Use of portable Raman spectroscopy in the quality control of extra virgin olive oil and adulterated compound oils

The practice of defrauding food and other consumer goods is growing in recent years, and products with high added value are the main targets of this practice, such as extra virgin olive oil (EVOO). In this paper, we evaluated eight samples of EVOO and four samples of compound oils (blends of soybean oil with EVOO) of different commercial brands. For this, we used the portable Raman spectroscopy in the region of 800 to 1800 cm(-1), aiming to identify the adulteration of EVOO using soybean oil. Analytical curves were constructed with different concentrations, ranging from 0 to 100 wt% of vegetable oil in EVOO to build regression models. They were developed by chemometric tools, as partial least squares (PLS) and partial least squares by intervals (iPLS). Besides, a univariate model was elaborated based on the ratio of the bands 1440 cm(-1) (CH2) and 1655 cm(-1) (CC=). These models were applied to predict the concentration of adulterant in twelve commercial samples. The iPLS method presented the best analytical results, with R(2)p of the 0.9967 and RMSEP of 0.0198 wt%, in addition to the limits of detection and quantification of 0.9731 and 3.2436 wt%, respectively. Among the commercial samples evaluated, seven presented higher soybean oil concentrations than the manufacturer's label, thus characterizing the adulteration of the product. Principal component analysis (PCA) was also used to qualitatively evaluate the degree of similarity between commercial EVOO samples, blends, and vegetable oils. The combination of the portable analytical technique with the chemometric methods allowed the identification and quantification, in loco, of adulterants present in commercial EVOO samples.

Automated summary

This study used portable Raman spectroscopy and chemometric methods to successfully identify and quantify adulteration in commercial EVOO samples, with the iPLS method showing the best performance. This provides an effective approach for detecting food fraud.