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In this paper (open access) two optical spectroscopic techniques,  Laser-Induced Breakdown Spectroscopy (LIBS) and UV-Vis-NIR absorption spectroscopy, are assessed for EVOO adulteration detection, using the same reference database of olive oil samples. In total, 184 samples were studied, including 40 EVOOs and 144 binary mixtures with pomace, soybean, corn, and sunflower oils, at various concentrations (ranging from 10 to 90% w/w). The reference class of “pure” EVOOs were limited to oils from a specific geographic region (either Crete, Lesvos, Kalamata or Achaia, with a different model built for each case).

The emission data from LIBS, related to the elemental composition of the samples, and the UV-Vis-NIR absorption spectra, related to the organic ingredients content, were analyzed, both separately and combined (i.e., fused), by Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs), and Logistic Regression (LR). In all cases, very highly predictive accuracies were achieved, attaining, in some cases, 100%.

The authors conclude that both techniques have the potential for efficient and accurate olive oil verification test protocols, with the LIBS technique being better suited as it can operate much faster.

The ISO 3632 UV–visible spectrophotometric method (using an aqueous extract) is the reference method for grading saffron.  It has been previously reported that this method is only able to detect adulteration  (safflower, turmeric or calendula) when the adulterant is greater than 50 % w/w.

In this study (purchase required) the authors reported that using acetonitrile, rather than water, as an extraction solvent gave far better discrimination. They analyzed 40 genuine and 123 adulterated saffron samples, each containing 5–10 % w/w contamination (41 samples for each type of adulterant). The resulting UV–visible spectra were processed using unsupervised multivariate statistical methods to distinguish between authentic and adulterated saffron. The Sequential Pre-processing through Orthogonalization (SPORT) algorithm, based on sequential and orthogonalized partial least squares (SO-PLS), was first applied to differentiate the two groups. Using a calibration set of 122 samples, the SPORT model correctly classified 37 of 38 external test samples, regardless of the type or level of contamination. Additionally, a class model for genuine saffron was developed using SIMCA (Soft Independent Modelling of Class Analogies), under the same calibration and validation conditions as the SPORT model. SIMCA accurately identified all test samples, with the exception of one pure saffron and one adulterated sample.