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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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Related in: MedlinePlus

Percent variance to the UATR-FTIR derivate spectra data divided into 16 equidistant intervals.
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f5-sensors-13-04258: Percent variance to the UATR-FTIR derivate spectra data divided into 16 equidistant intervals.

Mentions: The best results at the iPCA were achieved with the two first principal components (PC1 versus PC2) and splitting the spectrum into 16 equidistant intervals. Figure 5 features the percentage variance chart for every region studied. In this chart, for each interval, that is to say, for each region of the spectrum, variance is calculated, in percentage terms, for each principal component. It should be added that the bars present in percentage form (height of the bars) the variance contained in each main component for each interval. In this figure, interval 14 accumulates 99.54% of information in the first two principal components for the UATR-FTIR spectra data.


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)

Percent variance to the UATR-FTIR derivate spectra data divided into 16 equidistant intervals.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-13-04258: Percent variance to the UATR-FTIR derivate spectra data divided into 16 equidistant intervals.
Mentions: The best results at the iPCA were achieved with the two first principal components (PC1 versus PC2) and splitting the spectrum into 16 equidistant intervals. Figure 5 features the percentage variance chart for every region studied. In this chart, for each interval, that is to say, for each region of the spectrum, variance is calculated, in percentage terms, for each principal component. It should be added that the bars present in percentage form (height of the bars) the variance contained in each main component for each interval. In this figure, interval 14 accumulates 99.54% of information in the first two principal components for the UATR-FTIR spectra data.

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