Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, E08034 Barcelona, Spain; Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Diagonal 647, E08028, Barcelona, Spain.
Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, E08034 Barcelona, Spain.
Talanta. 2022 Sep 1;247:123586. doi: 10.1016/j.talanta.2022.123586. Epub 2022 May 27.
In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.
在这项工作中，比较了三种基于化学计量学的方法，用于二维液相色谱（LC×LC-MS）定量分析，以商业药物混合物中氨基酸的定量为研究案例。虽然这些方法已经用于一维气相或液相色谱，但这项工作的主要新颖之处在于证明它们适用于 LC×LC-MS 数据集。此外，与这里提出的方法不同，通常应用于这种数据分析的峰对齐和建模等步骤在这里不需要。在第一步中，使用感兴趣区域（ROI）策略对 LC×LC-MS 数据集进行光谱压缩。然后，第一种策略是从 ROI 压缩步骤中获得的区域构建校准曲线。或者，可以将 ROI 强度矩阵用作使用多变量曲线分辨率交替最小二乘法（MCR-ALS）方法进行第二分析步骤的输入。MCR-ALS 的主要优点是即使在强烈共洗脱和高信号重叠的情况下，也可以解析混合物中每个分析物的洗脱和光谱轮廓。仅应用非负约束（第二种策略）从峰面积解析的经典 MCR-ALS 基于校准曲线与在 ALS 优化期间施加面积相关约束时获得的结果（第三种策略）进行比较。总而言之，三种方法都可以获得类似的定量结果，但特别是在预测研究中，当在 ALS 优化期间施加面积相关约束时，从 MCR-ALS 获得的峰面积构建校准曲线时，可以获得更准确的定量结果。
