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利用同步荧光光谱快速鉴别潲水油
引用本文:孙艳辉,安海洋,贾小丽,王娟. 利用同步荧光光谱快速鉴别潲水油[J]. 光谱学与光谱分析, 2012, 32(10): 2726-2729. DOI: 10.3964/j.issn.1000-0593(2012)10-2726-04
作者姓名:孙艳辉  安海洋  贾小丽  王娟
作者单位:滁州学院生物与食品工程学院,安徽 滁州 239012
基金项目:国家自然科学基金项目,安徽省自然科学基金项目,安徽省农产品质量与安全特色专业基金项目
摘    要:为快速鉴别潲水油,采用三维同步荧光光谱结合平行因子法解析潲水油的特征波长差(Δλ),并利用支持向量机建立潲水油鉴别模型。结果表明,潲水油的特征Δλ为60 nm;特征Δλ下的样品原始同步荧光光谱经过主成分分析提取5个主成分,以径向基函数(RBF)为核函数,利用网格搜索和6-fold交叉验证优化建模参数,得到惩罚因子C=512、核参数g=0.5,该条件下建立的模型对训练集和预测集的判别率均达到100%。采用同步荧光光谱可以快速、准确地鉴别潲水油。

关 键 词:潲水油  同步荧光光谱  平行因子法  支持向量机  鉴别  
收稿时间:2012-02-21

Rapid Identification of Hogwash Oil by Using Synchronous Fluorescence Spectroscopy
SUN Yan-hui , AN Hai-yang , JIA Xiao-li , WANG Juan. Rapid Identification of Hogwash Oil by Using Synchronous Fluorescence Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2012, 32(10): 2726-2729. DOI: 10.3964/j.issn.1000-0593(2012)10-2726-04
Authors:SUN Yan-hui    AN Hai-yang    JIA Xiao-li    WANG Juan
Affiliation:School of Bio & Food Engineering, Chuzhou University, Chuzhou 239012, China
Abstract:To identify hogwash oil quickly, the characteristic delta lambda of hogwash oil was analyzed by three dimensional fluorescence spectroscopy with parallel factor analysis, and the model was built up by using synchronous fluorescence spectroscopy with support vector machines (SVM). The results showed that the characteristic delta lambda of hogwash oil was 60 nm. Collecting original spectrum of different samples under the condition of characteristic delta lambda 60 nm, the best model was established while 5 principal components were selected from original spectrum and the radial basis function (RBF) was used as the kernel function, and the optimal penalty factor C and kernel function g were 512 and 0.5 respectively obtained by the grid searching and 6-fold cross validation. The discrimination rate of the model was 100 % for both training sets and prediction sets. Thus, it is quick and accurate to apply synchronous fluorescence spectroscopy to identification of hogwash oil.
Keywords:Hogwash oil  Synchronous fluorescence spectroscopy  Parallel factor analysis  Support vector machines  Identification  
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