Multivariate monitoring of soybean oil ethanolysis by FTIR

Zagonel, Giuliano Fernandes; Peralta‐Zamora, Patricio; Ramos, Luiz Pereira

doi:10.1016/j.talanta.2004.01.008

Cited by 127 publications

(46 citation statements)

References 17 publications

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“…Although soybean oil has been included in some chemometric studies comparing vegetable oils, soybean cultivars have yet to be extensively classified using multivariate techniques (13–15). This study proposes and describes an analytical method to detect, identify and quantify fatty acid profiles in soybeans, applying GC with FID detection associated with PCA, a powerful chemometric approach.…”

Section: Introductionmentioning

confidence: 99%

Chemometric Approach to Fatty Acid Profiles in Soybean Cultivars by Principal Component Analysis (PCA)

Shin

Hwang

Lee

et al. 2012

JFN

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The purpose of this study was to investigate the fatty acid profiles in 18 soybean cultivars grown in Korea. A total of eleven fatty acids were identified in the sample set, which was comprised of myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1, ω7), stearic (C18:0), oleic (C18:1, ω9), linoleic (C18:2, ω6), linolenic (C18:3, ω3), arachidic (C20:0), gondoic (C20:1, ω9), behenic (C22:0), and lignoceric (C24:0) acids by gas-liquid chromatography with flame ionization detector (GC-FID). Based on their color, yellow-, black-, brown-, and green-colored cultivars were denoted. Correlation coefficients (r) between the nine major fatty acids identified (two trace fatty acids, myristic and palmitoleic, were not included in the study) were generated and revealed an inverse association between oleic and linoleic acids (r=−0.94, p<0.05), while stearic acid was positively correlated to arachidic acid (r=0.72, p<0.05). Principal component analysis (PCA) of the fatty acid data yielded four significant principal components (PCs; i.e., eigenvalues>1), which together account for 81.49% of the total variance in the data set; with PC1 contributing 28.16% of the total. Eigen analysis of the correlation matrix loadings of the four significant PCs revealed that PC1 was mainly contributed to by oleic, linoleic, and gondoic acids, PC2 by stearic, linolenic and arachidic acids, PC3 by behenic and lignoceric acids, and PC4 by palmitic acid. The score plots generated between PC1-PC2 and PC3-PC4 segregated soybean cultivars based on fatty acid composition.

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Section: Introductionmentioning

confidence: 99%

Chemometric Approach to Fatty Acid Profiles in Soybean Cultivars by Principal Component Analysis (PCA)

Shin

Hwang

Lee

et al. 2012

JFN

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“…Fourier transform infra-red (FTIR) can also be used to monitor FAEE content [18] and FAME content [19]. Finally, nuclear magnetic resonance (H-NMR) measurements based on the α-CH 2 , glyceridic and methyl ester protons can be used to monitor the fractions of FAME (or FAEE but with a little more difficulties) and total remaining glycerides simultaneously [19]- [22].…”

Section: Transesterification Monitoring: Literature Reviewmentioning

confidence: 99%

Estimation of kinetic parameters and diffusion coefficients for the transesterification of triolein with methanol on a solid ZnAl2O4 catalyst

Allain¹,

Portha²,

Girot³

et al. 2016

Chemical Engineering Journal

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“…Several authors have used infrared spectroscopy to monitor the quality of biodiesel blends with conventional diesel fuel because of the versatility that this technique offers [16][17][18][19][20][21][22]. Several studies have used infrared spectroscopy and multivariate calibration to monitor the quality of pure biodiesel (B100) and to observe the transesterification reaction conversion [23][24][25][26][27][28][29]. Other authors have employed NIR spectroscopy to predict parameters related to the stability of biodiesel [24][25][26]; water content [24] using models based on NIR spectroscopy associated with partial least squares (PLS) and principal component regressions (PCR); iodine value and kinematic viscosity [25] in B100 using NIR and PLS and acid number and water content were also monitored in vegetable oils for biodiesel production using multivariate near infrared spectroscopy models [26].…”

Section: Introductionmentioning

confidence: 99%

Easy to use spectrophotometric method for determination of aromatic diamines in biodiesel samples

Batista¹,

Silva²,

Fonseca³

et al. 2013

Microchemical Journal

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Multivariate monitoring of soybean oil ethanolysis by FTIR

Cited by 127 publications

References 17 publications

Chemometric Approach to Fatty Acid Profiles in Soybean Cultivars by Principal Component Analysis (PCA)

Chemometric Approach to Fatty Acid Profiles in Soybean Cultivars by Principal Component Analysis (PCA)

Estimation of kinetic parameters and diffusion coefficients for the transesterification of triolein with methanol on a solid ZnAl2O4 catalyst

Easy to use spectrophotometric method for determination of aromatic diamines in biodiesel samples

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