Kumar Keshav
Institute for Wine analysis and Beverage Research, Hochschule Geisenheim University, 65366, Geisenheim, Germany.
J Fluoresc. 2018 Sep;28(5):1075-1092. doi: 10.1007/s10895-018-2271-y. Epub 2018 Aug 20.
The present work successfully shows the application of novel chemometric approach constraint randomised non-negative factor analysis (CRNNFA) for the analyses of the composite multidimensional fluorescence data sets. The CRNNFA involves the initialisation of the spectral variables in a constraint fashion thus ensures that algorithm does not wander with chemically and spectro-chemically irrelevant variables. The CRNNFA approach does not require that there must be pure variables for each fluorophores of the multifluorophoric mixture. One of the biggest advantages of CRNNFA is that it does not involve any convergence criteria thus circumventing the premature convergence of the algorithm. The CRNNFA achieves the termination only when the iteration limit is reached. The CRNNFA analysis s carried out under the non-negativity constraints therefore the mathematically retrieved profiles can easily be compared with those obtained experimentally. In the present work, both trilinear as well as non-trilinear multidimensional data sets are subjected to CRNNFA to validate its applicability. Excitation emission matrix fluorescence (EEMF) spectral profiles of Catechol, Hydroquinone, Indole and Tryptophan mixtures is used as the source of trilinear data sets. Total synchronous fluorescence spectroscopy (TSFS) spectral profiles of Benzo[a] Pyrene, Chrysene and Pyrene mixtures are used as the source of non-trilinear data sets. The CRNNFA approach is found to work equally well with trilinear as well with non-trilinear data sets. Thus, CRNFFA clearly does not have any prerequisite in the data structure. The obtained results clearly shows that CRNNFA algorithm in combination with EEMF and TSFS data sets are potential analytical tool for the analysis of complex-multifluorophoric mixtures.
本研究成功展示了新型化学计量学方法——约束随机非负因子分析（CRNNFA）在复合多维荧光数据集分析中的应用。CRNNFA以约束方式初始化光谱变量，从而确保算法不会在化学和光谱化学无关变量上徘徊。CRNNFA方法并不要求多荧光团混合物的每个荧光团都有纯变量。CRNNFA的最大优点之一是它不涉及任何收敛标准，从而避免了算法的过早收敛。CRNNFA仅在达到迭代极限时才会终止。CRNNFA分析是在非负约束下进行的，因此数学检索到的轮廓可以很容易地与实验获得的轮廓进行比较。在本研究中，三线性和非三线性多维数据集都采用CRNNFA进行分析，以验证其适用性。邻苯二酚、对苯二酚、吲哚和色氨酸混合物的激发发射矩阵荧光（EEMF）光谱轮廓用作三线性数据集的来源。苯并[a]芘、屈和芘混合物的全同步荧光光谱（TSFS）光谱轮廓用作非三线性数据集的来源。发现CRNNFA方法对三线性和非三线性数据集同样有效。因此，CRNFFA显然对数据结构没有任何先决条件。所得结果清楚地表明，CRNNFA算法与EEMF和TSFS数据集相结合是分析复杂多荧光团混合物的潜在分析工具。
