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.

中文翻译：

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使用扩展双线性模型的双分量二阶多元校准中的初始化效果

增强数据矩阵的双线性分解通常因旋转歧义现象而变得复杂。如果后者很重要，则恢复的配置文件的定量和定性信息可能不太有用。尽管约束可以减少可行区域的范围和旋转模糊的程度，但开始分解的初始参数估计是多元曲线分辨率交替最小二乘法 (MCR-ALS) 研究中的一个重要阶段。在处理旋转模糊持续存在的增强数据矩阵的双线性分解时，问题仍然是是否仍有可能开发成功的校准协议。事实上，文献报告表明已经通过实验开发了各种分析系统，其中存在大量的旋转模糊性，然而，实验结果证实准确的分析物定量是可能的。在这项研究中，我们进一步研究了使用扩展双线性模型进行双分量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有助于实现正确的剖面分辨率，具体取决于它应用的数据方向。最后，为分析化学家提供了一些数据驱动的指南，以确定旋转模糊的潜在程度和初始化策略的正确选择。我们进一步研究了初始化步骤对扩展双线性模型的双分量二阶多元校准的影响。结果表明，基于所谓的最纯变量的选择策略有助于实现正确的剖面分辨率，具体取决于它应用的数据方向。最后，为分析化学家提供了一些数据驱动的指南，以确定旋转模糊的潜在程度和初始化策略的正确选择。我们进一步研究了初始化步骤对扩展双线性模型的双分量二阶多元校准的影响。结果表明，基于所谓的最纯变量的选择策略有助于实现正确的剖面分辨率，具体取决于它应用的数据方向。最后，为分析化学家提供了一些数据驱动的指南，以确定旋转模糊的潜在程度和初始化策略的正确选择。