Chiappini Fabricio A, Pinto Licarion, Alcaraz Mirta R, Omidikia Nematollah, Goicoechea Hector C, Olivieri Alejandro C
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.
Laboratório de tecnologia analítica de processos, Departamento de química analítica, Instituto de Química, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil.
Anal Chim Acta. 2024 Nov 1;1328:343159. doi: 10.1016/j.aca.2024.343159. Epub 2024 Aug 26.
Recent interest has been focused on the application of multivariate curve resolution-alternating least-squares (MCR-ALS) to systems involving the measurement of first-order and non-bilinear second-order data. The latter pose important challenges to bilinear decomposition models, due to the phenomenon of rotational ambiguity in the solutions, even under the application of the full set of chemical constraints that is usually employed in MCR-ALS calibration.
After the analysis of several simulated and experimental datasets, important conclusions regarding the role of the selectivity patterns in the constituent spectra have been drawn concerning the achievement of the second-order advantage. Theoretical considerations based on the calculation of the areas of feasible solutions helped to support the observations regarding the predictive ability of MCR- ALS in the various datasets.
The understanding of the impact of rotational ambiguity in obtaining the second-order advantage with both first-order and non-bilinear second-order data is of paramount importance in the future development of analytical protocols of complex samples.
最近，人们的兴趣集中在将多元曲线分辨交替最小二乘法（MCR-ALS）应用于涉及一阶和非双线性二阶数据测量的系统。由于解中存在旋转模糊现象，即使在MCR-ALS校准中通常采用的全套化学约束条件下，后者对双线性分解模型也构成了重大挑战。
在分析了几个模拟和实验数据集之后，就实现二阶优势而言，得出了关于选择性模式在成分光谱中的作用的重要结论。基于可行解面积计算的理论考虑有助于支持关于MCR-ALS在各种数据集中预测能力的观察结果。
了解旋转模糊对利用一阶和非双线性二阶数据获得二阶优势的影响，对于复杂样品分析方案的未来发展至关重要。
