Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication.

Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia and Vietnam Robusta-Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.

Data Fusion Approaches for the Characterization of Musts and Wines Based on Biogenic Amine and Elemental Composition.

Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.

Bioactive Amines in Wines. The Assessment of Quality Descriptors by Flow Injection Analysis with Tandem Mass Spectrometry.

Biogenic amines (BAs) occur in a wide variety of foodstuffs, mainly from the decomposition of proteins by the action of microorganisms. They are involved in several cellular functions but may become toxic when ingested in high amounts through the diet. In the case of oenological products, BAs are already present in low concentrations in must, and their levels rise dramatically during the fermentation processes. This paper proposes a rapid method for the determination of BAs in wines and related samples based on precolumn derivatization with dansyl chloride and further detection by flow injection analysis with tandem mass spectrometry. Some remarkable analytes such as putrescine, ethanolamine, histamine, and tyramine have been quantified in the samples. Concentrations obtained have shown interesting patterns, pointing out the role of BAs as quality descriptors. Furthermore, it has been found that the BA content also depends on the vinification practices, with malolactic fermentation being a significant step in the formation of BAs. From the point of view of health, concentrations found in the samples are, in general, below 10 mg L-1, so the consumption of these products does not represent any special concern. In conclusion, the proposed method results in a suitable approach for a fast screening of this family of bioactive compounds in wines to evaluate quality and health issues.

Characterization and Classification of Spanish Honey by Non-Targeted LC-HRMS (Orbitrap) Fingerprinting and Multivariate Chemometric Methods.

A non-targeted LC-HRMS fingerprinting methodology based on a C18 reversed-phase mode under universal gradient elution using an Orbitrap mass analyzer was developed to characterize and classify Spanish honey samples. A simple sample treatment consisting of honey dissolution with water and a 1:1 dilution with methanol was proposed. A total of 136 honey samples belonging to different blossom and honeydew honeys from different botanical varieties produced in different Spanish geographical regions were analyzed. The obtained LC-HRMS fingerprints were employed as sample chemical descriptors for honey pattern recognition by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The results demonstrated a superior honey classification and discrimination capability with respect to previous non-targeted HPLC-UV fingerprinting approaches, with them being able to discriminate and authenticate the honey samples according to their botanical origins. Overall, noteworthy cross-validation multiclass predictions were accomplished with sensitivity and specificity values higher than 96.2%, except for orange/lemon blossom (BL) and rosemary (RO) blossom-honeys. The proposed methodology was also able to classify and authenticate the climatic geographical production region of the analyzed honey samples, with cross-validation sensitivity and specificity values higher than 87.1% and classification errors below 10.5%.

Off-Line SPE LC-LRMS Polyphenolic Fingerprinting and Chemometrics to Classify and Authenticate Spanish Honey.

The feasibility of non-targeted off-line SPE LC-LRMS polyphenolic fingerprints to address the classification and authentication of Spanish honey samples based on both botanical origin (blossom and honeydew honeys) and geographical production region was evaluated. With this aim, 136 honey samples belonging to different botanical varieties (multifloral and monofloral) obtained from different Spanish geographical regions with specific climatic conditions were analyzed. Polyphenolic compounds were extracted by off-line solid-phase extraction (SPE) using HLB (3 mL, 60 mg) cartridges. The obtained extracts were then analyzed by C18 reversed-phase LC coupled to low-resolution mass spectrometry in a hybrid quadrupole-linear ion trap mass analyzer and using electrospray in negative ionization mode. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were employed to assess the pattern recognition capabilities of the obtained fingerprints to address honey classification and authentication. In general, a good sample discrimination was accomplished by PLS-DA, being able to differentiate both blossom-honey and honeydew-honey samples according to botanical varieties. Multiclass predictions by cross-validation for the set of blossom-honey samples showed sensitivity, specificity, and classification ratios higher than 60%, 85%, and 87%, respectively. Better results were obtained for the set of honeydew-honey samples, exhibiting 100% sensitivity, specificity, and classification ratio values. The proposed fingerprints also demonstrated that they were good honey chemical descriptors to deal with climatic and geographical issues. Characteristic polyphenols of each botanical variety were tentatively identified by LC-MS/MS in multiple-reaction monitoring mode to propose possible honey markers for future experiments (i.e., naringin for orange/lemon blossom honeys, syringic acid in thyme honeys, or galangin in rosemary honeys).

High-performance liquid chromatography with fluorescence detection fingerprints as chemical descriptors to authenticate the origin, variety and roasting degree of coffee by multivariate chemometric methods.

BACKGROUND: Coffee is one of the most popular beverages around the world, consumed as an infusion of ground roasting coffee beans with a characteristic taste and flavor. Two main varieties, Arabica and Robusta, are produced worldwide. Furthermore, interest of consumers in quality attributes related to coffee production region and varieties is increasing. Thus, it is necessary to encourage the development of simple methodologies to authenticate and guarantee the coffee origin, variety and roasting degree, aiming to prevent fraudulent practices. RESULTS: C18 high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprints obtained after brewing coffees without any sample treatment other than filtration (i.e. considerably reducing sample manipulation) were employed as sample chemical descriptors for subsequent coffee characterization and classification by principal component analysis (PCA) and partial least squares regression-discriminant analysis (PLS-DA). PLS-DA showed good classification capabilities regarding coffee origin, variety and roasting degree when employing HPLC-FLD fingerprints, although overlapping occurred for some sample groups. However, the discrimination power increased when selecting HPLC-FLD fingerprinting segments richer in discriminant features, which were deduced from PLS-DA loading plots. In this case, excellent separation was observed and 100% classification rates for both PLS-DA calibrations and predictions were obtained (all samples were correctly classified within their corresponding groups). CONCLUSION: HPLC-FLD fingerprinting segments were3 found to be suitable chemical descriptors for discriminating the origin (country of production), variety (Arabica and Robusta) and roasting degree of coffee. Therefore, HPLC-FLD fingerprinting can be proposed as a feasible, simple and cheap methodology to address coffee authentication, especially for developing coffee production countries. © 2020 Society of Chemical Industry.

Targeted UHPLC-HRMS (Orbitrap) Polyphenolic and Capsaicinoid Profiling for the Chemometric Characterization and Classification of Paprika with Protected Designation of Origin (PDO) Attributes.

Society's interest in the quality of food products with certain attributes has increased, the attribute of a Protected Designation of Origin (PDO) being an effective tool to guarantee the quality and geographical origin of a given food product. In Spain, two paprika production areas with PDO (La Vera and Murcia) are recognized. In the present work, targeted UHPLC-HRMS polyphenolic and capsaicinoid profiling through the TraceFinderTM screening software, using homemade accurate mass databases, was proposed as a source of chemical descriptors, to address the characterization, classification, and authentication of paprika. A total of 126 paprika samples from different production regions-Spain (La Vera PDO and Murcia PDO) and the Czech Republic, each including different flavor varieties, were analyzed. UHPLC-HRMS polyphenolic profiles showed to be good chemical descriptors to achieve paprika classification and authentication, based on the production region, through principal component analysis and partial least squares regression-discriminant analysis, with classification rates of 82%, 86%, and 100% for La Vera PDO, Murcia PDO, and the Czech Republic, respectively. In addition, a perfect classification was also accomplished among the flavor varieties for the Murcia PDO and Czech Republic samples. By employing the UHPLC-HRMS polyphenolic and capsaicinoid profiles as chemical descriptors, acceptable discrimination among La Vera PDO flavor varieties was also achieved.

Characterization, Classification and Authentication of Turmeric and Curry Samples by Targeted LC-HRMS Polyphenolic and Curcuminoid Profiling and Chemometrics.

The importance of monitoring bioactive substances as food features to address sample classification and authentication is increasing. In this work, targeted liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) polyphenolic and curcuminoid profiles were evaluated as chemical descriptors to deal with the characterization and classification of turmeric and curry samples. The profiles corresponding to bioactive substances were obtained by TraceFinderTM software using accurate mass databases with 53 and 24 polyphenolic and curcuminoid related compounds, respectively. For that purpose, 21 turmeric and 9 curry samples commercially available were analyzed in triplicate by a simple liquid-solid extraction procedure using dimethyl sulfoxide as extracting solvent. The obtained results demonstrate that the proposed profiles were excellent chemical descriptors for sample characterization and classification by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), achieving 100% classification rates. Curcuminoids and some specific phenolic acids such as trans-cinnamic, ferulic and sinapic acids, helped on the discrimination of turmeric samples; polyphenols, in general, were responsible for the curry sample distinction. Besides, the combination of both polyphenolic and curcuminoid profiles was necessary for the simultaneous characterization and classification of turmeric and curry samples. Discrimination among turmeric species such as Curcuma longa vs. Curcuma zedoaria, as well as among different Curcuma longa varieties (Alleppey, Madras and Erode) was also accomplished.

Non-Targeted HPLC-UV Fingerprinting as Chemical Descriptors for the Classification and Authentication of Nuts by Multivariate Chemometric Methods.

Recently, the authenticity of food products has become a great social concern. Considering the complexity of the food chain and that many players are involved between production and consumption; food adulteration practices are rising as it is easy to conduct fraud without being detected. This is the case for nut fruit processed products, such as almond flours, that can be adulterated with cheaper nuts (hazelnuts or peanuts), giving rise to not only economic fraud but also important effects on human health. Non-targeted HPLC-UV chromatographic fingerprints were evaluated as chemical descriptors to achieve nut sample characterization and classification using multivariate chemometric methods. Nut samples were extracted by sonication and centrifugation, and defatted with hexane; extracting procedure and conditions were optimized to maximize the generation of enough discriminant features. The obtained HPLC-UV chromatographic fingerprints were then analyzed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to carry out the classification of nut samples. The proposed methodology allowed the classification of samples not only according to the type of nut but also based on the nut thermal treatment employed (natural, fried or toasted products).

Modified distribution in the polyphenolic profile of rosemary leaves induced by plant inoculation with an arbuscular mycorrhizal fungus.

BACKGROUND: Rosemary forms an arbuscular mycorrhizal (AM) symbiosis with a group of soilborne fungi belonging to the phylum Glomeromycota, which can modify the plant metabolome responsible for the antioxidant capacity and other health beneficial properties of rosemary. RESULTS: The effect of inoculating rosemary plants with an AM fungus on their growth via their polyphenolic fingerprinting was evaluated after analyzing leaf extracts from non-inoculated and inoculated rosemary plants by ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Plant growth parameters indicated that mycorrhizal inoculation significantly increased plant height and biomass. Chemical modifications in the plant polyphenolic profile distribution were found after a principal components analysis (PCA) loading plots study. Four compounds hosting strong antioxidant properties - ferulic acid, asiatic acid, carnosol, and vanillin - were related to mycorrhizal rosemary plants while caffeic and chlorogenic acids had a higher influence on non-mycorrhizal plants. CONCLUSION: Mycorrhization was found to stimulate growth to obtain a higher biomass of plant leaves in a short time, avoiding chemical fertilization, while analytical results demonstrate that there is an alteration in the distribution of polyphenols in plants colonized by the symbiotic fungus, which can be related to an improvement in nutritional properties with future industrial significance. © 2018 Society of Chemical Industry.

Voltammetric and electrogeneration approaches for the assessment of the oxidative drug metabolism.

In this paper, electrochemical (EC) methods have been proposed to evaluate the oxidative behavior of drugs as rapid, simple, and cheap strategies to predict some metabolic features. Various commercial drugs belonging to different therapeutic families have been assayed to deal with a wide variety of biotransformations and to cover different metabolism extents. First, differential pulse voltammetry has been applied to evaluate the oxidative behavior of drugs. Voltammetric assays have demonstrated to be highly efficient to predict the metabolism extent from the current intensity data. The second objective of this work has been the comparison of metabolite profiles from both EC and in vitro methods based on liver microsome assays. The resulting samples have been analyzed by reversed-phase liquid chromatography mode using a core-shell column and UV detection. Chromatographic methods have been established for each particular drug and its metabolites using 0.1% (v/v) formic acid aqueous solution and methanol (MeOH) as the components of the mobile phase. Drug oxidation products from both EC- and microsome-based methodologies have been compared in terms of variety and percentage from the corresponding chromatographic profiles. In general, most of the metabolites occurring in vitro have also been reproduced in the EC runs. Besides, it has been found that compositional profiles from EC experiments are dependent on experimental variables such as pH and potential. In general, acid (pH 2) and basic (pH 10) conditions and too high potentials can contribute to the generation of oxidation artifacts which differ from metabolites while milder potentials and neutral pH values may reproduce more accurately the microsome patterns. The proposed methodology is suitable for a first study of the oxidative behavior of molecules that can be related to relevant metabolic properties. The obtained information could be of great interest to prioritize or discard compounds, as a first screening, on the research of drug candidates.

Liquid chromatography-mass spectrometry as a general approach for investigating covalent binding of drugs to DNA.

This paper aims at developing a general strategy to study the detection of adducts of drugs with DNA. In particular, ethacrynic acid has been chosen as a model reactive drug that could be able to bind covalently to DNA bases. Such interactions were detected by ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Principal component analysis (PCA) was applied as an unsupervised method to try to find the potential candidate adduct from MS features. The occurrence of adducts was investigated preliminarily using deoxynucleosides of the guanine, cytosine, adenine, and thymine separately as a way to optimize both separation and detection conditions. Interpretations of MS and MS/MS spectra provided tentative structures of the compounds formed. Conclusions extracted from such simple nucleoside models were further extended to the analysis of DNA adducts. For such a purpose, DNA was incubated in the presence of ethacrynic acid under appropriate experimental conditions and its further enzymatic hydrolysis released the corresponding nucleosides. UHPLC-MS analysis of the resulting test samples under the SRM detection mode confirmed the presence of ethacrynic acid derivatives of nucleosides occurring at very low concentration levels, thus proving the overall performance of the method. Graphical Abstract General approach for investigating drug-DNA adduct formation.

Determination of polyphenolic profiles by liquid chromatography-electrospray-tandem mass spectrometry for the authentication of fruit extracts.

Liquid chromatography-electrospray-tandem mass spectrometry (LC-ESI-MS/MS) was applied to the analysis and authentication of fruit-based products and fruit-based pharmaceutical preparations. A Kinetex C18 reversed-phase column under gradient elution with 0.1 % formic acid aqueous solution and methanol mobile phases was used for the simultaneous determination of 26 polyphenols, allowing an acceptable separation in less than 22 min. Instrumental quality parameters such as limits of detection (LOD, values between 12 and 14 µg/L for 19 of the 26 analyzed polyphenols), linearity (r (2) > 0.991), run-to-run and day-to-day precisions (relative standard deviation (RSD) values lower than 9.9 and 13.5 %, respectively), and accuracy (relative errors lower than 8 %) were established. A simple extraction method, consisting of a sample sonication with acetone/water/hydrochloric acid (70:29.9:0.1 v/v/v) and centrifugation, was proposed. Two calibration procedures, external calibration using standards prepared in water and standard addition, were evaluated for polyphenol quantification in several grape and cranberry fruits and processed fruit products. For a 95 % confidence level, no statistical differences were observed between the two calibration methods (p values between 0.06 and 0.95), denoting that external calibration was suitable enough for the quantitative analysis of polyphenols in fruit-based products. The proposed LC-ESI-MS/MS method was then applied to the analysis of polyphenols in 23 grape-based and cranberry-based natural products and pharmaceutical preparations. Polyphenolic concentration data was then analyzed by principal component analysis (PCA) to extract information of the most significant profile data contributing to authentication of natural extracts according to their fruit of origin.

Microwave-assisted extraction with natural deep eutectic solvents for polyphenol recovery from agrifood waste: Mature for scaling-up?

Agrifood industries generate large amounts of waste that may result in remarkable environmental problems, such as soil and water contamination. Therefore, proper waste management and treatment have become an environmental, economic, and social challenge. Most of these wastes are exceptionally rich in bioactive compounds (e.g., polyphenols) with potential applications in the food, cosmetic, and pharmaceutical industries. Indeed, the recovery of polyphenols from agrifood waste is an example of circular bioeconomy, which contributes to the valorization of waste while providing solutions to environmental problems. In this context, unconventional extraction techniques at the industrial scale, such as microwave-assisted extraction (MAE), which has demonstrated its efficacy at the laboratory level for analytical purposes, have been suggested to search for more efficient recovery procedures. On the other hand, natural deep eutectic solvents (NADES) have been proposed as an efficient and green alternative to typical extraction solvents. This review aims to provide comprehensive insights regarding the extraction of phenolic compounds from agrifood waste. Specifically, it focuses on the utilization of MAE in conjunction with NADES. Moreover, this review delves into the possibilities of recycling and reusing NADES for a more sustainable and cost-efficient industrial application. The results obtained with the MAE-NADES approach show its high extraction efficiency while contributing to green practices in the field of natural product extraction. However, further research is necessary to improve our understanding of these extraction strategies, optimize product yields, and reduce overall costs, to facilitate the scaling-up.

Olive tree leaves as a great source of phenolic compounds: Comprehensive profiling of NaDES extracts.

Waste from the olive industry is a noticeable source of antioxidant compounds that can be extracted and reused to produce raw materials related to the chemical, cosmetic, food and pharmaceutical sectors. This work studies the phenolic composition of olive leaf samples using liquid chromatography with ultraviolet detection coupled to mass spectrometry (LC-UV-MS). Olive leaf waste samples have been crushed, homogenized, and subjected to a solid-liquid extraction treatment with mechanical shaking at 80 °C for 2 h using Natural Deep Eutectic Solvents (NaDES). The phenolic compound identification in the resulting extracts has been carried out by high-resolution mass spectrometry (HRMS) using data-dependent acquisition mode using an Orbitrap HRMS instrument. >60 different phenolic compounds have been annotated tentatively, of which about 20 have been confirmed from the corresponding standards. Some of the most noticeable compounds are oleuropein and its aglycone and glucoside form, luteolin-7-O-glucoside, 3-hydroxytyrosol, and verbascoside.

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions.

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.

Identification of seafood as an important dietary source of heterocyclic amines by chemometry and chromatography-mass spectrometry.

Heterocyclic amines (HCAs), which are formed during the cooking of protein-rich foods, are potent mutagens and a risk factor for human cancers. Levels of HCAs have been extensively investigated in meat products but not in fish products. Here, we report levels of HCAs in fried salmon, tuna, hake, sardine, angler fish, cod, sole, swordfish, squid, and cuttlefish. The HCA levels of some of these foods have not been previously analyzed. We employed multivariate factor-analysis tools, including principal components analysis (PCA) and partial least-squares (PLS) regression, to study the effects of cooking weight loss and levels of creatine, glucose, and free amino acids on HCA levels. The highest concentrations of mutagenic HCAs, 159.3 ng·g(-1) total, where 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) accounted for 121 ng·g(-1), were found in fried swordfish (cooking loss of 51.8%). These levels are higher than those generally found in fried chicken, which is typically cited as the most contaminated food item. Thus, swordfish is among the richest known sources of HCAs. The other cooked seafood items contained from 0.4 to 35.4 ng·g(-1) HCAs, comparable to concentrations typically reported for meat. Chemometric analysis showed that the fish species is the most influential parameter on the formation of HCAs such as DMIP, PhIP, and norharman. Concentrations of histidine, lysine, creatine, and glucose, as well as weight loss, also influence the yield of HCAs. These results suggest that seafood is an important dietary source of HCAs. The formation of HCAs in fish is influenced by multiple factors, some of which remain unknown.

Authentication of Cocoa Products Based on Profiling and Fingerprinting Approaches: Assessment of Geographical, Varietal, Agricultural and Processing Features.

Cocoa and its derivative products, especially chocolate, are highly appreciated by consumers for their exceptional organoleptic qualities, thus being often considered delicacies. They are also regarded as superfoods due to their nutritional and health properties. Cocoa is susceptible to adulteration to obtain illicit economic benefits, so strategies capable of authenticating its attributes are needed. Features such as cocoa variety, origin, fair trade, and organic production are increasingly important in our society, so they need to be guaranteed. Most of the methods dealing with food authentication rely on profiling and fingerprinting approaches. The compositional profiles of natural components -such as polyphenols, biogenic amines, amino acids, volatile organic compounds, and fatty acids- are the source of information to address these issues. As for fingerprinting, analytical techniques, such as chromatography, infrared, Raman, and mass spectrometry, generate rich fingerprints containing dozens of features to be used for discrimination purposes. In the two cases, the data generated are complex, so chemometric methods are usually applied to extract the underlying information. In this review, we present the state of the art of cocoa and chocolate authentication, highlighting the pros and cons of the different approaches. Besides, the relevance of the proposed methods in quality control and the novel trends for sample analysis are also discussed.

Green Extraction of Antioxidant Compounds from Olive Tree Leaves Based on Natural Deep Eutectic Solvents.

Agri-food industries generate a large amount of waste that offers great revalorization opportunities within the circular economy framework. In recent years, new methodologies for the extraction of compounds with more eco-friendly solvents have been developed, such as the case of natural deep eutectic solvents (NADES). In this study, a methodology for extracting phenolic compounds from olive tree leaves using NADES has been optimized. The conditions established as the optimal rely on a solvent composed of choline chloride and glycerol at a molar ratio of 1:5 with 30% water. The extraction was carried out at 80 °C for 2 h with constant agitation. The extracts obtained have been analyzed by high-performance liquid chromatography coupled to tandem mass spectrometry (HPLC-MS/MS) in MRM mode. The comparison with conventional ethanol/water extraction has shown that NADES, a more environmentally friendly alternative, has improved extraction efficiency. The main polyphenols identified in the NADES extract were Luteolin-7-O-glucoside, Oleuropein, 3-Hydroxytyrosol, Rutin, and Luteolin at the concentrations of 262, 173, 129, 34, and 29 mg kg-1 fresh weight, respectively.

Targeted HPLC-UV Polyphenolic Profiling to Detect and Quantify Adulterated Tea Samples by Chemometrics.

Tea can be found among the most widely consumed beverages, but it is also highly susceptible to fraudulent practices of adulteration with other plants such as chicory to obtain an illicit economic gain. Simple, feasible and cheap analytical methods to assess tea authentication are therefore required. In the present contribution, a targeted HPLC-UV method for polyphenolic profiling, monitoring 17 polyphenolic and phenolic acids typically described in tea, was proposed to classify and authenticate tea samples versus chicory. For that purpose, the obtained HPLC-UV polyphenolic profiles (based on the peak areas at three different acquisition wavelengths) were employed as sample chemical descriptors for principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) studies. Overall, PLS-DA demonstrated good sample grouping and discrimination of chicory against any tea variety, but also among the five different tea varieties under study, with classification errors below 8% and 10.5% for calibration and cross-validation, respectively. In addition, the potential use of polyphenolic profiles as chemical descriptors to detect and quantify frauds was evaluated by studying the adulteration of each tea variety with chicory, as well as the adulteration of red tea extracts with oolong tea extracts. Excellent results were obtained in all cases, with calibration, cross-validation, and prediction errors below 2.0%, 4.2%, and 3.9%, respectively, when using chicory as an adulterant, clearly improving on previously reported results when using non-targeted HPLC-UV fingerprinting methodologies.