Wang Guoqing, Sun Yu-an, Ding Qingzhu, Dong Chunhong, Fu Dexue, Li Cunhong
Department of Applied Chemistry, Zhengzhou University of Light Industry, Zhengzhou, Henan 450002, China.
Anal Chim Acta. 2007 Jun 26;594(1):101-6. doi: 10.1016/j.aca.2007.05.004. Epub 2007 May 6.
A method that use kernel independent component analysis (KICA) and support vector regression (SVR) was proposed for estimation of source ultraviolet (UV) spectra profiles and simultaneous determination of polycomponents in mixtures. In KICA-SVR procedure, the UV source spectra profiles were estimated using KICA, then the mixing matrix of the components were calculated using the estimated sources, and the calibration model was build using SVR based on the calculated mixing matrix. A simulated UV dataset of three-component mixtures was used to test the ability of KICA for estimating source spectra profiles from spectra data of mixtures. It was found that KICA has the potential power to estimate pure UV spectra profiles, and correlation coefficient of estimated sources correspond to the real adopted ones are better compared with that by FastICA and Infomax ICA. An UV dataset of polycomponent vitamin B was processed using the proposed KICA-SVR method. The results show that the estimated source spectra profiles are correlative with the real UV spectra of the components and chemically interpretable, and accurate results were obtained.
提出了一种使用核独立成分分析（KICA）和支持向量回归（SVR）的方法，用于估计源紫外（UV）光谱轮廓并同时测定混合物中的多组分。在KICA-SVR过程中，使用KICA估计紫外源光谱轮廓，然后使用估计的源计算各组分的混合矩阵，并基于计算出的混合矩阵使用SVR建立校准模型。使用一个三组分混合物的模拟紫外数据集来测试KICA从混合物光谱数据估计源光谱轮廓的能力。发现KICA具有估计纯紫外光谱轮廓的潜在能力，与FastICA和Infomax ICA相比，估计源与实际采用源的相关系数更好。使用所提出的KICA-SVR方法处理了一个多组分维生素B的紫外数据集。结果表明，估计的源光谱轮廓与各组分的实际紫外光谱相关且具有化学可解释性，并获得了准确的结果。
