State Key Laboratory of Food Science and Technology, Nanchang University, China.
Appl Spectrosc. 2012 Jul;66(7):810-9. doi: 10.1366/12-06595. Epub 2012 Jun 15.
An analytical method for the classification of complex real-world samples was researched and developed with the use of excitation-emission fluorescence matrix (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and RC-C samples. The spectra (range: λ(ex) = 215∼395 nm and λ(em) = 290∼560 nm), arranged in two- and three-way data matrix formats, were processed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) to produce two-dimensional component-by-component plots for qualitative data classification. The RCD-A and RC-A object groups were clearly discriminated, but the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC analysis produced somewhat better discrimination, and loadings plots revealed the presence of the marker compound Protopine-a strongly fluorescing substance-as well as at least two other unidentified fluorescent components. Classification performance of the common K-nearest neighbors (KNN) and linear discrimination analysis (LDA) methods was relatively poor when compared with that of the back propagation- and radial basis function-artificial neural networks (BP-ANN and RBF-ANN) models on the basis of two- and three-way formatted data. The best results were obtained with the three-way fingerprints and the RBF-ANN model. Subsequently, the quality of the commercial samples (RCD-C and RC-C) was classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which provides three-way measured data, is potentially a powerful analytical technique for the analysis of complex real-world substances provided the classification is performed by the RBF-ANN or similar ANN methods.
研究并开发了一种利用激发-发射荧光矩阵（EEFM）光谱法对复杂实际样品进行分类的分析方法，以药用植物延胡索（RCD）和延胡索（RC）为例样。数据集来自各种真实的 RCD-A 和 RC-A、掺假 AD 以及商业 RCD-C 和 RC-C 样品。对光谱（范围：λ（ex）= 215∼395nm 和 λ（em）= 290∼560nm）进行处理，排列成二维和三维数据矩阵格式，使用主成分分析（PCA）和并行因子分析（PARAFAC）进行处理，以生成二维成分对成分图进行定性数据分类。RCD-A 和 RC-A 对象组得到了清晰的区分，但 AD 以及 RCD-C 和 RC-C 样品的分离效果较差。PARAFAC 分析产生了稍好的区分效果，并且加载图显示存在强烈荧光物质普罗托品-a 以及至少两种其他未识别的荧光成分。与基于二维和三维格式数据的常用 K-最近邻（KNN）和线性判别分析（LDA）方法相比，反向传播和径向基函数-人工神经网络（BP-ANN 和 RBF-ANN）模型的分类性能较差。基于三维指纹和 RBF-ANN 模型获得了最佳结果。随后，根据最佳优化的 RBF-ANN 模型对商业样品（RCD-C 和 RC-C）的质量进行了分类。因此，EEFM 光谱学提供了三向测量数据，只要通过 RBF-ANN 或类似的 ANN 方法进行分类，它就是一种用于分析复杂实际物质的强大分析技术。
