Using fluorescence excitation-emission matrices to predict bitterness and pungency of virgin olive oil: A feasibility study

Unlike other food products, virgin olive oil must undergo an organoleptic assessment that is currently based on a trained human panel, which presents drawbacks that might affect the efficiency and robustness. Therefore, disposing of instrumental methods that could serve as screening tools to support sensory panels is of paramount importance. The present work aimed to explore excitation-emission fluorescence spectroscopy (EEFS) to predict bitterness and pungency, since both attributes are related with fluorophore compounds, such as polar phenols. Bitterness and pungency intensities of 250 samples were provided by an official sensory panel and used to build and compare partial least squares regressions (PLSR) with the excitation-emission matrix. Both PARAFAC scores and two-way unfolded data led to successful PLSR. The most relevant PARAFAC scores agreed with virgin olive oil phenolic spectra, evidencing that EEFS would be the fit-for-purpose screening tool to support the sensory panel

Geographical authentication of virgin olive oil by GC–MS sesquiterpene hydrocarbon fingerprint: Verifying EU and single country label-declaration

According to the last report from the European Union (EU) Food Fraud Network, olive oil tops the list of the most notified products. Current EU regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking. Verifying the compliance of label-declared EU oils should be addressed with the highest priority level. Hence, the present work tackles this issue by developing a classification model (PLS-DA) based on the sesquiterpene hydrocarbon fingerprint of 400 samples obtained by HS-SPME-GC–MS to discriminate between EU and non-EU olive oils, obtaining an 89.6% of correct classification for the external validation (three iterations), with a sensitivity of 0.81 and a specificity of 0.95. Subsequently, multi-class discrimination models for EU and non-EU countries were developed and externally validated (with three different validation sets) with successful results (average of 92.2% of correct classification for EU and 96.0% for non-EU countries)

Large-scale evaluation of shotgun triacylglycerol profiling for the fast detection of olive oil adulteration

Fast and effective analytical screening tools providing new suitable authenticity markers and applicable to a large number of samples are required to efficiently control the global olive oil (OO) production, and allow the rapid detection of low levels of adulterants even with fatty acid composition similar to OO. The present study aims to develop authentication models for the comprehensive detection of illegal blends of OO with adulterants including different types of high linoleic (HL) and high oleic (HO) vegetable oils at low concentrations (2–10%) based on shotgun triacylglycerol (TAG) profile obtained by Flow Injection Analysis-Heated Electrospray Ionisation-High Resolution Mass Spectrometry (FIA-HESI-HRMS) at a large-scale experimental design. The sample set covers a large natural variability of both OO and adulterants, resulting in more than one thousand samples analysed. A combined PLS-DA binary modelling based on shotgun TAG profiling proved to be a fit for purpose screening tool in terms of efficiency and applicability. The external validation resulted in the correct classification of the 86.8% of the adulterated samples (diagnostic sensitivity = 0.87), and the 81.1% of the genuine samples (diagnostic specificity = 0.81), with an 85.1% overall correct classification (efficiency = 0.85)

Stepwise strategy based on 1H-NMR fingerprinting in combination with chemometrics to determine the content of vegetable oils in olive oil mixtures

1H NMR fingerprinting of edible oils and a set of multivariate classification and regression models organised in a decision tree is proposed as a stepwise strategy to assure the authenticity and traceability of olive oils and their declared blends with other vegetable oils (VOs). Oils of the ‘virgin olive oil’ and ‘olive oil’ categories and their mixtures with the most common VOs, i.e. sunflower, high oleic sunflower, hazelnut, avocado, soybean, corn, refined palm olein and desterolized high oleic sunflower oils, were studied. Partial least squares (PLS) discriminant analysis provided stable and robust binary classification models to identify the olive oil type and the VO in the blend. PLS regression afforded models with excellent precisions and acceptable accuracies to determine the percentage of VO in the mixture. The satisfactory performance of this approach, tested with blind samples, confirm its potential to support regulations and control bodies

Prospective exploration of hazelnut’s unsaponifiable fraction for geographical and varietal authentication: A comparative study of advanced fingerprinting and untargeted profiling techniques

This study compares two data processing techniques (fingerprinting and untargeted profiling) to authenticate hazelnut cultivar and provenance based on its unsaponifiable fraction by GC–MS. PLS-DA classification models were developed on a selected sample set (n = 176). As test cases, cultivar models were developed for “Tonda di Giffoni” vs other cultivars, whereas provenance models were developed for three origins (Chile, Italy or Spain). Both fingerprinting and untargeted profiling successfully classified hazelnuts by cultivar or provenance, revealing the potential of the unsaponifiable fraction. External validation provided over 90 % correct classification, with fingerprinting slightly outperforming. Analysing PLS-DA models’ regression coefficients and tentatively identifying compounds corresponding to highly relevant variables showed consistent agreement in key discriminant compounds across both approaches. However, fingerprinting in selected ion mode extracted slightly more information from chromatographic data, including minor discriminant species. Conversely, untargeted profiling acquired in full scan mode, provided pure spectra, facilitating chemical interpretability.This work was developed in the context of the project TRACENUTS, PID2020-117701RB100 financed by MCIN/AEI/https://doi.org/10.130 39/501100011033. B. Torres-Cobos thanks the Spanish Ministry of Universities predoctoral fellowships FPU20/014540. B. QuintanillaCasas thanks the Fundacion ´ Alfonso Martín Escudero for the research grant for universities and centers abroad 2022. A. Tres received a Ramon y Cajal grant (RYC-2017-23601) funded by MCIN/AEI/https://doi. org/10.13039/501100011033 and by “ESF Investing in your future”. INSA-UB Maria de Maeztu Unit of Excellence (Grant CEX2021- 001234-M) funded by MICIN/AEI/FEDER, UE. The authors would like to express their gratitude to Ferrero Hazelnut Company and Tuscia University (Department of Agriculture and Forest Science) for providing the hazelnut samples from Chile and Italy, respectively.info:eu-repo/semantics/publishedVersio