Abstract
AbstractA methodology based on Fourier transform infrared spectroscopy with attenuated total reflectance sampling technique, combined with multivariate analysis, was developed to monitor adulteration of extra virgin olive oil (EVOO) with peanut oil (PEO). Principal components regression (PCR), partial least squares regression (PLS-R), and linear discriminant analysis (LDA) allowed quantification of percentage of adulteration based on spectral data of 192 samples. Wavenumbers associated with the biochemical differences among several types of edible oils were investigated by principal component analysis. Two sets of frequencies were selected in order to establish a robust regression model. Set A consisted on the frequency regions from 600 to 1,800 cm−1 and from 2,750 to 3,050 cm−1. Set B comprised 17 discrete peak absorbance frequencies for which the communality value was higher than 0.6. Analysis of an external set of 25 samples allowed the validation and evaluation of the predictability of the models. Whe...
Highlights
Extra virgin olive oil (EVOO) is a vegetable edible oil made from healthy and intact fruits of the olive tree (Olea europaea L.) only by mechanical means and can be directly consumed unrefined
Small differences in relative intensity values were exploited in the classification and quantification of extra virgin olive oil (EVOO) adulterated with peanut oil (PEO), as discussed below
In this work, we presented an exploratory study of the applicability of Fourier transform infrared (FTIR)-attenuated total reflectance (ATR) spectroscopy in the prediction of the adulteration level of EVOO with PEO
Summary
Extra virgin olive oil (EVOO) is a vegetable edible oil made from healthy and intact fruits of the olive tree (Olea europaea L.) only by mechanical means (crushing, malaxation, and centrifugation) and can be directly consumed unrefined. The detection of adulteration of food products is important for consumers, industries, and retailers. Analysis of the quality and purity of edible oils in general and of EVOO, in particular, is of great relevance and has been the subject of research of several authors (Ben-Ayed, Kamoun-Grati, & Rebai, 2013). Adulteration of pure expensive edible oils either with low-priced oils, with degraded used oils or with toxic mineral oils can have great economic and social impact, and is a serious public health problem (Johnson, 2014). The improvement of fast and non-expensive analytical methodologies having the ability of detecting such adulterations in EVOO is a current topic of research
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