Detection and Discrimination of Liquid Precursor Chemicals Using EDXRS and Principal Component Analysis

doi:10.7503/cjcu20120726

Chem. J. Chinese Universities ›› 2013, Vol. 34 ›› Issue (5): 1127.doi: 10.7503/cjcu20120726

• Analytical Chemistry • Previous Articles Next Articles

Detection and Discrimination of Liquid Precursor Chemicals Using EDXRS and Principal Component Analysis

ZHONG Yu^1,2, LI Wei², SUN Bai², YU Dao-Yang², LI Min-Qiang², LIU Jin-Huai²

1. Anhui Provincial Laboratory of Biomimetic Sensor and Detecting Technology, West Anhui University, Lu'an 237012, China;
2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China

Received:2012-08-06 Online:2013-05-10 Published:2013-05-10

Abstract

Abstract:

The scattering spectra of eight kinds of liquid precursor chemicals have been obtained using energy dispersive X-ray scattering(EDXRS) technology. The results show that the EDXRS spectra of liquid precursor chemicals are unique under the same probe conditions. With the combination of EDXRS and principal component analysis(PCA), it is shown that the main information of X-ray scattering spectra can be expressed by first and second principal component extracted by PCA, and a satisfactory clustering effect for the classification and discrimination of liquid precursor chemicals is observed in the two-dimensional scores plot. The research de-monstrates that liquid precursor chemicals can be detected and classified by EDXRS and PCA, which shows the potential application to monitor and control of concealed liquid precursor chemicals.

Key words: Energy dispersive X-ray scattering(EDXRS), Principal component analysis(PCA), Liquid precursor chemicals, Detection, Discrimination

CLC Number:

O657.3

TrendMD:

ZHONG Yu, LI Wei, SUN Bai, YU Dao-Yang, LI Min-Qiang, LIU Jin-Huai. Detection and Discrimination of Liquid Precursor Chemicals Using EDXRS and Principal Component Analysis[J]. Chem. J. Chinese Universities, 2013, 34(5): 1127.

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Detection and Discrimination of Liquid Precursor Chemicals Using EDXRS and Principal Component Analysis

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