Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariada
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | http://hdl.handle.net/1843/SFSA-9RMGC8 |
Resumo: | Aiming at merging modern mass spectrometry techniques (electrospray ionization mass spectrometry - ESI-MS and easy ambient sonic-spray ionization mass spectrometry -EASI-MS) with chemometric methods (partial least squares -PLS and partial least squares discriminant analysis PLS-DA) the present work was developed forquality control of extra virgin olive oil and diesel b (blends diesel/biodiesel). The chapters were organized as following indicated:Chapter 4 was designed for quality control of extra virgin olive oil. A PLS2-DA model was developed basead on ESI-MS data, for classification of seven classes of olive oil (ordinary olive oil, extra virgin olive oil and adulterated with five adulterants oils). The best model was built with eight latent variables and showing good sensitivity (1.000) and specificity (0.967 1.000) values for the training and test sets. PLS models were also built, with seven models built with ESI-MS data, two models with data from a mass spectrometer for high resolution ESI-HRMS and a model constructed from data EASI (+)-MS. The 10 models were constructed for the quantification of adulterants oils( soybean, corn, sunflower and canola) in extra virgin olive oil. The models were validated by means of some figures of merit, was evaluated in models linearity, bias, accuracy, precision, selectivity, sensitivity and analytical sensitivity, limits of detection and quantification and Residual Prediction Deviation (RPD). Chapter 5 was intended for diesel b quality control ESI-MS data was used to construct a model for quantification of biodiesel in diesel. This model was also validated similarly as above mentioned. The proposed methods are promising because they are simple and fast. All models showed high efficiency and can be used in quality control of samples of extra virgin olive oil and biesel b. |
id |
UFMG_8f0aaa2ad4e10b054d6fc034e1e201b6 |
---|---|
oai_identifier_str |
oai:repositorio.ufmg.br:1843/SFSA-9RMGC8 |
network_acronym_str |
UFMG |
network_name_str |
Repositório Institucional da UFMG |
repository_id_str |
|
spelling |
Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariadaAdulteraçãoControle de qualidadeAzeite de Oliva extra virgemPLSDiesel bESI-MSPLS-DAEASI-MSValidação MultivariadaQuímica analíticaAnalise multivariadaEspectrometria de massaQuimiometriaAzeiteAiming at merging modern mass spectrometry techniques (electrospray ionization mass spectrometry - ESI-MS and easy ambient sonic-spray ionization mass spectrometry -EASI-MS) with chemometric methods (partial least squares -PLS and partial least squares discriminant analysis PLS-DA) the present work was developed forquality control of extra virgin olive oil and diesel b (blends diesel/biodiesel). The chapters were organized as following indicated:Chapter 4 was designed for quality control of extra virgin olive oil. A PLS2-DA model was developed basead on ESI-MS data, for classification of seven classes of olive oil (ordinary olive oil, extra virgin olive oil and adulterated with five adulterants oils). The best model was built with eight latent variables and showing good sensitivity (1.000) and specificity (0.967 1.000) values for the training and test sets. PLS models were also built, with seven models built with ESI-MS data, two models with data from a mass spectrometer for high resolution ESI-HRMS and a model constructed from data EASI (+)-MS. The 10 models were constructed for the quantification of adulterants oils( soybean, corn, sunflower and canola) in extra virgin olive oil. The models were validated by means of some figures of merit, was evaluated in models linearity, bias, accuracy, precision, selectivity, sensitivity and analytical sensitivity, limits of detection and quantification and Residual Prediction Deviation (RPD). Chapter 5 was intended for diesel b quality control ESI-MS data was used to construct a model for quantification of biodiesel in diesel. This model was also validated similarly as above mentioned. The proposed methods are promising because they are simple and fast. All models showed high efficiency and can be used in quality control of samples of extra virgin olive oil and biesel b.Visando a possibilidade de união de técnicas modernas de espectrometria de massas, (espectrometria de massas com ionização eletrospray-ESI-MS e espectrometria de massas com ionização easy ambiente sonic spray-EASI-MS), com ferramentas quimiométricas, (PLS regressão por mínimos quadrados parciais do inglês, Partial Least Squares) e PLS-DA (análise discriminante por mínimos quadrados parciais, Partial Least Squares Discriminant Analysis), o presente trabalho foi desenvolvido para análise de azeite de oliva extra virgem e diesel b (misturas diesel/biodiesel). Os resultados foram organizados em dois capítulos, um destinado ao controle de qualidade do azeite de oliva extra virgem, capitulo 4, e o outro voltado ao controle de qualidade do diesel b, capitulo 5. Um modelo PLS2-DA foi desenvolvido, com dados de ESI-MS, para a classificação de sete classes de azeite de oliva (extra virgem, puro e adulterado com cinco tipos de óleos adulterantes). O melhor modelo foi construído com oito varáveis latentes e apresentou bons valores de sensibilidade qualitativa (1,000) e especificidade (0,967-1,000) para os conjuntos de treinamento e teste. Foi desenvolvido também 10 modelos PLS para quantificação de óleos adulterantes (soja, milho, girassol e canola) sendo dois deles construídos com dados obtidos de um espectrômetro de massas de alta resolução ESI-HRMS e um modelo construído a partir de dados de EASI(+)-MS. Os modelos foram validados por meio de algumas figuras de mérito e todos os modelos têm comportamento linear e não apresentaram erros sistemáticos. Calcularam-se os erros médio quadrático de calibração e previsão (RMSEC e RMSEP) dos modelos que variaram de 0,55 a 1,47 % m/m e 1,01 a 1,98 % m/m, respectivamente. Os limites de detecção e quantificação variaram de 0,1 a 0,7 %m/m e 0,3 a 1,39 % m/m, respectivamente. A relação de desempenho do desvio (RPD) para os conjuntos de calibração e validação também foram avaliados, figura de mérito que representa a capacidade preditiva do modelo e variaram de 1,4 a 5,6 e 2,94 a 5,5, sendo que apenas um modelo ficou com o RDP abaixo de 2,5. Um modelo foi construído com dados de ESI-MS para quantificação de biodiesel em diesel e também foi validado da mesma maneira como descrito acima, apresentando desempenho semelhante aos modelos de quantificação de óleos adulterantes em azeite de oliva. As metodologias proposta se mostraram bastante promissoras, por serem simples e rápidas. Todos os modelos apresentaram alta eficiência podendo ser usados no controle de qualidade de amostras de azeite de oliva extra virgem e diesel b.Universidade Federal de Minas GeraisUFMGRodinei AugustiMarcelo Martins de SenaRosineide Costa SimasCleiton Antônio NunesLeticia Malta CostaHelvecio Costa MenezesJunia de Oliveira Alves2019-08-14T02:51:11Z2019-08-14T02:51:11Z2014-09-25info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttp://hdl.handle.net/1843/SFSA-9RMGC8info:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMG2019-11-14T16:45:16Zoai:repositorio.ufmg.br:1843/SFSA-9RMGC8Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oairepositorio@ufmg.bropendoar:2019-11-14T16:45:16Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.none.fl_str_mv |
Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariada |
title |
Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariada |
spellingShingle |
Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariada Junia de Oliveira Alves Adulteração Controle de qualidade Azeite de Oliva extra virgem PLS Diesel b ESI-MS PLS-DA EASI-MS Validação Multivariada Química analítica Analise multivariada Espectrometria de massa Quimiometria Azeite |
title_short |
Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariada |
title_full |
Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariada |
title_fullStr |
Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariada |
title_full_unstemmed |
Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariada |
title_sort |
Controle de qualidade de azeite de oliva extra virgem e misturas diesel/biodiesel utilizando espectrometria de massas e validação multivariada |
author |
Junia de Oliveira Alves |
author_facet |
Junia de Oliveira Alves |
author_role |
author |
dc.contributor.none.fl_str_mv |
Rodinei Augusti Marcelo Martins de Sena Rosineide Costa Simas Cleiton Antônio Nunes Leticia Malta Costa Helvecio Costa Menezes |
dc.contributor.author.fl_str_mv |
Junia de Oliveira Alves |
dc.subject.por.fl_str_mv |
Adulteração Controle de qualidade Azeite de Oliva extra virgem PLS Diesel b ESI-MS PLS-DA EASI-MS Validação Multivariada Química analítica Analise multivariada Espectrometria de massa Quimiometria Azeite |
topic |
Adulteração Controle de qualidade Azeite de Oliva extra virgem PLS Diesel b ESI-MS PLS-DA EASI-MS Validação Multivariada Química analítica Analise multivariada Espectrometria de massa Quimiometria Azeite |
description |
Aiming at merging modern mass spectrometry techniques (electrospray ionization mass spectrometry - ESI-MS and easy ambient sonic-spray ionization mass spectrometry -EASI-MS) with chemometric methods (partial least squares -PLS and partial least squares discriminant analysis PLS-DA) the present work was developed forquality control of extra virgin olive oil and diesel b (blends diesel/biodiesel). The chapters were organized as following indicated:Chapter 4 was designed for quality control of extra virgin olive oil. A PLS2-DA model was developed basead on ESI-MS data, for classification of seven classes of olive oil (ordinary olive oil, extra virgin olive oil and adulterated with five adulterants oils). The best model was built with eight latent variables and showing good sensitivity (1.000) and specificity (0.967 1.000) values for the training and test sets. PLS models were also built, with seven models built with ESI-MS data, two models with data from a mass spectrometer for high resolution ESI-HRMS and a model constructed from data EASI (+)-MS. The 10 models were constructed for the quantification of adulterants oils( soybean, corn, sunflower and canola) in extra virgin olive oil. The models were validated by means of some figures of merit, was evaluated in models linearity, bias, accuracy, precision, selectivity, sensitivity and analytical sensitivity, limits of detection and quantification and Residual Prediction Deviation (RPD). Chapter 5 was intended for diesel b quality control ESI-MS data was used to construct a model for quantification of biodiesel in diesel. This model was also validated similarly as above mentioned. The proposed methods are promising because they are simple and fast. All models showed high efficiency and can be used in quality control of samples of extra virgin olive oil and biesel b. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-09-25 2019-08-14T02:51:11Z 2019-08-14T02:51:11Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/SFSA-9RMGC8 |
url |
http://hdl.handle.net/1843/SFSA-9RMGC8 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais UFMG |
publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais UFMG |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFMG instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
instname_str |
Universidade Federal de Minas Gerais (UFMG) |
instacron_str |
UFMG |
institution |
UFMG |
reponame_str |
Repositório Institucional da UFMG |
collection |
Repositório Institucional da UFMG |
repository.name.fl_str_mv |
Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG) |
repository.mail.fl_str_mv |
repositorio@ufmg.br |
_version_ |
1823248262209994752 |