Wang Tong, Liu Qian, Long Wan-Jun, Chen An-Qi, Wu Hai-Long, Yu Ru-Qin
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China.
Biomedical Research Center, Hunan University of Medicine, Huaihua 418000, People's Republic of China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Feb 5;246:118988. doi: 10.1016/j.saa.2020.118988. Epub 2020 Sep 25.
In this paper, a simple, rapid, low-cost and potential method was established for the simultaneous quantitative analysis of dabigatran etexilate (DABE) and dabigatran (DAB) in spiked biological fluids. It combined excitation-emission matrix fluorescence (EEMF) with different second-order calibration methods, including the self-weighted alternating normalized residue fitting (SWANRF) algorithm based on trilinear decomposition model, the multivariate curve resolution - alternating least-squares (MCR-ALS) based on bilinear decomposition model and the unfolded partial least-square coupled with residual bilinearization (U-PLS/RBL) based on latent variables model. The proposed method showed "second-order advantage", that is, satisfactory quantitative results were successfully obtained even in the presence of unknown interferences and serious spectral overlap. The recoveries of DABE and DAB in spiked biological fluids were 91.7%-101.7% for SWANRF, 95.9%-117.8% for MCR-ALS, 83.0%-109.6% for U-PLS/RBL, respectively. Figures of merit and other statistical parameters were also calculated to assess the performance of the proposed method. Moreover, the modeling procedures and characteristics of three different models in EEMF analysis were discussed and compared.
本文建立了一种简单、快速、低成本且具有潜力的方法，用于同时定量分析加标生物流体中的达比加群酯（DABE）和达比加群（DAB）。该方法将激发发射矩阵荧光（EEMF）与不同的二阶校正方法相结合，包括基于三线性分解模型的自加权交替归一化残差拟合（SWANRF）算法、基于双线性分解模型的多元曲线分辨交替最小二乘法（MCR-ALS）以及基于潜变量模型的展开偏最小二乘与残差双线性化（U-PLS/RBL）。所提出的方法具有“二阶优势”，即即使在存在未知干扰和严重光谱重叠的情况下，也能成功获得令人满意的定量结果。加标生物流体中DABE和DAB的回收率分别为：SWANRF为91.7%-101.7%，MCR-ALS为95.9%-117.8%，U-PLS/RBL为83.0%-109.6%。还计算了品质因数和其他统计参数以评估所提出方法的性能。此外，对EEMF分析中三种不同模型的建模过程和特点进行了讨论和比较。
