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

• Published in: Sensors (Basel, Switzerland) Vol. 13; no. 4; pp. 4258 - 4271
• Main Authors: Mueller, Daniela, Ferrão, Marco Flôres, Marder, Luciano, da Costa, Adilson Ben, Schneider, Rosana de Cássia de Souza
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28-03-2013; Molecular Diversity Preservation International (MDPI)
• Subjects: Agribusiness; biodiesel; Biodiesel fuels; Biofuels; Biofuels - analysis; Cluster Analysis; Cotton; Economic development; Engines; Family farms; Farmers; Fourier transforms; HCA; Indexing in process; iPCA; Multivariate Analysis; Oils & fats; Oilseeds; Plant Oils - analysis; Principal Component Analysis; Principal components analysis; quality contro; Quality control; raw material; Raw materials; Reference Standards; Sensors; SIMCA; Soybeans; Spectroscopy, Fourier Transform Infrared - methods; UATR sensor; Vegetable oils; Brazil
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s130404258

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.