Multivariate Quality Control Applied to Detect the Soybean Oil Oxidation Using Fourier Transform Infrared Spectroscopy

Barthus, Rosangela Cristina; Poppi, Ronei J.

doi:10.1081/sl-120014943

Cited by 9 publications

(9 citation statements)

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“…The aim of the present paper was to determine liquid−liquid equilibrium data at different temperatures of (313.15, 318.15, and 328.15) K for fatty systems of interest in biodiesel production, using near-infrared spectroscopy (NIR) coupled with chemometric analysis (multivariate calibration and PLS regressionPartial Least Squares) as the analytical method for quantification of the phase compositions. Near-infrared spectroscopy supplies qualitative data about the samples that can be transformed into quantitative information using chemometric methods based on statistical and mathematical procedures . Reports on the use of NIR spectroscopy in the analysis of vegetable oils and in the determination of liquid−liquid equilibrium data for fatty systems can be found in the literature. ,− …”

Section: Introductionmentioning

confidence: 99%

“…Near-infrared spectroscopy supplies qualitative data about the samples that can be transformed into quantitative information using chemometric methods based on statistical and mathematical procedures. 17 Reports on the use of NIR spectroscopy in the analysis of vegetable oils and in the determination of liquid-liquid equilibrium data for fatty systems can be found in the literature. 11,[18][19][20][21]…”

Section: Introductionmentioning

confidence: 99%

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Liquid−Liquid Equilibrium Data for Systems Containing Vegetable Oils, Anhydrous Ethanol, and Hexane at (313.15, 318.15, and 328.15) K

et al. 2009

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The aim of this work was to determine liquid−liquid equilibrium data for systems of interest in ethyl ester (biodiesel) production. The following systems were investigated: refined soybean oil + anhydrous ethanol + hexane at (313.15 ± 0.1) K and (328.25 ± 0.1) K, refined canola oil + anhydrous ethanol + hexane at (313.25 ± 0.1) K and (328.15 ± 0.1) K, and refined palm oil + anhydrous ethanol + hexane at (318.15 ± 0.1) K and (328.25 ± 0.1) K. The instrumental technique of near-infrared spectroscopy allied to multivariate calibration (in this work, this union was denominated “NIR method”) was used for quantification of the phase compositions. The NIR method presented an appropriate experimental accuracy for quantifying equilibrium data at higher temperatures. Good reliability of the phase compositions was confirmed from values of global mass balance deviations ranging from (0.06 to 0.16) %. The experimental data were correlated using the NRTL model with an average global deviation of 0.83 %.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Liquid−Liquid Equilibrium Data for Systems Containing Vegetable Oils, Anhydrous Ethanol, and Hexane at (313.15, 318.15, and 328.15) K

et al. 2009

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“…Three very significant regions for the characterization of blends by PLS-DA stand out in the loading plot, Figure : (i) 1800–1500 cm –1 , with carbonyl CO stretching and CC vibration and (ii) 1500–1150 cm –1 , corresponding to C–O and C–C stretching, also representing the initial part of the “finger print” region. The region ranging from 3100 to 2700 cm –1 also shown in the loading plot corresponds to the symmetric and asymmetric stretching of the C–H bond of sp 2 and sp 3 carbons. , The selected regions correspond to ester chains in the investigated biodiesels and to the triacylglyceride chain in soybean oil.…”

Section: Results and Discussionmentioning

confidence: 99%

“…The region ranging from 3100 to 2700 cm −1 also shown in the loading plot corresponds to the symmetric and asymmetric stretching of the C−H bond of sp 2 and sp 3 carbons. 38,69 The selected regions correspond to ester chains in the investigated biodiesels and to the triacylglyceride chain in soybean oil.…”

Section: Methodsmentioning

confidence: 99%

Characterization of Biodiesel from Animal Fat, Vegetable Oil, and Adulterants by Infrared Spectroscopy Combined with Chemometric Methods

et al. 2021

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Second- and third-generation feedstocks, combined with first-generation feedstocks, have been showing a tendency to meet the supply and quality standards of biodiesel, a less polluting renewable energy source that has been gaining prominence year after year. The objective of the study was to quantify the weight proportion (%) that each biodiesel contributed to the composition of the blend, in addition to the presence of the adulterant soybean oil. Fourier-transform infrared spectroscopy combined with chemometric techniques allowed distinguishing between feedstocks of the animal and vegetable origin and adulterants and determining their ratios in 45 blends. Multivariate control charts based on the net analyte signal allowed obtaining most data on biodiesel from beef tallow and lard, whereas the interference chart was used for matrix variability. Partial least squares discriminant analysis (PLS-DA) was applied to the same dataset, and the coefficient of determination was 0.9999 for the calibration and prediction sets, proving to be a robust method for the identification of B100 biodiesel blends from different feedstocks and adulterants. PLS was used as a qualitative and quantitative tool, showing optimal correlation between calibration and prediction, with R cv 2 = 0.9999 and R p 2 = 0.9999, and low root-mean-square error of cross-validation and root-mean-square error of prediction values. Orthogonal signal correction was employed for data processing in the PLS-DA and PLS models, rendering them more cohesive and eliminating the variability of the dataset that is orthogonal to the parameter of interest. The full spectral range and only one latent variable were used. PLS-DA and PLS models proved to be 100% reliable for the identification and quantification of adulterants in biodiesel and biodiesel blends.

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“…To overcome this problem multivariate control charts have been used. [10][11][12] In the multivariate approach, two control charts, which are based on PCA are used. The use of PCA eliminates the correlation problems, because, by definition, the new variables used in the multivariate control charts are not correlated.…”

Section: Multivariate Control Chartsmentioning

confidence: 99%

Multivariate quality control of lubricating oils using Fourier transform infrared spectroscopy

Borin¹,

Poppi²

2004

J. Braz. Chem. Soc.

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Controle de qualidade multivariado, em conjunto com espectroscopia no infravermelho por transformada de Fourier (FTIR), foi usado para detectar qualitativamente o tipo e a condição de óleos lubrificantes. O procedimento multivariado baseou-se na análise de componentes principais (PCA), primeiro para classificar o tipo do lubrificante (mineral, sintético e semi-sintético) e depois para desenvolver duas cartas de controle: uma carta T 2 usando os componentes principais mais significativos e uma carta Q com os componentes principais não utilizados na primeira carta. A partir destas duas cartas, foi possível identificar amostras de óleo, baseado no parâmetro da viscosidade, que se encontravam fora do padrão normalmente presente em lubrificantes em condições de uso.Multivariate quality control in conjunction with Fourier transform infrared Spectroscopy (FTIR) were used to qualitatively detect the class and the condition of lubricating oils. The multivariate approach was based on principal component analysis (PCA), first to classify the lubricating oil type (mineral, synthetic and semi-synthetic) and then to develop two control charts: a T 2 chart using the most significant principal components and a Q chart with the PC not used in the first chart. From these two charts it was possible to identify oil samples, based on a viscosity parameter, which were out of the pattern normally present in utilizable lubricating oils.

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Multivariate Quality Control Applied to Detect the Soybean Oil Oxidation Using Fourier Transform Infrared Spectroscopy

Cited by 9 publications

References 8 publications

Liquid−Liquid Equilibrium Data for Systems Containing Vegetable Oils, Anhydrous Ethanol, and Hexane at (313.15, 318.15, and 328.15) K

Liquid−Liquid Equilibrium Data for Systems Containing Vegetable Oils, Anhydrous Ethanol, and Hexane at (313.15, 318.15, and 328.15) K

Characterization of Biodiesel from Animal Fat, Vegetable Oil, and Adulterants by Infrared Spectroscopy Combined with Chemometric Methods

Multivariate quality control of lubricating oils using Fourier transform infrared spectroscopy

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