Department of Chemistry, Capital Normal University, Beijing 100048, China.
Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08043 Barcelona, Spain.
Molecules. 2022 Apr 5;27(7):2338. doi: 10.3390/molecules27072338.
Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) can analyze three-way data under the assumption of a trilinear model using the trilinearity constraint. However, the rigid application of this constraint can produce unrealistic solutions in practice due to the inadequacy of the analyzed data to the characteristics and requirements of the trilinear model. Different methods for the relaxation of the trilinear model data requirements have been proposed, like in the PARAFAC2 and in the direct non-trilinear decomposition (DNTD) methods. In this work, the trilinearity constraint of MCR-ALS is adapted to different data scenarios where the profiles of all or some of the components of the system are shifted (not equally synchronized) or even change their shape among different slices in one of their data modes. This adaptation is especially useful in gas and liquid chromatography (GC and LC) and in Flow Injection Analysis (FIA) with multivariate spectroscopic detection. In a first data example, a synthetic LC-DAD dataset is built to investigate the possibilities of the proposed method to handle systematic changes (shifts) in the retention times of the elution profiles and the results are compared with those obtained using alternative methods like ATLD, PARAFAC, PARAFAC2 and DNTD. In a second data example, multiple wine samples were simultaneously analyzed by GC-MS where elution profiles presented large deviations (shifts) in their peak retention times, although they still preserve the same peak shape. Different modelling scenarios are tested and the results are also compared. Finally, in the third example, sample mixtures of acid compounds were analyzed by FIA under a pH gradient and monitored by UV spectroscopy and also examined by different chemometric methods using a different number of components. In this case, however, the departure of the trilinear model comes from the acid base speciation of the system depending on the pH more than from the shifting of the FIA diffusion profiles.
多元曲线分辨交替最小二乘法（MCR-ALS）可以在三线性模型的假设下，通过三线性约束，分析三向数据。然而，由于分析数据与三线性模型的特征和要求不匹配，该约束的严格应用在实际中可能会产生不真实的解。已经提出了多种放松三线性模型数据要求的方法，例如 PARAFAC2 和直接非三线性分解（DNTD）方法。在这项工作中，MCR-ALS 的三线性约束被应用于不同的数据场景，其中系统的所有或部分组分的轮廓被移动（不同步），甚至在其数据模式之一的不同切片中改变形状。这种适应在气相和液相色谱（GC 和 LC）以及多变量光谱检测中的流动注射分析（FIA）中特别有用。在第一个数据示例中，构建了一个合成的 LC-DAD 数据集，以研究所提出的方法处理洗脱轮廓保留时间系统变化（移动）的可能性，并将结果与替代方法（如 ATLD、PARAFAC、PARAFAC2 和 DNTD）的结果进行比较。在第二个数据示例中，同时通过 GC-MS 分析了多个葡萄酒样本，尽管其峰保留时间存在较大偏差（移动），但洗脱轮廓仍保留相同的峰形。测试了不同的建模场景，并对结果进行了比较。最后，在第三个示例中，在 pH 梯度下通过 FIA 分析了酸化合物的样品混合物，并通过 UV 光谱进行监测，还使用不同数量的组分通过不同的化学计量方法进行了检查。然而，在这种情况下，三线性模型的偏离来自于系统的酸碱形态，而不是来自于 FIA 扩散轮廓的移动。
