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1. 燕山大学 测试计量技术及仪器河北省重点实验室, 河北 秦皇岛 066004
2. 燕山大学 信息科学与工程学院,河北 秦皇岛,066004
纸质出版日期:2018-4-5,
网络出版日期:2017-11-15,
收稿日期:2017-7-24,
修回日期:2017-10-19,
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潘钊, 崔耀耀, 吴希军等. 三维荧光光谱结合Tchebichef矩快速鉴别掺伪芝麻油[J]. 发光学报, 2018,39(4): 568-572
PAN Zhao, CUI Yao-yao, WU Xi-jun etc. 3D Fluorescence Spectra Combined with Tchebichef Moments for Rapid Identification of Doping Sesame Oil[J]. Chinese Journal of Luminescence, 2018,39(4): 568-572
潘钊, 崔耀耀, 吴希军等. 三维荧光光谱结合Tchebichef矩快速鉴别掺伪芝麻油[J]. 发光学报, 2018,39(4): 568-572 DOI: 10.3788/fgxb20183904.0568.
PAN Zhao, CUI Yao-yao, WU Xi-jun etc. 3D Fluorescence Spectra Combined with Tchebichef Moments for Rapid Identification of Doping Sesame Oil[J]. Chinese Journal of Luminescence, 2018,39(4): 568-572 DOI: 10.3788/fgxb20183904.0568.
应用FS920荧光光谱仪测定样品的三维荧光光谱数据,直接利用Tchebichef矩提取三维光谱灰度图的特征信息,然后对其进行聚类分析,最后通过逐步回归建立样本中各成分的线性模型。聚类分析能够准确识别掺伪芝麻油,并正确解析其组成成分,得到的线性模型相关系数
R
>
0.99。研究表明,Tchebichef矩能够有效提取光谱的特征信息,应用于掺伪芝麻油鉴别可获得良好的定性和定量分析结果。
The three-dimensional fluorescence spectra of the samples were measured by FS920 fluorescence spectrometer
and the characteristic information of three-dimensional spectral grayscale was extracted directly by Tchebichef moments. And then
the cluster analysis was carried out. Finally
a linear model of each component in the sample was established by the stepwise regression. Clustering analysis can identify doping sesame oil with a high recognition rate and can correctly analyze its constituent components.
R
-squared of the obtained linear model is greater than 0.99. The results show that Tchebichef moments can effectively extract the characteristic information of the spectrum and can be used to identify the doping sesame oil and obtain good qualitative and quantitative analysis results.
三维荧光光谱Tchebichef矩聚类分析定量分析掺伪鉴别
three-dimensional fluorescence spectroscopyTchebichef momentsclustering analysisquantitative analysisadulteration identification
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张辉, 周健, TOUMOULIN C, 等. Tchebichef矩的快速算法[J]. 东南大学学报(自然科学版), 2006, 36(5):857-862. ZHANG H, ZHOU J, TOUMOULIN C, et al.. Fast algorithm for Tchebichef moments[J]. J. Southeast Univ.(Nat. Sci. Ed.), 2006, 36(5):857-862. (in Chinese)
梁曼, 黄富荣, 何学佳, 等. 荧光光谱成像技术结合聚类分析及主成分分析的藻类鉴别研究[J]. 光谱学与光谱分析, 2014, 34(8):2132-2136. LIANG M, HUANG F R, HE X J, et al.. Identification of algae by fluorescence spectroscopy combined with cluster analysis and principal component analysis[J]. Spectrosc. Spect. Anal., 2014, 34(8):2132-2136. (in Chinese)
刘丙新, 李颖, 韩亮, 等. 基于光谱反射率数据的水面油种鉴别研究[J]. 光谱学与光谱分析, 2016, 36(4):1100-1103. LIU BI X, LI Y, HAN L, et al.. Identification of surface oil based on spectral reflectance data[J]. Spectrosc. Spect. Anal., 2016, 36(4):1100-1103. (in Chinese)
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