Modern analytical instruments provide measurement data arrays with full of hidden and redundant information. Multivariate curve resolution (MCR) techniques decompose data set to physic‐chemically meaningful abstract profiles. On the other hand, for such data matrices, Borgen‐Rajkó self‐modeling curve resolution (SMCR) techniques reveal all possible solutions analytically under the minimal assumption. Although Lawton‐Sylvestre (LS) and Borgen methods have been proposed for the non‐negative curve resolution of two‐component and three‐component systems, there is still a great deal of interest to include further restrictions on the Borgen‐Rajkó SMCR. As modern hyphenated analytical instruments produce multiway (eg, three‐way) arrays, multiway analysis (eg, trilinear decomposition) was received much more popularity by chemists.

中文翻译：

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使用 Borgen-Rajkó 图的三线性自建模曲线分辨率

现代分析仪器提供了充满隐藏和冗余信息的测量数据阵列。多元曲线分辨率 (MCR) 技术将数据集分解为具有物理化学意义的抽象轮廓。另一方面，对于此类数据矩阵，Borgen-Rajkó 自建模曲线分辨率 (SMCR) 技术在最小假设下分析地揭示了所有可能的解决方案。尽管已经提出 Lawton-Sylvestre (LS) 和 Borgen 方法用于双组分和三组分系统的非负曲线分辨率，但仍然有很多兴趣包括对 Borgen-Rajkó SMCR 的进一步限制。由于现代连接分析仪器产生多路（例如，三路）阵列，多路分析（例如，三线分解）更受化学家欢迎。