کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5768425 1413223 2017 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV-Vis spectroscopies: A preliminary approach
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک دانش تغذیه
پیش نمایش صفحه اول مقاله
Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV-Vis spectroscopies: A preliminary approach
چکیده انگلیسی


- Fluorescence and UV-Vis spectroscopies properly determined the soybean oil content.
- Multivariate methods had different results for UV-Vis spectroscopy.
- Both spectroscopy techniques had similar predictive abilities.

This work presents a comparative study of chemometric methods used to quantify adulteration of extra virgin olive oil (EVOO) with soybean edible oil using fluorescence and UV-Vis spectroscopies. The adulteration 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 predictive ability 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 Error of Prediction (RMSEP) values varying from 14.0 to 17.5 g/kg. When the regression methods were evaluated for UV-Vis spectra, higher RMSEP values were found, varying from 13.3 to 30.4 g/kg. The results indicate that the two spectroscopic techniques have similar performances with respect to predictive ability of the regression models.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: LWT - Food Science and Technology - Volume 85, Part A, November 2017, Pages 9-15
نویسندگان
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