Department of Chemistry and ‡Neuroscience Center and Neurobiology Curriculum, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina 27599-3290, United States.
Anal Chem. 2017 Oct 3;89(19):10547-10555. doi: 10.1021/acs.analchem.7b02771. Epub 2017 Sep 13.
The use of multivariate analysis techniques, such as principal component analysis-inverse least-squares (PCA-ILS), has become standard for signal isolation from in vivo fast-scan cyclic voltammetric (FSCV) data due to its superior noise removal and interferent-detection capabilities. However, the requirement of collecting separate training data for PCA-ILS model construction increases experimental complexity and, as such, has been the source of recent controversy. Here, we explore an alternative method, multivariate curve resolution-alternating least-squares (MCR-ALS), to circumvent this issue while retaining the advantages of multivariate analysis. As compared to PCA-ILS, which relies on explicit user definition of component number and profiles, MCR-ALS relies on the unique temporal signatures of individual chemical components for analyte-profile determination. However, due to increased model freedom, proper deployment of MCR-ALS requires careful consideration of the model parameters and the imposition of constraints on possible model solutions. As such, approaches to achieve meaningful MCR-ALS models are characterized. It is shown, through use of previously reported techniques, that MCR-ALS can produce similar results to PCA-ILS and may serve as a useful supplement or replacement to PCA-ILS for signal isolation from FSCV data.
由于多元分析技术（如主成分分析-逆最小二乘法（PCA-ILS））具有出色的噪声去除和干扰检测能力，因此已成为从体内快速扫描循环伏安法（FSCV）数据中分离信号的标准方法。然而，PCA-ILS 模型构建需要收集单独的训练数据，这增加了实验的复杂性，因此成为了最近争议的根源。在这里，我们探索了一种替代方法，即多变量曲线分辨-交替最小二乘法（MCR-ALS），以避免这个问题，同时保留多元分析的优势。与 PCA-ILS 不同，后者依赖于用户对组件数量和轮廓的明确定义，MCR-ALS 依赖于单个化学物质的独特时间特征来确定分析物轮廓。然而，由于模型自由度的增加，正确部署 MCR-ALS 需要仔细考虑模型参数，并对可能的模型解决方案施加约束。因此，我们对实现有意义的 MCR-ALS 模型的方法进行了描述。通过使用先前报道的技术，我们表明 MCR-ALS 可以产生与 PCA-ILS 相似的结果，并且可以作为从 FSCV 数据中分离信号的 PCA-ILS 的有用补充或替代方法。
