info:eu-repo/semantics/article
Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil
Date
2011-08Registration in:
Cerretani, Lorenzo; Maggio, Ruben Mariano; Barnaba, Carlo; Gallina Toschi, Tullia; Chiavaro, Emma; Application of partial least square regression to differential scanning calorimetry data for fatty acid quantitation in olive oil; Elsevier; Food Chemistry; 127; 4; 8-2011; 1899-1904
0308-8146
CONICET Digital
CONICET
Author
Cerretani, Lorenzo
Maggio, Ruben Mariano
Barnaba, Carlo
Gallina Toschi, Tullia
Chiavaro, Emma
Abstract
A chemometric approach based on partial least (PLS) square methodology was applied to unfolded differential scanning calorimetry data obtained by 63 samples of different vegetable oils (58 extra virgin olive oils, one olive and one pomace olive oil, three seed oils) to evaluate fatty acid composition (palmitic, stearic, oleic and linoleic acids, saturated (SFA), mono (MUFA) and polysaturated (PUFA) percentages, oleic/linoleic and unsaturated/saturated ratios). All calibration models exhibited satisfactory figures of merit. Palmitic and oleic acids, as well as SFA showed very good correlation coefficients and low root mean square error values in both calibration and validation sets. Satisfactory results were also obtained for MUFA, PUFA, stearic and linoleic acids, O/L ratio in terms of percentage recoveries and relative standard deviations. No systematic and bias errors were detected in the prediction of validation samples. This novel approach could provide statistically similar results to those given by traditional official procedures, with the advantages of a very rapid and environmentally friendly methodology.