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.

中文翻译：

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使用多元曲线分辨率 (MCR) 从荧光团混合物中准确恢复各向异性

各向异性分辨多维发射光谱 (ARMES) 提供了对多荧光团系统（如具有复杂重叠发射带的蛋白质）的宝贵见解。该方法结合了多维荧光、各向异性和化学计量学，以促进具有非常相似的发射特性的荧光团的区分。在这里，我们解决了使用两个标准样品组从荧光团混合物中准确提取光谱和各向异性信息所需的化学计量学方法标准化的关键问题：甘油中的苝，以及具有重叠发射但各向异性不同的赤藓红 B 和 Phloxine B 的混合物。我们首次展示了如何使用多变量曲线分辨率 (MCR) 从使用全同步荧光扫描 (TSFS) 和激发发射矩阵 (EEM) 测量方法收集的数据中准确地模拟组件各向异性。选择这些数据集是为了避免内部过滤效应 (IFE) 或 Förster 共振能量转移 (FRET) 的存在，这些效应会使荧光发射去极化或降低数据三线性。一旦使用正确的数据增强策略进行建模，这允许非三线性 TSFS 数据产生准确的组件各向异性数据，但是，一旦采用最佳约束（非负性和物种之间的对应关系），EEM 数据被证明更准确。对于都具有非常弱的各向异性 (<0.06) 的苝 (S2) 和 Phloxine B，虽然光谱恢复非常好，由于较大的相对噪声贡献，模拟的各向异性值相当准确（实际值的±20%）。然而，对于具有大 (>0.2) 各向异性的苝 (S1) 和赤藓红 B，使用总三线性约束构建的双线性和三线性 EEM 模型产生的解没有任何旋转模糊性并且非常准确（实际值的 ±4%）各向异性值。因此，这些样品系统为验证 ARMES 的光谱测量和化学计量数据分析元素提供了简单而强大的测试系统。产生了没有任何旋转模糊性和非常准确（实际值的±4%）各向异性值的解决方案。因此，这些样品系统为验证 ARMES 的光谱测量和化学计量数据分析元素提供了简单而强大的测试系统。产生了没有任何旋转模糊性和非常准确（实际值的±4%）各向异性值的解决方案。因此，这些样品系统为验证 ARMES 的光谱测量和化学计量数据分析元素提供了简单而强大的测试系统。