
Y Freund, R E Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 1997, 55(1): 119–139 https://doi.org/10.1006/jcss.1997.1504
28
S H Zhou, W L Du. Modeling of ethylene cracking furnace yields based on transfer learning. CIESC Journal, 2014, 65(12): 4921–4928
