Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

Sensors

Daniela Mueller²  Marco Flôres Ferrão¹  Luciano Marder²  Adilson Ben da Costa² 

[1] Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Av. Bento Gonçalves, 9500, CEP 91501-970, Porto Alegre–RS, Brasil;Programa de Pós-Graduação em Sistemas e Processos Industriais, Universidade de Santa Cruz do Sul (UNISC), Av. Independência, 2293, CEP 96815-900, Santa Cruz do Sul–RS, Brasil; E-Mails:

关键词: HCA;    iPCA;    SIMCA;    UATR sensor;    biodiesel;    raw material;    quality control;   

DOI  :  10.3390/s130404258

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.