Wang Guoqing, Ding Qingzhu, Sun Yu'an, He Linghao, Sun Xiaoli
School of Materials and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan 450002, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2008 Aug;70(3):571-6. doi: 10.1016/j.saa.2007.07.051. Epub 2007 Aug 6.
Kernel independent component analysis (KICA), a kind of independent component analysis (ICA) algorithms based on kernel, was preliminarily investigated for blind source separation (BSS) of source spectra profiles from troches. The robustness of different ICA algorithms (KICA, FastICA and Infomax) was first checked by using them in the retrieval of source infrared (IR), ultraviolet (UV) and mass spectra (MS) from synthetic mixtures. It was found that KICA is the most robust method for retrieval of source spectra profiles. KICA algorithm is subsequently adopted in the analysis of diffuse reflection IR of acetylspiramycin (ASPM) troches. It is observed that KICA is able to isolate the theoretically predicted spectral features corresponding to the ASPM active components, excipients and other minor components as different independent (spectral) component. A troche can be authenticated and semi-quantified using the estimated ICs. KICA is an useful method for estimation of source spectral features of molecules with different geometry and stoichiometry, while features belonging to very similar molecules remain grouped.
核独立成分分析（KICA）是一种基于核的独立成分分析（ICA）算法，已被初步用于从片剂中盲源分离（BSS）源光谱轮廓。首先通过在从合成混合物中检索源红外（IR）、紫外（UV）和质谱（MS）时使用不同的ICA算法（KICA、FastICA和Infomax）来检查它们的稳健性。发现KICA是检索源光谱轮廓最稳健的方法。随后将KICA算法用于乙酰螺旋霉素（ASPM）片剂的漫反射红外分析。观察到KICA能够将与ASPM活性成分、辅料和其他次要成分相对应的理论预测光谱特征分离为不同的独立（光谱）成分。可以使用估计的独立成分对片剂进行鉴定和半定量。KICA是一种用于估计具有不同几何形状和化学计量的分子的源光谱特征的有用方法，而属于非常相似分子的特征仍会分组在一起。
