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Fourier transform infrared spectroscopy (FTIR) and multivariative analysis for identification of defferent vegetable oils used in biodiesel production

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Fourier transform infrared spectroscopy (FTIR) and multivariative analysis for identification of defferent vegetable oils used in biodiesel production

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Título Fourier transform infrared spectroscopy (FTIR) and multivariative analysis for identification of defferent vegetable oils used in biodiesel production
Autor Mueller, Daniela
Ferrão, Marco Flôres
Marder, Luciano
Costa, Adilson Ben da
Schneider, Rosana de Cassia de Souza
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.
Contido em Sensors [recurso eletrônico]. Basel. Vol. 13, no. 4 (Apr. 2013), p. 4258-4271
Assunto Análise multivariada
Biodiesel
Espectroscopia : Infravermelho : Transformada de fourier
Oleos vegetais
[en] HCA
[en] iPCA
[en] Quality control
[en] Raw material
[en] SIMCA
[en] UATR sensor
Origem Estrangeiro
Tipo Artigo de periódico
URI http://hdl.handle.net/10183/83595
Arquivos Descrição Formato
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