Trilinear Least-Squares Coupled to Residual Trilinearization: Anew Chemometric Tool for the Analysis of Four_Way Instrumental Data

ARSENIO MUÑOZ DE LA PEÑA; ANUNCIACIÓN ESPINOSA MANSILLA; ISABEL DURÁN MERÁS; DIEGO BOHOYO GIL; ALEJANDRO C. OLIVIERI; JUAN A. ARANCIBIA

Lisboa, Portugal

Congreso; 9th International Conference on Methods and Applications of Fluorescence Spectroscopy, Imaging and Probes; 2005

Multi-way data analysis is a powerful tool for the development of analytical determination methods, in the presence of complex samples components. The advantages of using data involving high-dimensional structured information is a higher stability towards interferents and matrix effects, in comparison with firs-order methodologies. Interestingly, most of the published analytical applications of second order (three-way) data are related to the use of Excitation-Emission Matrices (EEMs) as the analytical signal. Four-way data provide the opportunity of introducing an additional dimension to tree-way data sets, allowing for a theoretical improvement of the predictive ability of the model. Few experimental realizations for this interesting calibration scenario are known, most of them involving the analysis of the time-evolution of EEMs in cases in which a chemical reaction occurs. In this communication, a new chemometric algorithm, trilinear least-squares (TLLS) couplet to residual trilinearization (RTL), developed as an extension of bilinear leas squares (BLLS) coupled to residual bilinearization (RBL), is described for the analysis of four-way data. Monte Carlo numerical simulations are employed to compare its performance with that of the well-known parallel factor analysis (PARAFAC) model. An experimental system based on the kinetic measurement of the evolution of the excitation-emission fluorescence matrices with time for binary mixtures of anticancer drugs is also shown. The kinetic reaction is based on the oxidation of leucovorin and methotrexate with potassium permanganate to give highly fluorescent compounds. Both chemometric methodologies exploit the “second-order advantage” of the employed multiway data, allowing analyte concentrations to be estimated even in the presence of uncalibrated components in the samples. The new method herein described constitutes the only competitor of PARAFAC for this type of analyses, in which four-way instrumental data are used.