Validation Of Models Of Multivariate Calibration: An Application In The Determination Of Polymorphic Purity Of Carbamazepine By Near Infrared Spectroscopy [validação De Modelos De Calibração Multivariada: Uma Aplicação Na Determinação De Pureza Polimórfica De Carbamazepina Por Espectroscopia No Infravermelho Próximo]

The application of analytical procedures based on multivariate calibration models has been limited in several areas due to requirements of validation and certification of the model. Procedures for validation are presented based on the determination of figures of merit, such as precision (mean, repeatability, intermediate), accuracy, sensitivity, analytical sensitivity, selectivity, signal-to-noise ratio and confidence intervals for PLS models. An example is discussed of a model for polymorphic purity control of carbamazepine by NIR diffuse reflectance spectroscopy. The results show that multivariate calibration models can be validated to fulfill the requirements imposed by industry and standardization agencies.27610041011Barros Neto, B., Pimentel, M.F., Araújo, M.C.U., (2002) Quim. Nova, 25, p. 856http://www.analytik-news.de/Presse/2002/178.htmlCharne, R., De Luna, F.C.A., Charnet, E.M.R., Bovino, H., (1999) Análise de Modelos de Regressão Linear: Com Aplicações, , Ed. da UNICAMP: CampinasMiller, J.N., Miller, J.C., (2000) Statistics and Chemometrics for Analytical Chemistry, 4a Ed., , Prentice Hall: United KingdomChui, Q.S.H., Zucchini, R.R., Lichtig, J., (2001) Quim. Nova, 24, p. 374Barros Neto, B., Scarminio, I.S., Bruns, R.E., (2001) Como Fazer Experimentos: Pesquisa e Desenvolvimento Na Ciência e Na Industria, 2a Ed., , Ed. da UNICAMP: CampinasBrereton, R.G., (2000) Analyst, 125, p. 2125Jong, S., (1993) Chemom. Intell. Lab. Syst., 18, p. 251Lorber, A., Faber, K., Kowalski, B.R., (1997) Anal. Chem., 69, p. 1620Faber, N.M., (1998) Anal. Chem., 70, p. 5108Ferré, J., Brown, S.D., Rius, F.X., (2000) J. Chemom., 15, p. 537Goicoechea, H.C., Olivier, A.C., (2001) Chemom. Intell. Lab. Syst., 56, p. 73Faber, N.M., (1998) J. Chemom., 12, p. 405Curie, L.A., (1999) Anal. Chim. Acta., 391, p. 105(1997) International Conference on Harmonization, ICH Harmonised Tripartite Guideline - Validation of Analytical Procedures: Metodology, 62, p. 27463. , Fed. Regist(1995) International Conference on Harmonization, ICH Harmonised Tripartite Guideline - Text on Validation of Analytical Procedures, 60, p. 11260. , Fed. RegistStandards Practices for Infrared Multivariate Quantitative Analysis, , ASTM E 1655-00Moffat, A.C., Trafford, A.D., Jee, R.D., Graham, P., (2000) Analyst, 125, p. 1341Laasonen, M., Pulkkinen, T.H., Simard, C., Rasanen, M., Vuorela, H., (2003) Anal. Chem., 75, p. 754De La Pena, A.M., Mansilla, A.E., Valenzuela, M.I.A., Goicoechea, H.C., Olivieri, A.C., (2002) Anal. Chim. Acta, 463, p. 75Fernandez, J.A., Jin, L., Wahl, F., Faber, N.M., Massart, D.L., (2003) Chemom. Intell. Lab. Sys., 65, p. 281Voet, H.V.D., (1999) J. Chemom., 13, p. 195Borka, L., Lönmo, A., Winsnes, R., (1992) Pharm. Acta Helv., 8, p. 231Rustichelli, C., Gamberini, G., Ferioli, V., Gamberini, M.C., Ficarra, R., Tommasini, S., (2000) J. Pharm. Biomed. Anal., 23, p. 4

State Of The Art Of Figures Of Merit In Multivariate Calibration [estado Da Arte De Figuras De Mérito Em Calibração Multivariada]

The validation of an analytical procedure must be certified through the determination of parameters known as figures of merit. For first order data, the acuracy, precision, robustness and bias is similar to the methods of univariate calibration. Linearity, sensitivity, signal to noise ratio, adjustment, selectivity and confidence intervals need different approaches, specific for multivariate data. Selectivity and signal to noise ratio are more critical and they only can be estimated by means of the calculation of the net analyte signal. In second order calibration, some differentes approaches are necessary due to data structure.32512781287Barros Neto, B., Pimentel, M.F., Araújo, M.C.U., (2002) Quim. Nova, 25, p. 856Booksh, K.S., Kowalski, B.R., (1994) Anal. Chem, 66, pp. 782AEscandar, G.M., Faber, N.K.M., Goicoechea, H.C., Muñoz de la Pena, A., Olivieri, A.C., Poppi, R.J., (2007) TrAC, Trends Anal. Chem, 26, p. 752Bro, R., (2006) Crit. Rev. Anal. 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Application Of Genetic Algorithm For Selection Of Variables For The Blls Method Applied To Determination Of Pesticides And Metabolites In Wine

A variable selection methodology based on genetic algorithm (GA) was applied in a bilinear least squares model (BLLS) with second-order advantage, in three distinct situations, for determination by HPLC-DAD of the pesticides carbaryl (CBL), methyl thiophanate (TIO), simazin (SIM) and dimethoate (DMT) and the metabolite phthalimide (PTA) in wine. The chromatographic separation was carried out using an isocratic elution with 50:50 (v/v) acetonitrile:water as mobile phase. Preprocessing methods were performed for correcting the chromatographic time shifts, baseline variation and background. The optimization by GA provided a significant reduction of the errors, where for SIM and PTA a decrease of three times the value obtained using all variables, and an improvement in the distribution of them, reducing the observed bias in the results were observed. Comparing the RMSEP of the optimized model with the uncertainty estimates of the reference values it is observed that GA can be a very useful tool in second-order models. © 2006 Elsevier B.V. All rights reserved.5951-2 SPEC. ISS.5158Forrest, S., (1993) Science, 261, p. 872Goldberg, D.E., (1989) Genetic Algorithm in Search, Optimization and Machine Learning, , Addison-Wesley, New York p. 125Bangalore, A.S., Shaffer, R.E., Small, G.W., Arnold, M.A., (1996) Anal. Chem., 68, p. 4200Ding, Q., Smal, G.W., Arnold, M.A., (1998) Anal. Chem., 70, p. 4472Costa Filho, P.A., Poppi, R.J., (2001) Anal. Chim. Acta, 446, p. 39Lomillo, M.A.A., Renedo, O., Martinéz, M.J.A., (2001) Anal. Chim. Acta, 449, p. 167Ghasemi, J., Niazi, A., Leardi, R., (2003) Talanta, 59, p. 311Abdollahi, A., Bagheri, L., (2004) Anal. Chim. Acta, 514, p. 211Dantas Filho, H.A., Souza, E.S.O.N., Visani, V., Barros, S.R.R.C., Saldanha, T.C.B., Araújo, M.C.U., Galvão, R.K.H., (2005) J. Braz. Chem. Soc., 16, p. 58Ghasemi, J., Ebrahimi, D.M., Hejazi, L., Leardi, R., Niazi, A., (2006) J. Anal. Chem., 61, p. 92Franco, V.G., Perin, J.C., Mantovani, V.E., Goicoechea, H.C., (2006) Talanta, 68, p. 1005Wu, W., Guo, Q., Massart, D.L., Boucon, C., de Jong, S., (2003) Chemometr. Intell. Lab. Syst., 65, p. 83Lopes, J.A., Menezes, J.C., (2003) Chemometr. Intell. Lab. Syst., 68, p. 75Gourvénec, S., Capron, X., Massart, D.L., (2004) Anal. Chim. Acta, 519, p. 11Linder, M., Sundberg, R., (1998) Chemometr. Intell. Lab. Syst., 42, p. 159Linder, M., Sundberg, R., (2002) J. Chemometr., 16, p. 12Damiani, P.C., Nepote, A.J., Bearzotti, M., Olivieri, A.C., (2004) Anal. Chem., 76, p. 2798Marsili, N.R., Lista, A., Band, B.S.F., Goicoechea, H.C., Olivieri, A.C., (2005) Analyst, 130, p. 1291Goicoechea, H.C., Olivieri, A.C., (2005) Appl. Spectrosc., 59, p. 926Haimovich, A., Orselli, R., Escandar, G.M., Olivieri, A.C., (2006) Chemometr. Intell. Lab. Syst., 80, p. 99Faber, N.M., Ferré, J., Boqué, R., Kalivas, J.H., (2002) Chemometr. Intell. Lab. Syst., 63, p. 107Öhman, J., Geladi, P., Wold, S., (1990) J. Chemometr., 4, p. 79Lorber, A., (1986) Anal. Chem., 58, p. 1167Messick, N.J., Kalivas, J.H., Lang, P.M., (1996) Anal. Chem., 68, p. 1572Ho, C.N., Christian, G.D., Davidson, E.R., (1980) Anal. Chem., 52, p. 1071Olivieri, A.C., Faber, N.M., (2005) J. Chemometr., 19, p. 583Boqué, R., Larrechi, M.S., Rius, F.X., (1999) Chemometr. Intell. Lab. Syst., 45, p. 397Boqué, R., Ferre, J., Faber, N.M., Rius, F.X., (2002) Anal. Chim. Acta, 451, p. 313Olivieri, A.C., Faber, N.M., (2004) Chemometr. Intell. Lab. Syst., 70, p. 75Miller-Ihli, N.J., O'Haver, T.C., (1984) Spectrochim. Acta, 39 B, p. 1603http://www.codexalimentarius.net/mrls/pestdes/jsp/pest_q-e.jspPrazen, B.J., Synovec, R.E., Kowalski, B.R., (1998) Anal. Chem., 70, p. 218Riu, J., Rius, F.X., (1996) Anal. Chem., 68, p. 185