LAUSR

Anisotropy resolved multidimensional emission spectroscopy (ARMES) provides valuable insights into multi-fluorophore systems like proteins that have complex overlapping emission bands. The method combines multidimensional fluorescence, anisotropy, and chemometrics to facilitate the differentiation of fluorophores with very similar emission properties. Here, we address the critical issue of standardizing the chemometric methods required to accurately extract spectral and anisotropy information from fluorophore mixtures using two standard sample sets: perylene in glycerol, and a mixture of Erythrosin B and Phloxine B with overlapping emission but different anisotropies. We show for the first time how to accurately model component anisotropy using Multivariate Curve Resolution (MCR) from data collected using total synchronous fluorescence scan (TSFS) and Excitation Emission Matrix (EEM) measurement methods. These datasets were selected to avoid the presence of inner filter effects (IFE) or Förster resonance energy transfer (FRET) that would depolarize fluorescence emission or reduce data tri-linearity. This allowed the non-trilinear TSFS data to yield accurate component anisotropy data once modelled using the correct data augmentation strategy, however, the EEM data proved to be more accurate once optimal constraints (non-negativity and correspondence among species) were employed. For perylene (S2) and Phloxine B which both have very weak anisotropy (<0.06), while the spectral recovery was excellent, the modelled anisotropy values were reasonably accurate (±20% of the real value) because of large relative noise contributions. However, for perylene (S1) and Erythrosin B which have large (>0.2) anisotropies, bilinear and trilinear EEM models built using a total tri-linearity constraint, yielded solutions without any rotational ambiguities and very accurate (±4% of real value) anisotropy values. These sample systems thus provide simple and robust test systems for validating the spectral measurement and chemometric data analysis elements of ARMES.