Genetic algorithm for selection of variables in second-order calibration methods

Author(s)

Renato Lajarim Carneiro

• Document type: Master's Dissertation
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Instituto de Química
• Defense date: 2007-10-07

Examining board members

Ronei Jesus Poppi; Adriana Vitorino Rossi; Dionisio Borsato

• Advisor: Ronei Jesus Poppi

Abstract

The aim of this work was to develop a program in MatLab using Genetic Algorithm (GA) to apply and to verify the main advantages of variables selection for second-order calibration methods (BLLS-RBL, PARAFAC and N-PLS). For this purpose three data sets had been used: 1. Determination of pesticides and a metabolite in red wines using HPLC-DAD in three distinct situations, where overlappings of the interferentes on interest compounds are observed. These composites were the pesticides carbaryl (CBL), methyl thiophanate (TIO), simazine (SIM) and dimethoate (DMT) and the metabolite phthalimide (PTA). 2. Quantification of the B2 (riboflavine) and (pyridoxine) B6 vitamins for spectrofluorimetry of excitation-emission in commercial infantile products, being three powder milk and two supplement foods. 3. Analysis of ascorbic acid (AA) and acetylsalicylic acid (AAS) in pharmaceutical tablets by FIA with pH gradient and detection for diode array, where the variation of pH causes alterations in the structure of molecules of analites shifting its spectra in the region of the ultraviolet. The performance of the models, with and without selection of variable, was compared through its errors, expressed as the root mean square error of prediction (RMSEP), and the relative errors of prediction (REP). The best results were obtained when the GA was used for the selection of variable in second-order calibration methods. (AU)