Department of Chemistry, East Carolina University, Greenville, NC, 27858, United States; Department of Chemistry, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran.
Department of Chemistry, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran.
Anal Chim Acta. 2020 Apr 8;1105:64-73. doi: 10.1016/j.aca.2020.01.022. Epub 2020 Jan 13.
Multivariate curve resolution (MCR) is a powerful tool in chemometrics that has been involved in the solution of many analytical problems. The introduction of partial or incomplete knowledge of reference values as known-value constraints in an MCR model can considerably reduce the extent of rotational ambiguity for all components. Known-value constraints can provide enough information for MCR methods to perform both the identification and quantitative analysis of first-order data sets. In practice, in addition to noise and non-ideal behavior, limitations in the reference methods or procedures cause deviation in measured known values. It is shown that deviation in the measured known values, when used as known-value constraints, may result in considerable quantification errors in MCR results and can challenge identification analysis. This contribution investigates the importance and effect of soft known-value constraints on the accuracy of MCR solutions. The influence of noise levels, the amount of deviation of known values from true values, and the interaction of these two factors were evaluated with simulated data. An illustration using soft known-value constraints is given for a batch reaction experiment.
多元曲线分辨（MCR）是化学计量学中一种强大的工具，已经涉及到许多分析问题的解决。在 MCR 模型中引入参考值的部分或不完整知识作为已知值约束，可以大大减少所有组分的旋转不确定性程度。已知值约束可以为 MCR 方法提供足够的信息，以执行一阶数据集的识别和定量分析。在实践中，除了噪声和非理想行为之外，参考方法或程序的限制会导致测量的已知值出现偏差。结果表明，当用作已知值约束时，测量的已知值的偏差可能会导致 MCR 结果中的相当大的定量误差，并对识别分析构成挑战。本研究探讨了软已知值约束对 MCR 解准确性的重要性和影响。使用模拟数据评估了噪声水平、已知值与真实值的偏差量以及这两个因素的相互作用的影响。使用软已知值约束对批处理反应实验进行了说明。
