Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química de Rosario (IQUIR-CONICET), Rosario, Argentina.
Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan, P .O. Box 98135-674, Zahedan, Iran.
Anal Chim Acta. 2020 Aug 15;1125:169-176. doi: 10.1016/j.aca.2020.05.060. Epub 2020 May 28.
Bilinear decomposition of an augmented data matrix is usually complicated by the phenomenon of rotational ambiguity. If the latter is significant, quantitative and qualitative information of the recovered profiles may be less useful. Although constraints can reduce the extent of feasible regions and the degree of rotational ambiguity, the estimation of initial parameters to start the decomposition is an important phase in multivariate curve resolution-alternating least-squares (MCR-ALS) studies. Dealing with a bilinear decomposition of an augmented data matrix where rotational ambiguity persists, the question remains whether it is possible to still develop a successful calibration protocol. Indeed, literature reports indicate that various analytical systems have been experimentally developed, in which substantial rotational ambiguity exists, yet the experimental results confirmed that accurate analyte quantitation was possible. In this research, we further investigate on the effect of the initialization step for a two-component second-order multivariate calibration with the extended bilinear model. It is shown that the selection strategy based on the so-called purest variables can be helpful in achieving a correct profile resolution, depending on which data direction it is applied. Finally, some data-driven guidelines for analytical chemists are suggested, to identify the potential degree of rotational ambiguity and the correct choice of the initialization strategy.
增广数据矩阵的双线性分解通常会受到旋转不确定性现象的影响。如果后者很显著，那么恢复的谱图的定量和定性信息可能用处不大。尽管约束条件可以减小可行区域的范围和旋转不确定性的程度，但分解初始参数的估计是多变量曲线分辨-交替最小二乘法（MCR-ALS）研究中的一个重要阶段。在处理存在旋转不确定性的增广数据矩阵的双线性分解时，仍然存在一个问题，即是否有可能开发出成功的校准方案。实际上，文献报道表明，已经实验开发了各种分析系统，其中存在大量的旋转不确定性，但实验结果证实了准确分析物定量的可能性。在这项研究中，我们进一步研究了在扩展的双线性模型中，对于具有两个分量的二阶多元校准的初始化步骤的影响。结果表明，基于所谓的最纯变量的选择策略可以有助于实现正确的谱图分辨率，具体取决于它应用于哪个数据方向。最后，为分析化学家提出了一些数据驱动的准则，以确定潜在的旋转不确定性程度和正确选择初始化策略。
