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Fourier transform infrared spectroscopy (FTIR) and multivariate analysis for identification of different vegetable oils used in biodiesel production.

Mueller D, Ferrão MF, Marder L, da Costa AB, Schneider Rde C - Sensors (Basel) (2013)

Bottom Line: For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used.The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis.It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples.

View Article: PubMed Central - PubMed

Affiliation: Programa de Pós-Graduação em Sistemas e Processos Industriais, Universidade de Santa Cruz do Sul, Av. Independência, 2293, CEP 96815-900, Santa Cruz do Sul-RS, Brasil. danielamueller@hotmail.com

ABSTRACT
The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources--canola, cotton, corn, palm, sunflower and soybeans--were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples.

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Dendrogram for the UATR-FTIR spectra of biodiesel and degummed oil samples.
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f4-sensors-13-04258: Dendrogram for the UATR-FTIR spectra of biodiesel and degummed oil samples.

Mentions: Although the samples of degummed oils and the samples of biodiesel are on opposite sides in Figure 3, it is clear that the vegetable oil source exerts an influence on the PC2 of these samples, for example, by observing the samples of biodiesel and the samples of degummed palm and cotton oils, it is ascertained that they are located approximately at the same height of the PC2 axis, though on opposite sides. The same thing also occurs with the other samples. The trends observed through analyses of the principal components were confirmed through the dendrogram obtained by HCA (Figure 4).


Fourier transform infrared spectroscopy (FTIR) and multivariate analysis for identification of different vegetable oils used in biodiesel production.

Mueller D, Ferrão MF, Marder L, da Costa AB, Schneider Rde C - Sensors (Basel) (2013)

Dendrogram for the UATR-FTIR spectra of biodiesel and degummed oil samples.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3673082&req=5

f4-sensors-13-04258: Dendrogram for the UATR-FTIR spectra of biodiesel and degummed oil samples.
Mentions: Although the samples of degummed oils and the samples of biodiesel are on opposite sides in Figure 3, it is clear that the vegetable oil source exerts an influence on the PC2 of these samples, for example, by observing the samples of biodiesel and the samples of degummed palm and cotton oils, it is ascertained that they are located approximately at the same height of the PC2 axis, though on opposite sides. The same thing also occurs with the other samples. The trends observed through analyses of the principal components were confirmed through the dendrogram obtained by HCA (Figure 4).

Bottom Line: For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used.The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis.It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples.

View Article: PubMed Central - PubMed

Affiliation: Programa de Pós-Graduação em Sistemas e Processos Industriais, Universidade de Santa Cruz do Sul, Av. Independência, 2293, CEP 96815-900, Santa Cruz do Sul-RS, Brasil. danielamueller@hotmail.com

ABSTRACT
The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources--canola, cotton, corn, palm, sunflower and soybeans--were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples.

Show MeSH
Related in: MedlinePlus