Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe S3000ZAA, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290, CABA C1425FQB, Argentina.
Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química de Rosario (IQUIR-CONICET), Suipacha 531, Rosario S2002LRK, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290, CABA C1425FQB, Argentina.
Anal Chim Acta. 2021 May 29;1161:338465. doi: 10.1016/j.aca.2021.338465. Epub 2021 Mar 28.
The possibility of building an interference-free calibration with first-order instrumental data with multivariate curve resolution-alternating least-squares (MCR-ALS) has been a recent topic of interest. When the protocols were successful, MCR-ALS proved to be suitable for the extraction of chemically meaningful information from first-order calibration datasets, even in the presence of unexpected species, i.e., constituents present in the test samples but absent in the calibration set. This may represent an interesting advantage over classical first-order models, e.g. partial least-squares regression (PLS). However, the predictive capacity of MCR-ALS models can be severely affected by rotational ambiguity (RA), which is usually present in first-order datasets when interferents occur, and has not been always characterized in the published analytical protocols. The aim of this report is to discuss important issues regarding MCR-ALS modelling of first-order data for a calibration scenario with a single analyte and one interferent through simulated and experimental data. Specifically, the question of when and why MCR-ALS allows one to build interference-free calibration models with first-order data is studied in terms of signal overlapping, extent of RA, and especially the role of ALS initialization procedures in prediction performance. The aim is to alert analytical chemists that interference-free MCR-ALS with first-order data may not always be successful.
建立具有一阶仪器数据的无干扰校准的可能性，采用多元曲线分辨-交替最小二乘法（MCR-ALS），是最近的一个研究课题。当协议成功时，MCR-ALS 被证明适用于从一阶校准数据集提取具有化学意义的信息，即使存在意外的物种，即在测试样品中存在但在校准集中不存在的成分。这可能代表了与经典一阶模型（例如偏最小二乘回归（PLS））相比的一个有趣的优势。然而，MCR-ALS 模型的预测能力可能会受到旋转不确定性（RA）的严重影响，当干扰物存在时，通常会在一阶数据集中出现 RA，并且在已发表的分析协议中并不总是对其进行描述。本报告的目的是通过模拟和实验数据，讨论在具有单个分析物和一个干扰物的校准情况下，对一阶数据进行 MCR-ALS 建模的重要问题。具体而言，从信号重叠、RA 的程度以及特别是 ALS 初始化程序在预测性能中的作用方面，研究了当且仅当存在信号重叠时，MCR-ALS 如何允许用一阶数据构建无干扰的校准模型的问题。目的是提醒分析化学家，用一阶数据进行无干扰的 MCR-ALS 并不总是成功的。
