Authentication of the geographical origin of virgin olive oils from the main worldwide producing countries: A new combination of HS-SPME-GC-MS analysis of volatile compounds and chemometrics applied to 1217 samples

Characterization and identification of the key volatile and non-volatile substances of Vangueria madagascariensis J.F. Gmel fruits (Kirkir) and exploration of their binding interactions with olfactory and taste receptors using computational chemistry methodology

Profiling of metabolites that contribute to the taste and odor of fruit products is important to produce the desired products. In this study, volatile and non-volatile compounds were analyzed using SPME/GC-MS and UHPLC-Q-Exactive-orbitrap-MS/MS, respectively. A total of 59 volatiles (including alcohols, aldehydes, acids, terpenes, ketones, phenols, and hydrocarbons et al.) and 18 non-volatiles (including phenolic acids, flavones, flavonoids, glucosides, phenols, and quinic acid derivatives et al.) were detected in dried Kirkir fruits. The binding interactions between the key volatiles and the detected non-volatiles with taste and olfactory receptors were also evaluated. Based on the molecular docking, 11 volatile compounds may contribute to the overall odor, while 16 non-volatile compounds may contribute to the taste of the Kirkir fruits. In conclusion, in silico studies can serve as a powerful technique for understanding mechanisms of interaction and predicting the key phytochemicals that contribute to the odor and taste of fruits.

Analytical insights for ensuring authenticity of Greek agriculture products: Unveiling chemical marker applications

Food authentication, including the differentiation of geographical or botanical origin, the method of production i.e. organic vs. conventional farming as well as the detection of food fraud/adulteration, has been a rapidly growing field over the past two decades due to increasing public awareness regarding food quality and safety, nutrition, and health. Concerned parties include consumers, producers, and legislators. Thus, the development of rapid, accurate, sensitive, and reproducible analytical methods to guarantee the authenticity of foods is of primary interest to scientists and technologists. The aim of the present article is to summarize research work carried out on the authentication of Greek agricultural products using spectroscopic (NIR, FTIR, UV-Vis, Raman and fluorescence spectroscopy, NMR, IRMS, ICP-OES, ICP-MS) and chromatographic (GC, GC/MS, HPLC, HPLC/MS, etc.) methods of analysis in combination with chemometrics highlighting the chemical markers that enable product authentication. The review identified a large number of chemical markers including volatiles, phenolic substances, natural pigments, elements, isotopes, etc. which can be used for (i) the differentiation of botanical/geographical origin; conventional from organic farming; production procedure and vintage year, etc. and (ii) detection of adulteration of high quality plant and animal origin foods with lower value substitutes. Finally, the constant development of reliable analytical techniques in combination with law enforcement authorities will ensure authentic foods in terms of quality and safety for consumers.

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost-profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and "volatilomics," "volatiles," "food authenticity," "adulteration," "origin," "fingerprint," "chemometrics," and variations thereof as keywords, without chronological restriction. One hundred thirty-seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

Combining different biomarkers to distinguish Chemlali virgin olive oils from different geographical areas of Tunisia

BACKGROUND

Despite their high potential, Tunisian virgin olive oils (VOOs) are mainly exported in bulk or blended with VOOs of other origin, hindering their international market placement. To face this situation, their valorization is needed by highlighting their unique features and by developing tools to guarantee their geographical authenticity. Compositional features of Chemlali VOOs produced in three Tunisian regions were assessed to identify suitable authenticity markers.

RESULTS

Quality indices ensured the quality of the VOOs studied. Volatile compounds, total phenols, fatty acid (FA) and chlorophylls are significantly influenced by the region of origin, which was justified by the differences found in soil and climatic conditions of the three geographical regions. To explore the capabilities of these markers for the geographical authentication of Tunisian Chemlali VOOs, classification models based on partial least squares-discriminant analysis (PLS-DA) were developed by grouping the minimum number of variables allowing the highest discrimination power, minimizing in this way the analytical procedure. The PLS-DA authentication model based on combining volatile compounds with FA or with total phenols achieved a correct classification of 95.7% of the VOOs according to their origin, as assessed by 10%-out cross-validation. Sidi Bouzid Chemlali VOOs achieved 100% of correct classification, while the misclassification between Sfax and Enfidha ones did not exceed 10%.

CONCLUSIONS

These results allowed to establish the most promising and affordable combination of markers for the geographical authentication of Tunisian Chemlali VOOs from distinct production regions and provide the basis to further develop authentication models based on wider datasets. © 2023 Society of Chemical Industry.

Discrimination between Korean and Chinese Kimchi using inductively coupled plasma-optical emission spectroscopy and mass spectrometry: A multivariate analysis of Kimchi

Kimchi has been designated as one of the world's five healthiest foods, and it is a traditional Korean fermented food. The purpose of this study was to investigate whether the origin of Kimchi can be discriminated against by using inorganic elements to develop a more accurate method. The OPLS-DA showed that the R² and Q² values were 0.908 and 0.81, a high-quality model. We selected 24 elements (¹³³Cs, ²³⁸U, ⁸⁸Sr, K, ¹⁵⁷Gd, Na, Mg, ¹³⁹La, P, ¹⁴¹Pr, ⁷²Ge, ¹⁴⁶Nd, ¹⁴⁷Sm, ¹⁵³Eu, ⁵⁵Mn, ¹⁶⁵Ho, ¹⁶³Dy, ¹⁶⁶Er, Fe, ¹⁷²Yb, ¹⁶⁹Tm, ¹⁸⁵Re, ¹⁷⁵Lu, and ¹¹⁸Sn) with VIP 1 or higher in the OPLS-DA model. In ROC, the selected elements had an accuracy of 100%. The heatmap confirmed the contents of rare earth elements (REEs) in Korean and Chinese Kimchi, the accuracy of distinguishing classification in CDA is 100%. Such results will help to distinguish the origin of agricultural products through inorganic analysis.

A comprehensive comparison of Chinese olive oils from different cultivars and geographical origins

China has become increasingly interested in producing olive oil in recent years. In this study, we determined the characteristics of virgin olive oil in five typical cultivars (cv. 'Arbequina,' 'Coratina,' 'Ezhi 8,' 'Frantoio,' and 'Koroneiki') from two of the most suitable production areas for olive cultivation in China. In addition, linear discriminant analysis (LDA) was used to differentiate oils originating from various cultivars and geographical origins. Heatmap was also constructed to describe the differences straightly. Compared to Xichang oils, Longnan oils generally contained higher levels of C18:0, lignans, total esters and total furans, and lower levels of phenolic acids and phenolic alcohols. A variety of cultivars differed in total sterols, hydroxytyrosol derivatives, and volatile compounds. Coratina oils showed excellent properties in two regions. Our findings are closely related to select the optimum olive cultivars in different regions to promote the development of Chinese olive industry scientifically.

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Varieties of raisins are diverse, and different varieties have different nutritional properties and commercial value. In this paper, we propose a method to identify different varieties of raisins by combining near-infrared (NIR) spectroscopy and machine learning algorithms. The direct averaging of the spectra taken for each sample may reduce the experimental data and affect the extraction of spectral features, thus limiting the classification results, due to the different substances of grape skins and flesh. Therefore, this experiment proposes a method to fuse the spectral features of pulp and peel. In this experiment, principal component analysis (PCA) was used to extract baseline corrected features, and linear models of k-nearest neighbor (KNN) and linear discriminant analysis (LDA) and nonlinear models of back propagation (BP), support vector machine with genetic algorithm (GA-SVM), grid search-support vector machine (GS-SVM) and particle swarm optimization with support vector machine (PSO- SVM) coupling were used to classify. This paper compared the results of four experiments using only skin spectrum, only flesh spectrum, average spectrum of skin and flesh, and their spectral feature fusion. The experimental results showed that the accuracy and Macro-F1 score after spectral feature fusion were higher than the other three experiments, and GS-SVM had the highest accuracy and Macro-F1 score of 94.44%. The results showed that feature fusion can improve the performance of both linear and nonlinear models. This may provide a new strategy for acquiring spectral data and improving model performance in the future. The code is available at https://github.com/L-ain/Source.

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).

Assessing food authenticity through protein and metabolic markers

This chapter aims to address an issue of ancient origins, but more and more topical in a globalized world in which consumers and stakeholders are increasingly aware: the authenticity of food. Foods are systems that can also be very complex, and verifying the correspondence between what is declared and the actual characteristics of the product is often a challenging issue. The complexity of the question we want to answer (is the food authentic?) means that the answer is equally articulated and makes use of many different analytical techniques. This chapter will consider the chemical analyses of foods aimed at guaranteeing their authenticity and will focus on frontier methods that have been developed in recent years to address the need to respond to ever-increasing guarantees of authenticity. Targeted and non-targeted approaches will be considered for verifying the authenticity of foods, through the study of different classes of constituents (proteins, metabolites, lipids, flavors). The numerous approaches available (proteomics, metabolomics, lipidomics) and the related analytical techniques (LC-MS, GC-MS, NMR) are first described from a more general point of view, after which their specific application for the purposes of authentication of food is addressed.

Geographical Origin Assessment of Extra Virgin Olive Oil via NMR and MS Combined with Chemometrics as Analytical Approaches

Geographical origin assessment of extra virgin olive oil (EVOO) is recognised worldwide as raising consumers’ awareness of product authenticity and the need to protect top-quality products. The need for geographical origin assessment is also related to mandatory legislation and/or the obligations of true labelling in some countries. Nevertheless, official methods for such specific authentication of EVOOs are still missing. Among the analytical techniques useful for certification of geographical origin, nuclear magnetic resonance (NMR) and mass spectroscopy (MS), combined with chemometrics, have been widely used. This review considers published works describing the use of these analytical methods, supported by statistical protocols such as multivariate analysis (MVA), for EVOO origin assessment. The research has shown that some specific countries, generally corresponding to the main worldwide producers, are more interested than others in origin assessment and certification. Some specific producers such as Italian EVOO producers may have been focused on this area because of consumers’ interest and/or intrinsic economical value, as testified also by the national concern on the topic. Both NMR- and MS-based approaches represent a mature field where a general validation method for EVOOs geographic origin assessment could be established as a reference recognised procedure.

Geographical origin discrimination of "Ntopia" olive oil cultivar from Ionian islands using volatile compounds analysis and computational statistics

The aim of the present study was to characterize the aroma profile of olive oil of the "Ntopia" (local) cultivar from the Ionian islands (Zakynthos, Kefalonia, Leukada, and Kerkyra) (Greece), and investigate whether specific volatile compounds could be considered as indicators of olive oil geographical origin, using computational statistics. In this context, 137 olive oil samples were subjected to headspace solid phase microextraction coupled to gas chromatography/mass spectrometry using the internal standard method. Computational statistics on the semi-quantitative data of olive oil samples, as rapid machine learning algorithms, showed that specific volatile compounds could be used as indicators of geographical origin of olive oil of the "Ntopia" cultivar, among the four main Ionian islands. Volatile compounds such as ethanol, pentanal, 2,4-dimethylheptane, 3,7-dimethyl-1,3,6-octatriene (E), 2,5-dimethylnonane, 1-hexanol, 6-methyl-5-hepten-2-one, octanal, dl-Limonene, acetic acid hexyl ester and dodecane could aid to the geographical origin discrimination of "Ntopia" olive oil cultivar when two (Zakynthos and Kefalonia) or four (Zakynthos, Kefalonia, Leukada and Kerkyra) Ionian islands are subjected to statistical analysis. The discrimination rate using the cross-validation method was 100% and 85.7%, respectively. These results were further evaluated using training and holdout partitions, during which a comparable classification rate was obtained.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00217-021-03863-2.

The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils: A systematic review (2013-2020)

The global market for imported, high-quality priced foods has grown dramatically in the last decade, as consumers become more conscious of food originating from around the world. Many countries require the origin label of food to protect consumers need about true characteristics and origin. Regulatory authorities are looking for an extended and updated list of the analytical techniques for verification of authentic oils and to support law implementation. This review aims to introduce the efforts made using various analytical tools in combination with the multivariate analysis for the verification of the geographical origin of oils. The popular analytical tools have been discussed, and scientometric assessment that underlines research trends in geographical authentication and preferred journals used for dissemination has been indicated. Overall, we believe this article will be a good guideline for food industries and food quality control authority to assist in the selection of appropriate methods to authenticate oils.

A Predictive Strategy Based on Volatile Profile and Chemometric Analysis for Traceability and Authenticity of Sugarcane Honey on the Global Market

Sugarcane honey (SCH) is a syrup produced on Madeira Island and recognized by its unique aroma, a complex attribute of quality with an important influence on the final consumer's acceptance of the product, and determined by a complex mixture of a large number of volatile organic compounds (VOCs) generated during its traditional making process and storage. Therefore, the purpose of this study was to establish the volatile profile of genuine SCH produced by a regional certified producer for seven years and compare it with syrups from non-certified regional producers and with producers from different geographical regions (Spain, Egypt, Brazil and Australia), as a powerful strategy to define the volatomic fingerprint of SCH. Different volatile profiles were recognized for all samples, with 166 VOCs being identified belonging to different chemical classes, including furans, ketones, carboxylic acids, aldehydes and alcohols. Chemometric analysis allowed (i) the differentiation between all syrups, being more pronounced between SCH and other syrups; and (ii) the identification of 32 VOCs as potential markers for the traceability and authenticity of SCH on the global market.

Metabolomics as a marketing tool for geographical indication products: a literature review

Geographical indication (GI) is used to identify a product's origin when its characteristics or quality are a result of geographical origin, which includes agricultural products and foodstuff. Metabolomics is an “omics” technique that can support product authentication by providing a chemical fingerprint of a biological system, such as plant and plant-derived products. The main purpose of this article is to verify possible contributions of metabolomic studies to the marketing field, mainly for certified regions, through an integrative review of the literature and maps produced by VOSviewer software. The results indicate that studies based on metabolomics approaches can relate specific food attributes to the region’s terroir and know-how. The evidence of this connection, marketing of GIs and metabolomics methods, is viewed as potential tool for marketing purposes (e.g., to assist communication of positive aspects and quality), and legal protection. In addition, our results provide a taxonomic categorization that can guide future marketing research involving metabolomics. Moreover, the results are also useful to government agencies to improve GIs registration systems and promotion strategies.

Amino Acid Profiling with Chemometric Analysis as a Feasible Tool for the Discrimination of Marine-Derived Peptide Powders

Marine-derived peptide powders have suffered from adulteration via the substitution of lower-price peptides or the addition of adulterants in the market. This study aims to establish an effective approach for the discrimination and detection of adulterants for four representative categories of marine-derived peptide powders, namely, oyster peptides, sea cucumber peptides, Antarctic krill peptides, and fish skin peptides, based on amino acid profiling alongside chemometric analysis. The principal component analysis and orthogonal partial least squares discriminant analysis results indicate that four categories of marine-derived peptides could be distinctly classified into four clusters and aggregated with the respective raw materials. Taurine, glycine, lysine, and protein contents were the major discriminants. A reliable classification model was constructed and validated by the prediction dataset, mixture sample dataset, and unclassified sample dataset with accuracy values of 100%, 100%, and 100%, respectively.

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.

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.

Determination of volatile compounds responsible for sensory characteristics from Brazilian extra virgin olive oil using HS-SPME/GC-MS direct method

Producing of extra virgin olive oils (EVOOs) containing pleasant sensory notes depends on fruits quality and production process and is strongly associated with their classification that is based on aroma and sensory taste. Consolidated as an efficient method, the direct headspace solid phase microextraction technique (HS-SPME) was utilized to characterize the volatile organic compounds (VOCs) profile, which contributes to the aroma of olive oils from southwestern (Serra da Mantiqueira region) and southern (Campanha Gaúcha region) Brazil. In this work, the relationship between the VOCs and sensory characteristics has been established; 19 EVOO samples (12 from Campanha Gaúcha and 7 from Serra da Mantiqueira) were studied. Indeed, the main volatile compounds were analyzed and grouped by their classification as well stood up with the trained sensorial panel perceptions. Relevant correlation between artichoke notes and ripe EVOO and between herbaceous notes and green EVOO was found. Additional correlations were observed for C5 and C6 VOCs with green and fruit/floral notes. The results denote the high quality among the samples and imply that besides the genetic factor, ripe or green classification influenced the volatile composition. PRACTICAL APPLICATION: As the Brazilian olive oil production is increasing, knowing about different sensory characteristics and its correlation with the volatile compounds of extra virgin olive oil represents a good tool to improve the quality. Moreover, the application of direct SPME method was possible evidence in the differentiation of ripe and green olive oils, beyond the production region and in consonance with its sensory notes and characteristics.

Rapid Analytical Method to Characterize the Freshness of Olive Oils Using Fluorescence Spectroscopy and Chemometric Algorithms

One of the most important issues in the field of quality assurance of olive oils is the detection of the freshness of olive oil. In this study, 400 nm laser-induced fluorescence spectroscopy was used with supervised and unsupervised multivariate analysis methods to develop a rapid method able to discriminate between freshly produced olive oils and oil that has been stored for a period of time ranging from 12 to 24 months. The fluorescence spectral data were firstly processed by the PCA. This method shows strong discrimination of the three oil classes using the first three components which present 96% of the total variability of the initial data, and then supervised classification models were constructed using the discriminant partial least square regression PLS-DA, support vector machine SVM, and linear discriminant analysis LDA. These methods show a high capacity in the classification of the three classes of olive oil. The validation of these classification models by external samples shows a high capacity of classification of the samples in their class with an accuracy of 100%. This study demonstrated the feasibility of the fluorescence spectroscopy fingerprint (routine technique) for the classification of olive oils according to their freshness and storage time.