Abstract
In this paper, the effectiveness of methods for determining the number of significant components is evaluated in four simulated and four experimental liquid chromatography nuclear magnetic resonance (LC–NMR) spectrometric datasets. The following methods are tested: eigenvalues, log eigenvalues and eigenvalue ratios from principal component analysis (PCA) of the overall data; error indicator functions [residual sum of squares (rssq), residual standard deviation (RSD), ratio of successive residual standard deviations (RSDRatio), root mean square error (RMS), imbedded error (IE), factor indicator functions, scree test and Exner function], together with their ratio of derivatives (ROD); F-test (Malinowski, Faber–Kowalski and modified FK); cross-validation; morphological score (MS); purity-based approaches including orthogonal projection approach (OPA) and SIMPLISMA; correlation and derivative plots; evolving PCA (EPCA) and evolving PC innovation analysis (EPCIA); subspace comparison. Five sets of methods are selected as best, including several error indicator functions, their ratio of derivatives, the residual standard deviation ratio, orthogonal projection approach (OPA) concentration profiles and evolving PCA using an expanding window (EW). Omitting the dataset with the highest noise level, RSS, Malinowski's F-test, concentration profiles using SIMPLISMA and subspace comparison with PCA score also perform well.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
