Leveraging the performance of conventional spectroscopic techniques through data fusion approaches in high-quality edible oil adulteration analyses

MUCH. DIEGO; MIRTA R. ALCARAZ; CAMIÑA, JOSÉ MANUEL; GOICOECHEA, HÉCTOR C.; AZCARATE, SILVANA MARIELA

Talanta Open

Elsevier B.V.

Año: 2024 vol. 9

The high demand, high cost, and low regulations surrounding high-quality edible oils (HQEO) make them atarget for fraudulent actions, particularly adulteration with refined oils. Consequently, the authentication of thiskind of oil is of great interest. This work assessed the adulteration degree of five HQEOs: sesame, flaxseed, chia,rapeseed, and extra virgin olive oils, using different chemometric strategies to enhance the detection capability ofthe analytical methodology. Refined oils used as adulterants were evaluated at low concentrations (2–15 % v/v).Three multidimensional spectroscopic techniques (UV–Visible, near-infrared, and excitation-emission matrixfluorescence) were used, and two data fusion strategies (low- and mid-level) were evaluated. Principal component analysis was applied as an exploratory analysis tool to visualize and interpret the information contained inthe dataset. For the adulterant quantification, partial least squares regression analysis was used to build thesensitive predictive models. The results revealed that chemical information enhancement leverages the ability toattain reduced prediction compared to unidimensional signals. In scenarios with low sample variability, conventional unidimensional spectroscopy (UV–Visible or near-infrared) data was shown to be adequate to guarantee predictive efficiency. In contrast, when analysing predictive figures derived from models built using adataset with high variability, e.g., brands, low-level data fusion approaches enhance predictive efficiency. Theresults showed that excitation-emission matrix-based or low-level data fusion approaches can be accuratelyimplemented to guarantee the authenticity of edible oils even when a low content of adulterant oil is presented.