An efficient method for determining the chemical rank of three-way fluorescence data arrays

An efficient method is proposed for determining the chemical rank of three-way fluorescence data arrays. At first, the original three-way fluorescence data arrays are preprocessed by Monte Carlo simulation and a new set of data arrays is generated. The new set of data arrays obtained does not only keep all the useful information, but the noises from the common background are largely removed, which results in the improvement of the signal to noise ratio of the data and is beneficial for the later frequency analysis. Then, we perform singular value decomposition over the new data and frequency analysis on the subsequent eigenvectors, with which it is very easy to distinguish the spectra from the noises. Furthermore, a new quantity frequency localization is introduced to quantify the frequency characteristics of the eigenvectors. With this quantity, we can easily and accurately select out the spectra from the mess of data. The feasibility of the method is verified by determining the chemical rank of two-component mixtures with simple calculation procedures and high efficiency. Finally, the efficiency of our method is further illustrated by comparison with the core consistency diagnostic (CORCONDIA) method in the analysis of mixtures with different concentration and different number of components.