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首页> 外文期刊>Chemometrics and Intelligent Laboratory Systems >Exploiting third-order advantage using four-way calibration method for direct quantitative analysis of active ingredients of Schisandra chinensis in DMEM by processing four-way excitation-emission-solvent fluorescence data
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Exploiting third-order advantage using four-way calibration method for direct quantitative analysis of active ingredients of Schisandra chinensis in DMEM by processing four-way excitation-emission-solvent fluorescence data

机译：利用四向校准方法开发三阶优势，通过处理四向激发-发射-溶剂荧光数据直接定量分析五味子中五味子的有效成分

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In this paper, a new strategy of introducing an extra solvent mode in the three-way EEMs data to construct a four-way EX-EM-solvent-samples data set was used in order to investigate the 'third-order advantage' by comparing the performances of two four-way calibration algorithms with those of two three-way calibration algorithms. The results of the self-weighted alternating normalized residue fitting (SWANRF) and the parallel factor analysis (PARAFAC) algorithms were both disappointing in this work due to serious collinearity and high background interference. In contrast, the satisfactory results (average recovery, 100.0-107.4% and standard deviation, 1.0-3.0%) were obtained by the third-order calibration methods based on both four-way self-weighted alternating normalized residue fitting (four-way SWANRF) and four-way parallel factor analysis (four-way PARAFAC) algorithms. In addition, for prediction of Dulbecco's modified eagle medium (DMEM) samples, the root-mean-square error of prediction (RMSEP) values for schizandrol B obtained from two three-way calibration algorithms were 60.91 and 35.46 mu g mL(-1) with 80% ethanol in the aqueous solvent, which were obviously much larger than these (0.0832 and 0.0825 mu g mL(-1)) obtained using two algorithms of the four-way calibration. The results demonstrated that third-order calibration can extract more inherent information from the data, and can easily solve the problems of serious collinearity and high background interference. In short, among the advantages of this method over the existing methods, low cost, non-toxic and non-destructive analysis can be cited. (C) 2016 Elsevier B.V. All rights reserved.
机译：本文使用一种在三效EEM数据中引入额外溶剂模式以构建四效EX-EM-溶剂样本数据集的新策略，以通过比较来研究“三阶优势”两种四向校准算法的性能与两种三向校准算法的性能。由于严重的共线性和高背景干扰，自加权交替归一化残差拟合（SWANRF）和并行因子分析（PARAFAC）算法的结果都令人失望。相比之下，基于四向自加权交替归一化残差拟合（四向SWANRF）的三阶校正方法，获得了令人满意的结果（平均回收率100.0-107.4％和标准偏差1.0-3.0％）。 ）和四向并行因子分析（四向PARAFAC）算法。此外，为了预测Dulbecco的改良老鹰培养基（DMEM）样品，从两种三效校准算法获得的五味子酚B的预测均方根误差（RMSEP）值分别为60.91和35.46μg mL（-1）在含水溶剂中含有80％的乙醇时，明显比使用两种四向校正算法获得的乙醇（0.0832和0.0825μg mL（-1））大得多。结果表明，三阶校正可以从数据中提取更多固有信息，并且可以轻松解决严重的共线性和高背景干扰的问题。简而言之，在该方法相对于现有方法的优点中，可以引用低成本，无毒且无损的分析。 （C）2016 Elsevier B.V.保留所有权利。

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