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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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SIMCA Model—Distance between the classes for the training biodiesel samples (Class II versus Class IV).
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f8-sensors-13-04258: SIMCA Model—Distance between the classes for the training biodiesel samples (Class II versus Class IV).

Mentions: Table 3 presents a summary of the SIMCA model obtained. Figure 8 presents the Coomans diagram which features the orthogonal distances of the biodiesel utilized for the training set. It is observed that Class II and Class IV samples classify correctly into their respective classes.


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)

SIMCA Model—Distance between the classes for the training biodiesel samples (Class II versus Class IV).
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-13-04258: SIMCA Model—Distance between the classes for the training biodiesel samples (Class II versus Class IV).
Mentions: Table 3 presents a summary of the SIMCA model obtained. Figure 8 presents the Coomans diagram which features the orthogonal distances of the biodiesel utilized for the training set. It is observed that Class II and Class IV samples classify correctly into their respective classes.

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