What can we add to our work by applying chemometrics when developing Analytical separations?

HÉCTOR C. GOICOECHEA; MARÍA J. CULZONI; MIRTA R. ALCARÁZ; CARLA M. TEGLIA; ADRIANO DE ARAÚJO GOMES; MILAGROS MONTEMURRO; SILVANA AZCARATE

Mendoza

Simposio; 24th Latin-American Symposium on Biotechnology, Biomedical, Biopharmaceutical, and Industrial Applications of Capillary Electrophoresis and Microchip Technology (LACE); 2018

Chemometrics can be defined as the science of extracting information from chemical systems by mathematical modeling of the data obtained as a result of experimentation procedures. It can be divided in three main areas: calibration, experimental design- optimization and classification. In this talk, fundamentals of the areas of chemometrics will be briefly presented. Then, several applications of chemometric tools for enhancing the potentiality of analytical methodologies developed in our laboratory concerning the separation field will be explored [1-3]. Firstly, experimental design for optimization of several responses and factors, which has shown to be a useful way to reach optimal conditions by doing a reduced number of experiments, will be presented. Then, classification of wines using second-order capillary electrophoresis-UV-Vis detection will be discussed. Finally, several second- and third-order calibration applications with capillary electrophoretic/chromatographic data applied to quantitate target compounds in complex samples presenting highly overlapped peaks will be analyzed. [1] M. Montemurro, G.G. Siano, M.R. Alcaráz, H.C. Goicoechea, Third order chromatographic-excitation-emission fluorescence data: Advances, challenges and prospects in analytical applications, Trends Anal. Chem. (TRAC) 93 (2017) 119-133.[2] C.M. Teglia, P.M. Peltzer, S.N. Seib, M.J. Culzoni, H.C. Goicoechea, Simultaneous multi-residue determination of twenty one veterinary drugs in poultry litter by multivariate modeling of liquid chromatography with fluorescence and UV detection data, Talanta 167 (2017) 442-452.[3] S.M. Azcarate, A. de Araujo Gomes, A. Muñoz de la Peña, H.C Goicoechea, Modeling second-order data for classification issues: data characteristics, algorithms, processing procedures and applications, Trends Anal. Chem. (TRAC) 107(2018) 151-169.