Seeding multivariate algorithms for spectral analysis, a data augmentation approach to enhance analytical performance

Abstract

Seeding spectral datasets by augmenting the data matrix with either the full spectrum or selected spectral features in order to bias multivariate analysis towards the solution of interest is explored. It is demonstrated that such seeding can have a profound effect on the endpoint of the analysis. Using Raman spectroscopic data of human lung adenocarcinoma cells (A549) in vitro, systematic perturbations to the spectra are introduced to simulate dose dependent exposure to a drug (cisplatin), and/or cellular response, representing reduced viability. Taking Principal Components Analysis (PCA) as the first example, seeding with the known spectral profiles of the drug exposure is demonstrated to greatly increase the ability of the algorithm to differentiate two distinct data subsets, representing control and exposed. The improved differentiation is quantified by further Linear Discriminant Analysis of the PCA data. Other examples of where seeding may be applied include, simulated datasets consisting of simultaneous changes in the spectral markers of exposure dose and cellular response, which are used for Multivariate Curve Resolution – Alternating Least Squares analysis (MCR-ALS). In the example presented, adding pure components to the dataset improves the ability of the algorithm to both model the systematic variation of concentration dependent data and extract the component spectra more accurately than the unseeded dataset. The seeded approach thus provides improved performance for differential analysis of datasets, as well as spectral unmixing analyses, to monitor, for example, the kinetic evolution of a reaction mixture, or metabolic pathway.