Fourier-transform infrared spectroscopy (FTIR), followed by multivariate treatment of spectral data, was used to evaluate the oxidised fatty acid (OFA) concentration in virgin olive oil samples characterised by different oxidative status. The entire FTIR spectra (4000–700 cmÿ1) of oils were divided in 25 wavelength regions. The normalised absorbances of the peak areas within these regions were used as predictors. In order to predict the OFA concentration, multiple linear regression (MLR) models were performed. After a cube root transformation of data, an MLR model constructed using eight predictors was able to predict OFA concentration with an average error of 17%. The main wavelength regions selected to construct this MLR model corresponded to =C–H (trans and cis, stretching), –C–H (CH2, stretching asym), O–H (bending in plane), C–O (stretching), –H–C=C–H– (cis?) and =CH2 (wagging), due to the fact that these regions were those more affected by oxidation. This FTIR method is an extremely quick and simple procedure for OFA determination which can be easily automatised.
Lerma-García M.J., Simó-Alfonso E.F., Bendini A., Cerretani L. (2011). Rapid evaluation of oxidised fatty acid concentration in virgin olive oil using Fourier-transform infrared spectroscopy and multiple linear regression. FOOD CHEMISTRY, 124, 679-684 [10.1016/j.foodchem.2010.06.054].
Rapid evaluation of oxidised fatty acid concentration in virgin olive oil using Fourier-transform infrared spectroscopy and multiple linear regression
BENDINI, ALESSANDRA;CERRETANI, LORENZO
2011
Abstract
Fourier-transform infrared spectroscopy (FTIR), followed by multivariate treatment of spectral data, was used to evaluate the oxidised fatty acid (OFA) concentration in virgin olive oil samples characterised by different oxidative status. The entire FTIR spectra (4000–700 cmÿ1) of oils were divided in 25 wavelength regions. The normalised absorbances of the peak areas within these regions were used as predictors. In order to predict the OFA concentration, multiple linear regression (MLR) models were performed. After a cube root transformation of data, an MLR model constructed using eight predictors was able to predict OFA concentration with an average error of 17%. The main wavelength regions selected to construct this MLR model corresponded to =C–H (trans and cis, stretching), –C–H (CH2, stretching asym), O–H (bending in plane), C–O (stretching), –H–C=C–H– (cis?) and =CH2 (wagging), due to the fact that these regions were those more affected by oxidation. This FTIR method is an extremely quick and simple procedure for OFA determination which can be easily automatised.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
