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.

中文翻译：

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用于快速扫描循环伏安数据中信号隔离的多元曲线分辨率

使用多元分析技术，例如主成分分析-最小二乘法（PCA–ILS），已成为从体内快速扫描循环伏安（FSCV）数据中分离信号的标准方法，因为它具有出色的噪声去除和干扰源识别功能。检测能力。但是，为PCA–ILS模型构建收集单独的训练数据的要求增加了实验的复杂性，因此，这已成为近期争议的源头。在这里，我们探索了另一种方法，即多元曲线分辨率-交替最小二乘（MCR–ALS），从而在保留多元分析优势的同时，规避了此问题。与PCA–ILS相比，后者依赖于用户对组件编号和配置文件的明确定义，MCR–ALS依靠各个化学成分的独特时间特征来确定分析物的轮廓。但是，由于增加了模型自由度，MCR–ALS的正确部署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，表征了实现有意义的MCR-ALS模型的方法。通过使用以前报道的技术，可以看出，MCR–ALS可以产生与PCA–ILS相似的结果，并且可以作为PCA–ILS的有用补充或替代，以从FSCV数据中隔离信号。描述了实现有意义的MCR-ALS模型的方法。通过使用以前报道的技术，可以看出，MCR–ALS可以产生与PCA–ILS相似的结果，并且可以作为PCA–ILS的有用补充或替代，以从FSCV数据中隔离信号。描述了实现有意义的MCR-ALS模型的方法。通过使用先前报道的技术，可以看出，MCR–ALS可以产生与PCA–ILS相似的结果，并且可以作为PCA–ILS的有用补充或替代，以从FSCV数据中隔离信号。