Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafés
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFPB |
Texto Completo: | https://repositorio.ufpb.br/jspui/handle/123456789/13045 |
Resumo: | The search for a better quality of life has led to increased consumption of functional foods. Ensure a quality of food is an aspect of great relevance and, therefore, the parameters of quality in all cases and being constantly monitored by inspection agencies. Thus, this thesis presents the development of analytical methods that were evaluated to monitor some quality parameters in samples of functional flours and coffee. The work was divided into three stages. The first stage (Chapter II) reports the determination of the concentrations of macroelements, microelements and non-essential elements in 60 samples of functional flours of 20 different types. The spectroanalytical techniques: optical emission spectrometry with inductively coupled plasma (ICP OES) and inductively coupled plasma mass spectrometry (ICP-MS) were used in the determinations of these elements. A statistical approach utilizing Principal Component Analysis (PCA) was realized for exploratory data analysis (elements) in characterization of the flour samples. The concentrations of macroelements (K> P> Ca> Mg), ranged from 746.4 up to 30,328 mg kg-1, and concentrations of the microelements (in descending order for Fe, Al, Mn, Cu, Mo, and Cr) ranged from 0.03 up to 160.1 mg kg-1. The non-essential elements, As, Cd and Pb, determined in some samples of the flours presented concentrations that are within the range required for a daily intake. The second stage (Chapter III) were investigates different strategies of calibration model transfer to determine crude protein and fiber properties in samples of flours with functional properties using portable and benchtop near infrared (NIR) region instruments. In the study, direct standardization (DS), Piecewise Direct Standardization (PDS), and reverse standardization (RS) methods were evaluated for the transfer of the spectra obtained with the two instruments and different numbers of transfer samples were tested. The Partial Least Squares with the full spectrum (PLSfull spectrum), PLS with regression coefficients selected by the Jack-Knife algorithm (PLS/JK) and Multiple Linear Regression (MLR) with previous selection of variables by the Successive Projections Algorithm (MLR/SPA) calibration models were evaluated. The results showed that the lowest values of the Root Mean Squared Error of Prediction (RMSEP) were obtained with the PLSfull spectrum (1.10) and MLR/SPA (1.45) models for the crude protein parameter using the DS method, while for fiber the lowest RMSEP value was obtained using the PLSfull spectrum (2.34) with RS, although no statistically significant difference was found among the RMSEP values obtained for the analyzed models, confirmed through F test (95% confidence level). Finally, in the third stage (Chapter IV), Thermogravimetric Analysis (TGA) and Linear Discriminant Analysis (LDA) were used to classify coffee samples as caffeinated or decaffeinated. The classification models were constructed through the association of LDA and algorithms of selection of variables: SPA, Genetic Algorithm (GA), and Stepwise (SW). The GA/LDA model presented no error in the classification of the test samples (caffeinated: 8, decaffeinated: 5), while the SPA/LDA and SW/LDA models presented 1 error in each. The models were compared in terms of accuracy, specificity and sensitivity values obtained for the test subsets, in which GA / LDA presented the best performance with 100% accuracy and 1.00 in sensitivity and specificity values for each class. |
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Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafésAlimentosICP OESICP-MSTransferência de calibraçãoNIRTGALDAFoodCalibration transferCNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICAThe search for a better quality of life has led to increased consumption of functional foods. Ensure a quality of food is an aspect of great relevance and, therefore, the parameters of quality in all cases and being constantly monitored by inspection agencies. Thus, this thesis presents the development of analytical methods that were evaluated to monitor some quality parameters in samples of functional flours and coffee. The work was divided into three stages. The first stage (Chapter II) reports the determination of the concentrations of macroelements, microelements and non-essential elements in 60 samples of functional flours of 20 different types. The spectroanalytical techniques: optical emission spectrometry with inductively coupled plasma (ICP OES) and inductively coupled plasma mass spectrometry (ICP-MS) were used in the determinations of these elements. A statistical approach utilizing Principal Component Analysis (PCA) was realized for exploratory data analysis (elements) in characterization of the flour samples. The concentrations of macroelements (K> P> Ca> Mg), ranged from 746.4 up to 30,328 mg kg-1, and concentrations of the microelements (in descending order for Fe, Al, Mn, Cu, Mo, and Cr) ranged from 0.03 up to 160.1 mg kg-1. The non-essential elements, As, Cd and Pb, determined in some samples of the flours presented concentrations that are within the range required for a daily intake. The second stage (Chapter III) were investigates different strategies of calibration model transfer to determine crude protein and fiber properties in samples of flours with functional properties using portable and benchtop near infrared (NIR) region instruments. In the study, direct standardization (DS), Piecewise Direct Standardization (PDS), and reverse standardization (RS) methods were evaluated for the transfer of the spectra obtained with the two instruments and different numbers of transfer samples were tested. The Partial Least Squares with the full spectrum (PLSfull spectrum), PLS with regression coefficients selected by the Jack-Knife algorithm (PLS/JK) and Multiple Linear Regression (MLR) with previous selection of variables by the Successive Projections Algorithm (MLR/SPA) calibration models were evaluated. The results showed that the lowest values of the Root Mean Squared Error of Prediction (RMSEP) were obtained with the PLSfull spectrum (1.10) and MLR/SPA (1.45) models for the crude protein parameter using the DS method, while for fiber the lowest RMSEP value was obtained using the PLSfull spectrum (2.34) with RS, although no statistically significant difference was found among the RMSEP values obtained for the analyzed models, confirmed through F test (95% confidence level). Finally, in the third stage (Chapter IV), Thermogravimetric Analysis (TGA) and Linear Discriminant Analysis (LDA) were used to classify coffee samples as caffeinated or decaffeinated. The classification models were constructed through the association of LDA and algorithms of selection of variables: SPA, Genetic Algorithm (GA), and Stepwise (SW). The GA/LDA model presented no error in the classification of the test samples (caffeinated: 8, decaffeinated: 5), while the SPA/LDA and SW/LDA models presented 1 error in each. The models were compared in terms of accuracy, specificity and sensitivity values obtained for the test subsets, in which GA / LDA presented the best performance with 100% accuracy and 1.00 in sensitivity and specificity values for each class.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESA busca por uma melhor qualidade de vida tem levado ao aumento do consumo de alimentos com propriedades funcionais. Garantir a qualidade de tais alimentos é um aspecto de grande relevância e, por isso, os parâmetros de qualidade vêm sendo constantemente monitorados por órgãos de fiscalização. Assim, este trabalho de tese de doutorado apresenta o desenvolvimento de métodos analíticos que foram avaliados para monitorar alguns parâmetros de qualidade em amostras de farinhas funcionais e cafés. O trabalho foi dividido em três etapas. A primeira etapa (capítulo II) relata a determinação das concentrações dos macrelementos, microelementos e elementos não essenciais em 60 amostras de farinhas funcionais de 20 diferentes tipos. A espectrometria de emissão óptica com plasma indutivamente acoplado (ICP OES) e espectrometria de massa com plasma indutivamente acoplado (ICP-MS) foram utilizadas nas determinações destes elementos. Uma abordagem estatística utilizando Análise por Componentes Principais (PCA) foi realizada para análise exploratória dos dados (elementos) na caracterização das amostras de farinhas. As concentrações dos macroelementos (K> P> Ca>Mg), variaram de 746,4 a 30.328 mg kg-1, e as concentrações dos microelementos (em ordem decrescente para Fe, Al, Mn, Cu, Mo e Cr), variaram de 0,03 até 160,1 mg kg-1. Os elementos não essenciais, As, Cd e Pb, determinados em algumas amostras das farinhas apresentaram concentrações que estavam dentro da faixa requerida para uma ingestão diária. Na segunda etapa (Capítulo III) foram investigadas diferentes estratégias de transferência de modelos de calibração para determinar propriedades de proteínas brutas e fibras em amostras de farinhas com propriedades funcionais utilizando instrumentos que trabalham na região do infravermelho próximo (NIR) portátil e de bancada. Neste estudo, métodos de padronização direta (DS), padronização direta por partes (PDS) e padronização reversa (RS) foram avaliados para a transferência dos espectros obtidos nos dois instrumentos e diferentes números de amostras de transferência foram testados. Os modelos de calibração dos Mínimos Quadrados Parciais com o espectro completo (PLSespectro completo), PLS com os coeficientes de regressão significativos selecionados pelo algoritmo Jack-Knife (PLS/JK) e Regressão Linear Múltipla (MLR) com prévia seleção de variáveis pelo Algoritmo das Projeções Sucessivas (MLR/SPA) foram avaliados. Os resultados mostraram que os menores valores da Raiz do Erro Quadrático Médio de Predição (RMSEP) foram obtidos com os modelos PLSespectro completo (1,10) e MLR/SPA (1,45) para o parâmetro proteína bruta usando o método DS, enquanto para fibra, o valor RMSEP mais baixo foi obtido utilizando o PLSespectro completo (2,34) com RS, embora não tenha sido encontrada diferença estatisticamente significava entre os valores de RMSEP obtidos para os modelos analisados, confirmados através do teste F (95% de nível de confiança). Por fim, na terceira etapa (Capítulo IV) a Análise Termogravimetrica (TGA) aliada a Análise Discriminante Linear (LDA) foi usada para classificar amostras de cafés quanto ao tipo cafeinado ou descafeinados. Os modelos de classificação foram construídos através da associação da LDA e algoritmos de seleção de variáveis: SPA, Algoritmo Genético (GA) e Stepwise (SW). O modelo GA/LDA não apresentou nenhum erro na classificação das amostras de teste (cafeinado: 8; descafeinado: 5) enquanto os modelos SPA/LDA e SW/LDA apresentaram 1 erro em cada. Os modelos foram comparados em termos de exatidão e dos valores de especificidade e sensibilidade obtidos para as amostras de teste, em que o GA/LDA apresentou a melhor performance com 100% de exatidão e 1,00 nos valores de sensibilidade e especificidade para cada classe.Universidade Federal da ParaíbaBrasilQuímicaPrograma de Pós-Graduação em QuímicaUFPBPontes, Liliana de Fatima Bezerra Lira dehttp://lattes.cnpq.br/0438588394065892Pessoa, Amália Geiza Gamahttp://lattes.cnpq.br/7413783656311087Brito, Anna Luiza Bizerra de2019-01-22T20:29:54Z2019-01-222019-01-22T20:29:54Z2018-02-23info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesishttps://repositorio.ufpb.br/jspui/handle/123456789/13045porAttribution-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nd/3.0/br/info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFPBinstname:Universidade Federal da Paraíba (UFPB)instacron:UFPB2019-01-23T06:01:44Zoai:repositorio.ufpb.br:123456789/13045Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufpb.br/PUBhttp://tede.biblioteca.ufpb.br:8080/oai/requestdiretoria@ufpb.br|| diretoria@ufpb.bropendoar:2019-01-23T06:01:44Biblioteca Digital de Teses e Dissertações da UFPB - Universidade Federal da Paraíba (UFPB)false |
dc.title.none.fl_str_mv |
Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafés |
title |
Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafés |
spellingShingle |
Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafés Brito, Anna Luiza Bizerra de Alimentos ICP OES ICP-MS Transferência de calibração NIR TGA LDA Food Calibration transfer CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA |
title_short |
Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafés |
title_full |
Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafés |
title_fullStr |
Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafés |
title_full_unstemmed |
Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafés |
title_sort |
Desenvolvimento de métodos analíticos para monitoramento da qualidade de farinhas funcionais e cafés |
author |
Brito, Anna Luiza Bizerra de |
author_facet |
Brito, Anna Luiza Bizerra de |
author_role |
author |
dc.contributor.none.fl_str_mv |
Pontes, Liliana de Fatima Bezerra Lira de http://lattes.cnpq.br/0438588394065892 Pessoa, Amália Geiza Gama http://lattes.cnpq.br/7413783656311087 |
dc.contributor.author.fl_str_mv |
Brito, Anna Luiza Bizerra de |
dc.subject.por.fl_str_mv |
Alimentos ICP OES ICP-MS Transferência de calibração NIR TGA LDA Food Calibration transfer CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA |
topic |
Alimentos ICP OES ICP-MS Transferência de calibração NIR TGA LDA Food Calibration transfer CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA |
description |
The search for a better quality of life has led to increased consumption of functional foods. Ensure a quality of food is an aspect of great relevance and, therefore, the parameters of quality in all cases and being constantly monitored by inspection agencies. Thus, this thesis presents the development of analytical methods that were evaluated to monitor some quality parameters in samples of functional flours and coffee. The work was divided into three stages. The first stage (Chapter II) reports the determination of the concentrations of macroelements, microelements and non-essential elements in 60 samples of functional flours of 20 different types. The spectroanalytical techniques: optical emission spectrometry with inductively coupled plasma (ICP OES) and inductively coupled plasma mass spectrometry (ICP-MS) were used in the determinations of these elements. A statistical approach utilizing Principal Component Analysis (PCA) was realized for exploratory data analysis (elements) in characterization of the flour samples. The concentrations of macroelements (K> P> Ca> Mg), ranged from 746.4 up to 30,328 mg kg-1, and concentrations of the microelements (in descending order for Fe, Al, Mn, Cu, Mo, and Cr) ranged from 0.03 up to 160.1 mg kg-1. The non-essential elements, As, Cd and Pb, determined in some samples of the flours presented concentrations that are within the range required for a daily intake. The second stage (Chapter III) were investigates different strategies of calibration model transfer to determine crude protein and fiber properties in samples of flours with functional properties using portable and benchtop near infrared (NIR) region instruments. In the study, direct standardization (DS), Piecewise Direct Standardization (PDS), and reverse standardization (RS) methods were evaluated for the transfer of the spectra obtained with the two instruments and different numbers of transfer samples were tested. The Partial Least Squares with the full spectrum (PLSfull spectrum), PLS with regression coefficients selected by the Jack-Knife algorithm (PLS/JK) and Multiple Linear Regression (MLR) with previous selection of variables by the Successive Projections Algorithm (MLR/SPA) calibration models were evaluated. The results showed that the lowest values of the Root Mean Squared Error of Prediction (RMSEP) were obtained with the PLSfull spectrum (1.10) and MLR/SPA (1.45) models for the crude protein parameter using the DS method, while for fiber the lowest RMSEP value was obtained using the PLSfull spectrum (2.34) with RS, although no statistically significant difference was found among the RMSEP values obtained for the analyzed models, confirmed through F test (95% confidence level). Finally, in the third stage (Chapter IV), Thermogravimetric Analysis (TGA) and Linear Discriminant Analysis (LDA) were used to classify coffee samples as caffeinated or decaffeinated. The classification models were constructed through the association of LDA and algorithms of selection of variables: SPA, Genetic Algorithm (GA), and Stepwise (SW). The GA/LDA model presented no error in the classification of the test samples (caffeinated: 8, decaffeinated: 5), while the SPA/LDA and SW/LDA models presented 1 error in each. The models were compared in terms of accuracy, specificity and sensitivity values obtained for the test subsets, in which GA / LDA presented the best performance with 100% accuracy and 1.00 in sensitivity and specificity values for each class. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-02-23 2019-01-22T20:29:54Z 2019-01-22 2019-01-22T20:29:54Z |
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 |
https://repositorio.ufpb.br/jspui/handle/123456789/13045 |
url |
https://repositorio.ufpb.br/jspui/handle/123456789/13045 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
Attribution-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nd/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal da Paraíba Brasil Química Programa de Pós-Graduação em Química UFPB |
publisher.none.fl_str_mv |
Universidade Federal da Paraíba Brasil Química Programa de Pós-Graduação em Química UFPB |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da UFPB instname:Universidade Federal da Paraíba (UFPB) instacron:UFPB |
instname_str |
Universidade Federal da Paraíba (UFPB) |
instacron_str |
UFPB |
institution |
UFPB |
reponame_str |
Biblioteca Digital de Teses e Dissertações da UFPB |
collection |
Biblioteca Digital de Teses e Dissertações da UFPB |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da UFPB - Universidade Federal da Paraíba (UFPB) |
repository.mail.fl_str_mv |
diretoria@ufpb.br|| diretoria@ufpb.br |
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1801842941815160832 |