The application of the recently developed area correlation constraint in Multivariate CurveResolution-Alternating Least Squares (MCR-ALS) for the quantitative determination of analyte mixtures is shown. The feasibility of the proposed constraint is tested firstly for the calibration and quantitation of PAHs mixtures in their synthetic mixtures (validation samples) and in river water samples dissolved organic matter (DOM) using EEM fluorescent three-way data. In this case, MCR-ALS results obtained with the proposed area correlation constraint are comparable with the results obtained with methods based on the fulfillment of the trilinear model, like PARAFAC and MCR-ALS with the trilinearity constraint. Secondly, the possibility of applying this new area correlation constraint is extended to the analytical determination of lipid mixtures in synthetic and cell culture samples by LC-MS, where the trilinear model does not hold. The applicability of the proposed area correlation constraint is assessed, and it is proposed as a general tool for the quantitative determination of unknown mixtures of analytes in complex natural samples with severe profile overlapping and unknown composition, whatever the data structure is.

中文翻译：

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面积相关约束在复杂混合物样品MCR-ALS定量分析中的应用

显示了最近开发的面积相关约束在多元曲线分辨率交替最小二乘法 (MCR-ALS) 中用于分析物混合物的定量测定的应用。首先使用 EEM 荧光三元数据对合成混合物（验证样品）和河水样品溶解有机物 (DOM) 中的多环芳烃混合物进行校准和定量，测试该约束的可行性。在这种情况下，使用提出的面积相关约束获得的 MCR-ALS 结果与使用基于满足三线性模型的方法获得的结果相当，如具有三线性约束的 PARAFAC 和 MCR-ALS。第二，应用这种新的面积相关性约束的可能性扩展到通过 LC-MS 对合成和细胞培养样品中的脂质混合物进行分析测定，其中三线性模型不成立。评估了所提出的面积相关性约束的适用性，并将其作为定量测定具有严重剖面重叠和未知组成的复杂天然样品中分析物的未知混合物的通用工具，无论数据结构是什么。