Prediction of retention indices for identification of fatty acid methyl esters

Farkas, Orsolya; Зенкевич, И. Г.; Stout, Forrest; Kalivas, John H.; Héberger, Károly

doi:10.1016/j.chroma.2008.05.019

Cited by 35 publications

(15 citation statements)

References 30 publications

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“…The lipid phase is extracted with organic solvents, and the fatty acid profile is determined after transesterification into fatty acid methyl esters (FAMEs), followed by gas chromatography-flame ionization detector (GC-FID) analysis. GC-FID is the method of choice for the determination of relative contributions of individual fatty acids to the fatty acid pattern due to the relative constant response factors irrespective of the chain length and number of double bonds (THOMPSON, 1996;TORRES;TRUGO;TRUGO, 2002;THURNHOFER;VETTER, 2006;VENKATACHALAM;SATHE, 2006;FARKAS et al, 2008).…”

Section: Fatty Acid Identificationmentioning

confidence: 99%

Fatty acids profile of pulp and nuts of Brazilian fruits

Costa

Ballus

Filho

et al. 2011

Ciênc. Tecnol. Aliment.

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ResumoFrutas e castanhas das regiões Norte e Nordeste do Brasil foram coletadas para determinação da composição em ácidos graxos do óleo da polpa e da castanha. As espécies estudadas foram castanha-do-pará (Bertholletia excelsa H.B.K.), mucajá (Coumarigida M.), inajá (Maximilianamaripa D.), jenipapo (Genipa Americana L.) e buriti (Mauritia flexuosa L.). Os ésteres metílicos de ácidos graxos foram analisados por cromatografia gasosa com detector por ionização em chama (GC-FID). O ácido graxo majoritário da castanha-do-pará foi o 18:3n-3 (ácido α-linolênico). A castanha do buriti apresentou aproximadamente 23 vezes mais 18:3n-3 do que a polpa. A castanha do mucajá apresentou elevados teores de 12:0 (ácido láurico) e 16:0 (ácido palmítico), enquanto a polpa do mucajá mostrou níveis significativos de 18:2n-6 (ácido linoleico). Considerando o somatório de ácidos graxos poli-insaturados (PUFA), quase todas as frutas e castanhas analisadas apresentaram níveis bastante altos destes compostos. No que se refere à razão n-6/n-3, apenas a castanha-do-pará, a castanha de buriti, a polpa de inajá e a polpa de jenipapo corresponderam ao perfil desejado. Estas frutas e castanhas brasileiras podem ser de potencial interesse devido ao seu elevado valor nutritivo e teor de lipídios. Palavras-chave: Bertholletia excelsa H.B.K.; Couma rigida M.; Maximiliana maripa D.; Genipa Americana L.; Mauritia flexuosa L.; ácidos graxos; GC-FID. AbstractFruits and nuts from the North and Northeast regions of Brazil were collected to determine the fatty acid profile of their oils. The species studied were Brazil (Bertholletia excelsa H.B.K.), Mucajá (Couma rigida M.), Inajá (Maximiliana maripa D.), Jenipapo (Genipa Americana L.), and Buriti (Mauritia flexuosa L.) nuts. Fatty acid methyl esters were analyzed by gas chromatography with flame ionization detection (GC-FID). Brazil nut major fatty acid was 18:3n-3 (α-linolenic acid), and Buriti nut had approximately 23 times more 18:3n-3 than the pulp. Mucajá nut presented high content of 12:0 (lauric acid) and 16:0 (palmitic acid), and Mucajá pulp showed significant levels of 18:2n-6 (linoleic acid). Considering the PUFA (polyunsaturated fatty acid) sum values, almost all fruits and nuts analyzed presented very high levels of these compounds. Regarding n-6/n-3 ratio, only Brazil Nut, Buriti Nut, Inajá pulp, and Jenipapo pulp corresponded to the desired profile. These Brazilian fruits and nuts could be of potential interest due to their high nutritive value and lipid content.

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Section: Fatty Acid Identificationmentioning

confidence: 99%

Fatty acids profile of pulp and nuts of Brazilian fruits

Costa

Ballus

Filho

et al. 2011

Ciênc. Tecnol. Aliment.

View full text Add to dashboard Cite

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“…QSRR can be decomposed into three main steps: (1) variable selection, (2) model building, and (3) model validation [2]. Quantitative structure-retention relationships (QSRRs) represent a powerful technique for relating the chromatographic retention parameters of groups of analytes and their descriptors, which are quantities encoding the structural characteristics [3][4][5]. Multiple linear regression (MLR) was the first statistical tool widely used for QSRR models [6,7].…”

Section: Introductionmentioning

confidence: 99%

Comparative QSRR Modeling of Nitrobenzene Derivatives Based on Original Molecular Descriptors and Multivariate Image Analysis Descriptors

Garkani-Nejad

Ahmadvand

2011

Chromatographia

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A comparative quantitative structure-retention relationship (QSRR) study has been carried out to predict the retention time of nitrobenzene derivatives using original molecular descriptors and multivariate image analysis (MIA) descriptors. First, original molecular descriptors were generated from molecular structures and applied to construct QSRR models using multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN) modeling methods. Then, multivariate image analysis (MIA) descriptors were generated from pixels of images and analyzed using correlation rankingprincipal component regression (CR-PCR) and correlation ranking-principal component-artificial neural network (CR-PC-ANN) methods. In this paper, the CR-PC-ANN method presented better results than the other methods for predicting the retention time of the studied compounds. Coefficients of determination (R 2 ) using the CR-PC-ANN method for the training, test, and validation sets were 0.989, 0.999, and 0.999, respectively.

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“…Some methods to evaluate the importance of variables have been proposed in QSRRs studies . The simple and understandable index, correlation coefficient of regression equation (or modified by weighting), was widely applied in many publications . Nevertheless, because of a large number of descriptors (e.g.…”

Section: Resultsmentioning

confidence: 99%

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine

Yan

Huang

et al. 2013

J of Separation Science

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Retention indices for frequently reported compounds of plant essential oils on three different stationary phases were investigated. Multivariate linear regression, partial least squares, and support vector machine combined with a new variable selection approach called random-frog recently proposed by our group, were employed to model quantitative structure-retention relationships. Internal and external validations were performed to ensure the stability and predictive ability. All the three methods could obtain an acceptable model, and the optimal results by support vector machine based on a small number of informative descriptors with the square of correlation coefficient for cross validation, values of 0.9726, 0.9759, and 0.9331 on the dimethylsilicone stationary phase, the dimethylsilicone phase with 5% phenyl groups, and the PEG stationary phase, respectively. The performances of two variable selection approaches, random-frog and genetic algorithm, are compared. The importance of the variables was found to be consistent when estimated from correlation coefficients in multivariate linear regression equations and selection probability in model spaces.

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Prediction of retention indices for identification of fatty acid methyl esters

Cited by 35 publications

References 30 publications

Fatty acids profile of pulp and nuts of Brazilian fruits

Fatty acids profile of pulp and nuts of Brazilian fruits

Comparative QSRR Modeling of Nitrobenzene Derivatives Based on Original Molecular Descriptors and Multivariate Image Analysis Descriptors

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine

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