Combining hard- and soft-modelling to solve kinetic problems
Combining hard- and soft-modelling to solve kinetic problems
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DOI：
10.1016/s0169-7439(00)00112-x
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发表时间：
2000-12-29
影响因子：
3.9
通讯作者：
Tauler, R
中科院分区：
文献类型：
--
作者：
de Juan, A;Maeder, M;Tauler, R
提出了一种混合硬建模和软建模方法的新方法来分析光谱监测的动力学数据。在多元曲线分辨率-交替最小二乘法（MCR-ALS）的基础上，采用典型的软建模约束来获得原始测量中存在的所有吸收物质的纯浓度分布和光谱，并引入新的硬约束来强制部分或全部浓度分布满足动力学模型，并在优化过程的每个迭代周期中对其进行细化。这种对MCR-ALS的修改极大地减少了与仅使用软建模约束获得的动力学剖面相关的旋转模糊性。选择性地将部分或全部吸收物质纳入动力学模型，可以成功地处理数据矩阵，其仪器响应不完全是由于动力学过程中涉及的化学成分，这是经典硬建模方法不可能出现的情况。此外，在一个三向数据集中，每个矩阵可能的不同约束允许同时分析具有不同动力学模型和速率常数的动力学运行。因此，在处理动力学数据集时引入基于模型和无模型的特征比应用purr：硬或纯自建模方法产生更令人满意的结果。通过仿真和实际算例验证了这一说法。(C) 2000 Elsevier Science B.V.版权所有
A novel approach mixing the qualities of hard-modelling and soft-modelling methods is proposed to analyse kinetic data monitored spectrometrically. Taking as a basis the Multivariate Curve Resolution-Alternating Least Squares method (MCR-ALS), which obtains the pure concentration profiles and spectra of all absorbing species present in the raw measurements by using typical soft-modelling constraints, a new hard constraint is introduced to force some or all the concentration profiles to fulfill a kinetic model, which is refined at each iterative cycle of the optimisation process.This modification of MCR-ALS drastically decreases the rotational ambiguity associated with the kinetic profiles obtained using exclusively soft-modelling constraints. The optional inclusion of some or all the absorbing species into the kinetic model allows the successful treatment of data matrices whose instrumental response is not exclusively due to the chemical components involved in the kinetic process, an impossible scenario for classical hard-modelling approaches. Moreover, the possible distinct constraint of each of the matrices in a three-way data set allows for the simultaneous analysis of kinetic runs with diverse kinetic models and rate constants. Thus, the introduction of model-based and model-free features in the treatment of kinetic data sets yields more satisfactory results than the application of purr: hard- or pure self-modelling approaches. Simulated and real examples are used to confirm this statement. (C) 2000 Elsevier Science B.V. All rights reserved.