Quality and statistical classification of Brazilian vegetable oils using mid-infrared and Raman spectroscopy.
Appl Spectrosc
; 66(5): 552-65, 2012 May.
Article
in En
| MEDLINE
| ID: mdl-22524961
ABSTRACT
Palm oil, soy oil, sunflower oil, corn oil, castor oil, and rapeseed oil were analyzed with Fourier transform infrared (FT-IR) and FT-Raman spectroscopy. The quality of different oils was evaluated and statistically classified by principal component analysis (PCA) and a partial least squares (PLS) regression model. First, a calibration set of spectra was selected from one sampling batch. The qualitative variations in spectra are discussed with a prediction of oil composition (saturated, mono- and polyunsaturated fatty acids) from mid-infrared analysis and iodine value from FT-Raman analysis, based on ratioing the intensity of bands at given wavenumbers. A more robust and convincing oil classification is obtained from two-parameter statistical models. The statistical analysis of FT-Raman spectra favorably distinguishes according to the iodine value, while the mid-infrared spectra are most sensitive to hydroxyl moieties. Second, the models are validated with a set of spectra from another sampling batch, including the same oil types as-received and after different aging times together with a hydrogenated castor oil and high-oleic sunflower oil. There is very good agreement between the model predictions and the Raman measurements, but the statistical significance is lower for mid-infrared spectra. In the future, this calibration model will be used to check vegetable oil qualities before using them in polymerization processes.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Spectrophotometry, Infrared
/
Spectrum Analysis, Raman
/
Plant Oils
Type of study:
Prognostic_studies
/
Qualitative_research
/
Risk_factors_studies
Country/Region as subject:
America do sul
/
Brasil
Language:
En
Journal:
Appl Spectrosc
Year:
2012
Document type:
Article
Affiliation country:
Germany