第36卷，第5期 2016年5月
光谱学与光谱分析 Spectroscopy and Spectral Analysis
Vol.36,No.5+pp1571-1575
May,2016
SpectrumQuantitativeAnalysisBasedonBootstrap-SVM
ModelwithSmaulSampleSet
MAXiao,ZHAOZhong*,XIONGShan-hai
College of Information Science and Technology, Bejing University of Chemical Technology, Beijing100029 , China
Abstract A new spectrum quantitative analysis method based on Bootstrap-SVM model with small sample set is proposed in this paper. To build the spectrum quantitative analysis model for bitumen penetration index, al-together 29 bitumen samples were collected from 6 companies. Based on the collected 29 bitumen samples, spectrum quantitative analysis model with proposed method for predicting bitumen penetration index has been built. To verify the feasibility and effectiveness of the proposed method, the comparative experiments of pre dicting the bitumen sample penetration index with the proposed method, partial least squares (PLS) and sup port vector machine (SVM) have also been done. Comparative experiment results have verified that the mini-mum prediction root mean squared error (RMSE) is achieved by using the proposed Bootstrap-SVM model with the small sample set. The proposed method provides a new way to solve the problem of building the spec
trum quantitative analysis model with small sample set. Keywords
Spectrum quantitative analysis; Small sample set; Bootstrap; Support vector machines: Partial
least squares
中图分类号：0657.3
Introduction
文献标识码：A
D0I: 10. 3964/j. issn. 10000593[2016)051571-05
trum quantitative analysis based on small sample set, while the spectrum quantitative analysis based on large sample set has been well studiedi-. In the cases of small sample set, it
Spectrum quantitative analysis is an important research area in spectroscopy. Building a stable and accurate prediction model is the premise of spectrum quantitative analysis for un-known samples. Successful applications of spectrum quantita-tive analysis methods can now be seen in a wide variety areas, such as multiple linear regression (MLR)(i) , principle com-ponent regression (PCR)[23, partial least squares (PLS)[8), artificial neural networks (ANN)Et) and support vector ma-chine(SVM). MLR, PCR and PLS are usually applied to build the linear prediction model and ANN, SVM can be ap-plied to build the nonlinear prediction model. In the real appli-cations, it is often difficult to obtain complete information from samples due to the limitations of the sample sources. It is noticed that less effort has been made to the studies of spec
Received: 2015-03-02; aceepted: 2015-07-09
is usually difficult to build the stable and accurate prediction models for spectrum quantitative analysis with traditional methods. Hence, it is important to study the modeling meth ods for spectrum quantitative analysis with small sample set.
In this paper, how to build quantitative analysis model of the bitumen penetration index spectrum with small sample set is studied. Bitumen as pavement gumming material is widely used in road engineering.Bitumen penetration index is one of the important indicators which reflect the hardness of the pitch, consistency and ability to resist shear failure. Although the bitumen penetration index is a physical property, it is closely related with the content of the bitumen components. Aromatics saturation and aromatics have the high penetration indexes, while the penetration indexes of the resin and as
Foundation item: Fundamental Research Founds for Central Universities (YS1404)
Biography : MA Xiao, (1990—), Master degree candidate in Beijing University of Chemical Technology
email: maxiao2014job@163. com
Corresponding author
e-mail: zhaozhong@mail. buct. edu. cn