Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics

Vacuum-assisted and multi-cumulative trapping in headspace solid-phase microextraction combined with comprehensive multidimensional chromatography-mass spectrometry for profiling virgin olive oil aroma

In the present work vacuum (Vac) and multiple cumulative trapping (MCT) headspace solid phase microextraction (HS-SPME) were evaluated as alternative or combined techniques for the volatile profiling. A higher extraction performance for semi-volatiles was shown by all three techniques. Synergic combination of Vac and MCT showed up to 5-times extraction power for less volatile compounds. The hyphenation of said techniques with comprehensive two-dimensional gas chromatography (GC × GC) enabled a comprehensive analysis of the volatilome. Firstly, 18 targeted quality markers, previously defined by means of classical HS-SPME, were explored for their ability to classify commercial categories. The applicability of such markers proved to be limited with the alternative sampling techniques. An untargeted approach enables the selection of specific features for each technique showing a better classification capacity of the commercial categories. No misclassifications were observed, except for one extra virgin olive oil classified as virgin olive oil in 3 × 10 min Vac-MCT-HS-SPME.

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.

Exploring multiple-cumulative trapping solid-phase microextraction coupled to gas chromatography-mass spectrometry for quality and authenticity assessment of olive oil

This study explores the potential of an innovative multi-cumulative trapping headspace solid-phase microextraction approach coupled with untargeted data analysis to enhance the information provided by aroma profiling of virgin olive oil. Sixty-nine samples of different olive oil commercial categories (extra-virgin, virgin and lampante oil) and different geographical origins were analysed using this novel workflow. The results from each sample were aligned and compared using for the first time a tile-based approach to enable the mining of all of the raw data within the chemometrics platform without any pre-processing methods. The data matrix obtained allowed the extraction of multiple-level information from the volatile profile of the samples. Not only was it possible to classify the samples within the commercial category that they belonged to, but the same data also provided interesting information regarding the geographical origin of the extra-virgin olive oil.

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).

E-Nose and Olfactory Assessment: Teamwork or a Challenge to the Last Data? The Case of Virgin Olive Oil Stability and Shelf Life

Electronic nose (E-nose) devices represent one of the most trailblazing innovations in current technological research, since mimicking the functioning of the biological sense of smell has always represented a fascinating challenge for technological development applied to life sciences and beyond. Sensor array tools are right now used in a plethora of applications, including, but not limited to, (bio-)medical, environmental, and food industry related. In particular, the food industry has seen a significant rise in the application of technological tools for determining the quality of edibles, progressively replacing human panelists, therefore changing the whole quality control chain in the field. To this end, the present review, conducted on PubMed, Science Direct and Web of Science, screening papers published between January 2010 and May 2021, sought to investigate the current trends in the usage of human panels and sensorized tools (E-nose and similar) in the food industry, comparing the performances between the two different approaches. In particular, the focus was mainly addressed towards the stability and shelf life assessment of olive oil, the main constituent of the renowned “Mediterranean diet”, and nowadays appreciated in cuisines from all around the world. The obtained results demonstrate that, despite the satisfying performances of both approaches, the best strategy merges the potentialities of human sensory panels and technological sensor arrays, (i.e., E-nose somewhat supported by E-tongue and/or E-eye). The current investigation can be used as a reference for future guidance towards the choice between human panelists and sensorized tools, to the benefit of food manufacturers.

Chromatographic Fingerprinting Enables Effective Discrimination and Identitation of High-Quality Italian Extra-Virgin Olive Oils

The challenging process of high-quality food authentication takes advantage of highly informative chromatographic fingerprinting and its identitation potential. In this study, the unique chemical traits of the complex volatile fraction of extra-virgin olive oils from Italian production are captured by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and explored by pattern recognition algorithms. The consistent realignment of untargeted and targeted features of over 73 samples, including oils obtained by different olive cultivars (n = 24), harvest years (n = 3), and processing technologies, provides a solid foundation for sample identification and discrimination based on production region (n = 6). Through a dedicated multivariate statistics workflow, identitation is achieved by two-level partial least-square (PLS) regression, which highlights region diagnostic patterns accounting between 58 and 82 of untargeted and targeted compounds, while sample classification is performed by sequential application of soft independent modeling for class analogy (SIMCA) models, one for each production region. Samples are correctly classified in five of the six single-class models, and quality parameters [i.e., sensitivity, specificity, precision, efficiency, and area under the receiver operating characteristic curve (AUC)] are equal to 1.00.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Delineating the extra-virgin olive oil aroma blueprint by multiple headspace solid phase microextraction and differential-flow modulated comprehensive two-dimensional gas chromatography

Comprehensive two-dimensional gas chromatography with parallel mass spectrometry and flame ionization detection (GC × GC-MS/FID) enables effective chromatographic fingerprinting of complex samples by comprehensively mapping untargeted and targeted components. Moreover, the complementary characteristics of MS and FID open the possibility of performing multi-target quantitative profiling with great accuracy. If this synergy is applied to the complex volatile fraction of food, sample preparation is crucial and requires appropriate methodologies capable of providing true quantitative results. In this study, untargeted/targeted (UT) fingerprinting of extra-virgin olive oil volatile fractions is combined with accurate quantitative profiling by multiple headspace solid phase microextraction (MHS-SPME). External calibration on fifteen pre-selected analytes and FID predicted relative response factors (RRFs) enable the accurate quantification of forty-two analytes in total, including key-aroma compounds, potent odorants, and olive oil geographical markers. Results confirm good performances of comprehensive UT fingerprinting in developing classification models for geographical origin discrimination, while quantification by MHS-SPME provides accurate results and guarantees data referability and results transferability over years. Moreover, by this approach the extent of internal standardization procedure inaccuracy, largely adopted in food volatiles profiling, is measured. Internal standardization yielded an average relative error of 208 % for the fifteen calibrated compounds, with an overestimation of + 538% for (E)-2-hexenal, the most abundant yet informative volatile of olive oil, and a -89% and -80% for (E)-2-octenal and (E)-2-nonenal respectively, analytes with a lower HS distribution constant. Compared to existing methods based on 1D-GC, the current procedure offers better separation power and chromatographic resolution that greatly improve method specificity and selectivity and results in lower LODs and LOQs, high calibration performances (i.e., R² and residual distribution), and wider linear range of responses. As an artificial intelligence smelling machine, the MHS-SPME-GC × GC-MS/FID method is here adopted to delineate extra-virgin olive oil aroma blueprints; an objective tool with great flexibility and reliability that can improve the quality and information power of each analytical run.

Chromatographic Fingerprinting Strategy to Delineate Chemical Patterns Correlated to Coffee Odor and Taste Attributes

Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavor quality and therefore requires complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routine quality control laboratories. Coffee flavor chemical data from 155 samples were obtained by analyzing volatile (headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS)) and nonvolatile (liquid chromatography-ultraviolet/diode array detector (LC-UV/DAD)) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scores, and investigate the networks between features. A comparison of the Q model parameter and root-mean-squared error prediction (RMSEP) highlights the variable influence that the nonvolatile fraction has on prediction, showing that it has a higher impact on describing acid, bitter, and woody notes than on flowery and fruity. The data fusion emphasized the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and nonvolatile data deserve a thorough investigation to verify the potential of odor-taste integration.

Virgin Olive Oil Volatile Compounds: Composition, Sensory Characteristics, Analytical Approaches, Quality Control, and Authentication

Volatile organic compounds strongly contribute to both the positive and negative sensory attributes of virgin olive oil, and more and more studies have been published in recent years focusing on several aspects regarding these molecules. This Review is aimed at giving an overview on the state of the art about the virgin olive oil volatile compounds. Particular emphasis was given to the composition of the volatile fraction, the analytical issues and approaches for analysis, the sensory characteristics and interaction with phenolic compounds, and the approaches for supporting the Panel Test in virgin olive oil classification and in authentication of the botanical and geographic origin based on volatile compounds. A pair of detailed tables with a total of approximately 700 volatiles identified or tentatively identified to date and tables dealing with analytical procedures, sensory characteristics of volatiles, and specific chemometric approaches for quality assessment are also provided.