平行因子结合支持向量机对多环芳烃的荧光检测

Fluorescence Detection of Polycyclic Aromatic Hydrocarbons by Parallel Factor Combined with Support Vector Machine

基于荧光检测机理,将平行因子与支持向量机(SVM)算法相结合,对多环芳烃中的苊、芴和萘进行检测。将荧光光谱数据预处理后作为训练集,输入到粒子群优化的SVM算法中建立分类模型;利用核一致性分析、残差平方和分析以及迭代次数分析方法确定成分数;采用得到的最佳成分数进行平行因子分解,将得到的发射载荷矩阵作为测试集输入到SVM的分类模型中,分类正确率为100%,最终得到苊、芴和萘的回收率分别为100.45%±6.25%、100.10%±6.39%和95.07%±7.46%。所用算法避免了人为操作增加的时间复杂性及主观因素造成的误差,为多环芳烃的荧光检测提供了一种新方法。

Herein, based on the fluorescence detection mechanism, acenaphthene, fluorene, and naphthalene were detected in polycyclic aromatic hydrocarbons by the parallel factor combined with the support vector machine (SVM) algorithm. The fluorescence spectral data were preprocessed and used as the training set, which was fed into the particle-swarm-optimized SVM algorithm to establish the classification model. The number of components was determined using the methods of core consistency analysis, residual square sum analysis, and iterative frequency analysis, and the optimal component number thus obtained was used to perform parallel factor decomposition. The obtained transmit load matrix was used as the test set and fed into the SVM classification model. The classification accuracy rate was 100%. The recovery rates of 100.45%±6.25%, 100. 10%± 6. 39%, and 95.07%± 7. 46 % were achieved for acenaphthene, fluorene, and naphthalene, respectively. The proposed algorithm avoids time complexity caused by human operation and errors caused by subjective factors. Thus, it can be applied for fluorescence detection of polycyclic aromatic hydrocarbons.