Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

A multilevel LC-HRMS and NMR correlation workflow towards foodomics advancement: Application in table olives

Foodomics employs advanced analytical techniques to provide answers regarding food composition, authenticity control, marker identification and issues related to food quality and safety. Nuclear magnetic resonance (NMR) spectroscopy and chromatography hyphenated to mass spectrometry (MS) are the main analytical platforms used in this field. Nevertheless, they are rarely employed in an integrated manner, and even then, the contribution of each technique remains vague. Table olives (Olea europaea L.) are a food commodity of high economic and nutritional value with an increasing production tendency over the last two decades, which, however, suffers from extensive fraud incidents and quality determination uncertainties. Thus, the current attempt aims towards two axes with the first being the multilevel integration of LC-HRMS and NMR data of the same samples and table olives being the selected matrix. In more detail, UPLC-HRMS/MS-based analysis was compared at different stages within an untargeted metabolomics workflow with an NMR-based study and the complementarity of the two platforms was evaluated. Furthermore, statistical heterospectroscopy (SHY), rarely employed in foodomics, combining the spectroscopic with spectrometric datasets and aiming to increase the confidence level of annotated biomarkers was applied. Amongst these lines, the second parallel axis of this study was the detailed characterization of table olives' metabolome in search for quality markers considering the impact of geographical (from Northern to Southern Greece) and botanical origin (Kalamon, Konservolia, Chalkidikis cultivars), as well as processing parameters (Spanish, Greek). To that end, using deep dereplication tools including statistical methods, with SHY employed for the first time in table olives, different biomarkers, belonging to the classes of phenyl alcohols, phenylpropanoids, flavonoids, secoiridoids and triterpenoids were identified as responsible for the observed classifications. The current binary pipeline, focusing on biomarkers' identification confidence, could be suggested as a meaningful workflow not only in olive-based products, but also in food quality control and foodomics in general.

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.

Effect of Growing Regions on Discrimination of Turkish-Style Black Table Olives from Gemlik Cultivar

Gemlik is a cultivar that grows in a distinct region of Turkiye and is ideal for brine fermentation of brine black table olives. Bursa Protected Designated Origin (PDO) and Izmir non-PDO Gemlik table olives have high levels of oleic acid (74%), total phenol (190 mg/kg), and dry matter (57%), while being low in linoleic acid (8%). The pH values and salt contents were observed to be in the range of 4.1 to 4.3 and 3.9% to 4.8%, respectively. During the fermentation of Gemlik table olives, a mass transfer occurred, resulting in a reduction in reducing sugar and total sugar contents as well as an increase in the salt content of the olives. Despite the reduction of phenolic content in both Gemlik PDO and non-PDO table olives, their antioxidant capacity remains high after fermentation. The oil content, antioxidant activity, phenolic contents, palmitic, palmitoleic, oleic, and linoleic acids were all found to be significant variables in distinguishing between Gemlik PDO and non-PDO table olives using PLS-DA analysis. There is a statistically significant correlation between the phenolic content and oleic (0.588) and linoleic (-0.659) acids (p < 0.05). Bursa PDO and Izmir non-PDO exhibit enhanced nutritional quality and antioxidant activity, unequivocally differentiating them from Hatay and Mersin non-PDO Gemlik table olives with 98% accuracy through discriminant analysis (p < 0.05). PLS-DA and DA can effectively identify variations in the quality of Turkish-style black table olives preserved in brine, originating from PDO and non-PDO growing areas.

The Rising Role of Omics and Meta-Omics in Table Olive Research

Table olives are often the result of fermentation, a process where microorganisms transform raw materials into the final product. The microbial community can significantly impact the organoleptic characteristics and safety of table olives, and it is influenced by various factors, including the processing methods. Traditional culture-dependent techniques capture only a fraction of table olives' intricate microbiota, prompting a shift toward culture-independent methods to address this knowledge gap. This review explores recent advances in table olive research through omics and meta-omics approaches. Genomic analysis of microorganisms isolated from table olives has revealed multiple genes linked to technological and probiotic attributes. An increasing number of studies concern metagenomics and metabolomics analyses of table olives. The former offers comprehensive insights into microbial diversity and function, while the latter identifies aroma and flavor determinants. Although proteomics and transcriptomics studies remain limited in the field, they have the potential to reveal deeper layers of table olives' microbiome composition and functionality. Despite the challenges associated with implementing multi-omics approaches, such as the reliance on advanced bioinformatics tools and computational resources, they hold the promise of groundbreaking advances in table olive processing technology.

Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil

Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.

From sample preparation to NMR-based metabolic profiling in food commodities: The case of table olives

INTRODUCTION

Nuclear magnetic resonance (NMR)-based metabolic profiling has been widely used in food and plant sciences. Despite its simplicity and inherent reproducibility, the determination of the appropriate pre-processing procedures greatly affects the obtained metabolic profile.

OBJECTIVES

The current study represents a detailed guide of use for untargeted NMR-based metabolic profiling of table olives (Olea europaea L.).

METHODS

Greek Kalamon table olives from different geographical origins were selected as reference materials. Differently treated samples were extracted using different solvents and/or solvent systems. Chemical profiles were evaluated with high-performance thin layer chromatography (HPTLC). Different deuterated solvents and sample concentrations were evaluated for the recording of optimal quality spectra.

RESULTS

The methanol extract of freeze-dried table olives was found to contain the most representative secondary metabolites, in higher concentrations, as well. The optimal deuterated solvent for the NMR analysis was methanol-d₄ , while final sample concentration should be within the range of 10 to 15 mg/mL. Multivariate data analysis was also used to estimate and confirm the variation and clustering caused by different characteristics of the samples.

CONCLUSIONS

Results of the present study make evident the necessity for thorough planning and method development prior to any extensive metabolomic study based on NMR spectroscopy. Pre-processing and sample preparation stages seemed to greatly affect the metabolic profile and spectral quality in the case of table olives, which by extrapolation could apply to other food commodities. Nevertheless, the nature of the samples must be fully described in general, in order to proceed to solid conclusions.

Chemical characterisation of Pelargonium sidoides root based on LC-QToF-MS non-target screening strategies

INTRODUCTION

Pelargonium sidoides is a member of the Geraniaceae family and it originates from the coastal regions of South Africa. In the last decades, Pelargonium sidoides root has been subjected to several surveys due to the assertion of its health benefits, such as the relief of symptoms of acute bronchitis, common cold and acute rhinosinusitis. Many studies have been conducted to reveal its naturally occurring bioactive chemicals, yet no wide-scope chemical characterisation strategies have been done using mass spectrometry.

OBJECTIVE

This research aimed to comprehensively characterise the chemical profile of Pelargonium sidoides root via high-resolution mass spectrometry.

METHODOLOGY

The Pelargonium sidoides root was extracted by a mixture of methanol: water in the proportion of 80:20. The extraction procedure included vortexing, shaking as well as the use of an ultrasound sonication bath under 40°C. After centrifugation, the supernatant was evaporated to dryness. The dry residue was reconstituted with a mixture of methanol/water (50:50, v/v), filtered and injected into an ultra-high-pressure liquid chromatography-quadruple time-of-flight mass spectrometer.

RESULTS

Overall, 33 compounds were identified in the root using suspect and non-target screening. These compounds were originated from different classes of compounds such as amino acids, phenolic acids, α-hydroxy-acids, vitamins, polyphenols, flavonoids, coumarins, coumarins glucosides, coumarin sulphates and nucleotides. Quantitative results were provided for the identified compounds, where their reference standards were available.

CONCLUSION

Some important compounds were elucidated, belonging to different classes of compounds such as antioxidants (coumarins and phenolic compounds), amino acids, nucleotides and vitamins revealing the importance of the bioactive content of this root.

Characterization of the Phenolic Fingerprint of Kolovi Extra Virgin Olive Oils from Lesvos with Regard to Altitude and Farming System Analyzed by UHPLC-QTOF-MS

Extra virgin olive oil (EVOO) is recognized for its nutritional virtues and the beneficial health effects deriving from its hydrophilic fraction (phenolic acids, phenolic alcohols, flavonoids, and secoiridoids). The phenolic compounds of EVOOs possess multiple biological properties such as antioxidant, antimicrobial, anticarcinogenic, and anti-inflammatory properties, among others. Considering that EVOOs produced in Greece are recognized as high-quality products due to their rich phenolic content, it is imperative to characterize Greek monovarietal EVOOs and ensure that their uniqueness is closely linked to their botanical and territorial origin. In this work, an ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method combined with target and suspect screening was used to characterize monovarietal EVOOs of the Kolovi variety from Lesvos, and thereby establish their phenolic fingerprint. Overall, 25 phenols were determined, and the total quantification and semi-quantification results ranged between 251 and 1230 mg/kg, highlighting the high phenolic content of the Kolovi variety from the island of Lesvos in the North Aegean.

Geographical Characterization of Olive Oils from the North Aegean Region Based on the Analysis of Biophenols with UHPLC-QTOF-MS

Olive oil is famous due to the nutritional properties and beneficial health effects. The exceptional properties of virgin (VOO) and extra virgin olive oil (EVOO) are credited to the bioactive constituents of their polar fraction, the phenolic compounds. The concentration and composition of biophenols can be influenced by the geographical origin, the cultivar, as well as several agronomic and technological parameters. In this study, an ultra-high-performance liquid chromatography coupled to quadrupole-time of flight tandem mass spectrometry (UHPLC-QTOF-MS) method was used to determine biophenols in Greek EVOOs from five islands originating from the North Aegean Region (Chios, Fournoi, Ikaria, Lesvos, and Samos) through target and suspect screening. In total, 14 suspect and 5 target compounds were determined in the analyzed EVOOs. The quantitative and semiquantitative results were compared to investigate discriminations between different regions. Significant differences were found between the islands based on the overall phenolic content and the concentration levels of individual compounds, as well. In the case of Lesvos, the territory was separated in subdivisions (zones), and each zone was studied individually.

Authentication and Chemometric Discrimination of Six Greek PDO Table Olive Varieties through Morphological Characteristics of Their Stones

Table olives, the number one consumed fermented food in Europe, are widely consumed as they contain many valuable ingredients for health. It is also a food which may be the subject of adulteration, as many different olive varieties with different geographical origin, exist all over the word. In the present study, the image analysis of stones of six main Greek protected designation of origin (PDO) table olive varieties was performed for the control of their authentication and discrimination, with cv. Prasines Chalkidikis, cv. Kalamata Olive, cv. Konservolia Stylidas, cv. Konservolia Amfissis, cv. Throuba Thassos and cv. Throuba Chios being the studied olive varieties. Orthogonal partial least square discriminant analysis (OPLS-DA) was used for discrimination and classification of the six Greek table olive varieties. With a 98.33% of varietal discrimination, the OPLS-DA model proved to be an efficient tool to authentify table olive varieties from their morphological characteristics.

Development of a Wine Metabolomics Approach for the Authenticity Assessment of Selected Greek Red Wines

Wine metabolomics constitutes a powerful discipline towards wine authenticity assessment through the simultaneous exploration of multiple classes of compounds in the wine matrix. Over the last decades, wines from autochthonous Greek grape varieties have become increasingly popular among wine connoisseurs, attracting great interest for their authentication and chemical characterization. In this work, 46 red wine samples from Agiorgitiko and Xinomavro grape varieties were collected from wineries in two important winemaking regions of Greece during two consecutive vintages and analyzed using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QToF-MS). A targeted metabolomics methodology was developed, including the determination and quantification of 28 phenolic compounds from different classes (hydroxycinnamic acids, hydroxybenzoic acids, stilbenes and flavonoids). Moreover, 86 compounds were detected and tentatively identified via a robust suspect screening workflow using an in-house database of 420 wine related compounds. Supervised chemometric techniques were employed to build an accurate and robust model to discriminate between two varieties.

Comparison of MPL-ANN and PLS-DA models for predicting the severity of patients with acute pancreatitis: An exploratory study

OBJECTIVE

Acute pancreatitis (AP) is a common inflammatory disorder that may develop into severe AP (SAP), resulting in life-threatening complications and even death. The purpose of this study was to explore two different machine learning models of multilayer perception-artificial neural network (MPL-ANN) and partial least squares-discrimination (PLS-DA) to diagnose and predict AP patients' severity.

METHODS

The MPL-ANN and PLS-DA models were established using candidate markers from 15 blood routine parameters and five serum biochemical indexes of 133 mild acute pancreatitis (MAP) patients, 167 SAP (including 88 moderately SAP) patients, and 69 healthy controls (HCs). The independent parameters and combined model's diagnostic efficiency in AP severity differentiation were analyzed using the area under the receiver operating characteristic curve (AUC).

RESULTS

The neutrophil to lymphocyte ratio (NLR) is the most useful marker in 20 parameters for screening AP patients [AUC = 0.990, 95% confidence interval (CI): 0.984-0.997, sensitivity 94.3%, specificity 98.6%]. The MPL-ANN model based on six optimal parameters exhibited better diagnostic and predict performance (AUC = 0.984, 95% CI: 0.960-1.00, sensitivity 92.7%, specificity 93.3%, accuracy 93.0%) than the PLS-DA model based on five optimal parameters (AUC = 0.912, 95% CI: 0.853-0.971, sensitivity 87.8%, specificity 84.4%, accuracy 84.8%) in discriminating MAP patients from SAP patients.

CONCLUSION

The results demonstrated that the MPL-ANN model based on routine blood and serum biochemical indexes provides a reliable and straightforward daily clinical practice tool to predict AP patients' severity.

FoodOmicsGR_RI. A Consortium for Comprehensive Molecular Characterisation of Food Products

The national infrastructure FoodOmicsGR_RI coordinates research efforts from eight Greek Universities and Research Centers in a network aiming to support research and development (R&D) in the agri-food sector. The goals of FoodOmicsGR_RI are the comprehensive in-depth characterization of foods using cutting-edge omics technologies and the support of dietary/nutrition studies. The network combines strong omics expertise with expert field/application scientists (food/nutrition sciences, plant protection/plant growth, animal husbandry, apiculture and 10 other fields). Human resources involve more than 60 staff scientists and more than 30 recruits. State-of-the-art technologies and instrumentation is available for the comprehensive mapping of the food composition and available genetic resources, the assessment of the distinct value of foods, and the effect of nutritional intervention on the metabolic profile of biological samples of consumers and animal models. The consortium has the know-how and expertise that covers the breadth of the Greek agri-food sector. Metabolomics teams have developed and implemented a variety of methods for profiling and quantitative analysis. The implementation plan includes the following research axes: development of a detailed database of Greek food constituents; exploitation of "omics" technologies to assess domestic agricultural biodiversity aiding authenticity-traceability control/certification of geographical/genetic origin; highlighting unique characteristics of Greek products with an emphasis on quality, sustainability and food safety; assessment of diet's effect on health and well-being; creating added value from agri-food waste. FoodOmicsGR_RI develops new tools to evaluate the nutritional value of Greek foods, study the role of traditional foods and Greek functional foods in the prevention of chronic diseases and support health claims of Greek traditional products. FoodOmicsGR_RI provides access to state-of-the-art facilities, unique, well-characterised sample sets, obtained from precision/experimental farming/breeding (milk, honey, meat, olive oil and so forth) along with more than 20 complementary scientific disciplines. FoodOmicsGR_RI is open for collaboration with national and international stakeholders.

Target and Suspect HRMS Metabolomics for the Determination of Functional Ingredients in 13 Varieties of Olive Leaves and Drupes from Greece

The huge interest in the health-related properties of foods to improve health has brought about the development of sensitive analytical methods for the characterization of natural products with functional ingredients. Greek olive leaves and drupes constitute a valuable source of biophenols with functional properties. A novel ultra-high-performance liquid chromatography-quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to identify biophenols through target and suspect screening in Greek olive leaves and drupes of the varieties: Koroneiki, Throumbolia, Konservolia, Koutsourelia, Kalamon, Petrolia, Amigdalolia, Megaritiki, Mastoeidis, Agouromanakolia, Agrilia, Adramitiani and Kolovi. The method's performance was evaluated using the target compounds: oleuropein, tyrosol and hydroxytyrosol. The analytes demonstrated satisfactory recovery efficiency for both leaves (85.9-90.5%) and drupes (89.7-92.5%). Limits of detection (LODs) were relatively low over the range 0.038 (oleuropein)-0.046 (hydroxytyrosol) and 0.037 (oleuropein)-0.048 (hydroxytyrosol) for leaves and drupes, respectively For leaves, the precision limit ranged between 4.7 and 5.8% for intra-day and between 5.8 and 6.5% for inter-day experiments, and for drupes, it ranged between 3.8 and 5.2% for intra-day and between 5.1 and 6.2% for inter-day experiments, establishing the good precision of the method. The regression coefficient (r²) was above 0.99 in all cases. Furthermore, the preparation of herbal tea from olive leaves is suggested after investigating the optimum infusion time of dried leaves in boiling water. Overall, 10 target and 36 suspect compounds were determined in leaves, while seven targets and thirty-three suspects were identified in drupes, respectively.