MULTIVARIATE MODELING FOR DETECTING ADULTERATION OF EXTRA VIRGIN OLIVE OIL WITH SOYBEAN OIL USING FLUORESCENCE AND UV-VIS SPECTROSCOPIES: A PRELIMINARY APPROACH

MATIAS INSAUSTI; MATIAS INSAUSTI; DANIELLE SILVA NASCIMENTO; DANIELLE SILVA NASCIMENTO; FERNÁNDEZ BAND BEATRIZ; FERNÁNDEZ BAND BEATRIZ

LEBENSMITTEL-WISSENSCHAFT UND-TECHNOLOGIE-FOOD SCIENCE AND TECHNOLOGY

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2017 p. 9 - 15

0023-6438

virgin olive oil (EVOO) with soybean edible oil using fluorescence and UVeVis spectroscopies. Theadulteration was prepared by adding soybean edible oil in different concentrations (10, 50, 100, 150, 200,250 and 300 g/kg). Different multivariate regression strategies were evaluated: partial least squares (PLS)using full spectrum; PLS with significant regression coefficients selected by the Jack-Knife algorithm(PLS-JK) and multiple linear regression (MLR) with previous selection of variables by stepwise algorithms(SW-MLR); successive projections algorithm (SPA-MLR); and genetic algorithm (GA-MLR). The predictiveability of the models was assessed, for each spectroscopic technique. For fluorescence spectroscopy,satisfactory prediction results were obtained for all the regression models with Root Mean Square Errorof Prediction (RMSEP) values varying from 14.0 to 17.5 g/kg. When the regression methods were evaluatedfor UVeVis spectra, higher RMSEP values were found, varying from 13.3 to 30.4 g/kg. The resultsindicate that the two spectroscopic techniques have similar performances with respect to predictiveability of the regression model