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.

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.

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.

Characterization and Classification of Spanish Honeydew and Blossom Honeys Based on Their Antioxidant Capacity.

Honey is a very appreciated product for its nutritional characteristics and its benefits for human health, comprising antioxidant, anti-inflammatory, antifungal, and antibacterial activities. These attributes depend on the specific composition of each honey variety, with the botanical origin as one of the distinctive features. Indeed, honeydew and blossom honeys show different physicochemical properties, being the antioxidant capacity, mainly relying on the phenolic compound content, one of the most important. In this work, Folin-Ciocalteu (FC) index, total flavonoid content (TFC), and the antioxidant capacity based on the ferric reducing antioxidant power (FRAP) assay were determined for a total of 73 honeys (50 blossom honeys and 23 honeydew honeys). Mean content of oxidizable species (FC index) ranges from 0.17 to 0.7 mg eq. gallic acid g-1, with honeydew honeys being the ones with higher values. Regarding TFC, mean values above 1.5 mg eq. quercetin g-1 (method applied in the absence of NaNO2) were obtained for honeydew honeys and heather honey. Lower and not discriminatory values (below 0.3 mg eq. epicatechin g-1) were obtained in the presence of NaNO2. The maximum antioxidant capacity was observed for thyme honeys (2.2 mg eq. Trolox g-1) followed by honeydew and heather honeys. Individually, only the FC index was able to discriminate between honeydew and blossom honeys, while the other spectroscopic indexes tested allowed the differentiation of some honey types according to the botanical origin. Thus, a holistic treatment of the results was performed using partial least square discriminant analysis (PLS-DA) for classification purposes using FC, TFC, and FRAP results as data. Honeydew and blossom honey were satisfactorily discriminated (error 5%). In addition, blossom honeys can be perfectly classified according to their botanical origin based on two-class PLS-DA classification models.

Recovery of phenolic compounds from wine lees using green processing: Identifying target molecules and assessing membrane ultrafiltration performance.

Winery wastes are rich in polyphenols with high added value to be used in cosmetics, pharmaceuticals, and food products. This work aims at recovering and purifying the polyphenolic fraction occurring in the malolactic fermentation lees generated during the production of Albariño wines. Phenolic acids, flavonoids, and related compounds were recovered from this oenological waste by green liquid extraction using water as the solvent. The resulting extract solution was microfiltered to remove microparticles and further treated by ultrafiltration (UF) using membranes of 30 kDa and 5 kDa molecular weight cut-offs (MWCOs). The feed sample and the filtrate and retentate solutions from each membrane system were analyzed by reversed-phase liquid chromatography (HPLC) with UV and mass spectrometric (MS) detection. The most abundant polyphenols in the extracts were identified and quantified, namely: caftaric acid with a concentration of 200 µg g-1 and trans-coutaric acid, cis-coutaric acid, gallic acid, and astilbin with concentrations between 15 and 40 µg g-1. Other minor phenolic acids and flavanols were also found. The UF process using the 30 kDa membrane did not modify the extract composition, but filtration through the 5 kDa poly-acrylonitrile membrane elicited a decrease in polyphenolic content. Hence, the 30 kDa membrane was recommended to further pre-process the extracts. The combined extraction and purification process presented here is environmentally friendly and demonstrates that malolactic fermentation lees of Albariño wines are a valuable source of phenolic compounds, especially phenolic acids.

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%.

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.

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).

Characterization, Classification and Authentication of Spanish Blossom and Honeydew Honeys by Non-Targeted HPLC-UV and Off-Line SPE HPLC-UV Polyphenolic Fingerprinting Strategies.

Honey is a highly consumed natural product produced by bees which is susceptible to fraudulent practices, some of them regarding its botanical origin. Two HPLC-UV non-targeted fingerprinting approaches were evaluated in this work to address honey characterization, classification, and authentication based on honey botanical variety. The first method used no sample treatment and a universal reversed-phase chromatographic separation. On the contrary, the second method was based on an off-line SPE preconcentration method, optimized for the isolation and extraction of polyphenolic compounds, and a reversed-phase chromatographic separation optimized for polyphenols as well. For the off-line SPE method, the use of HLB (3 mL, 60 mg) cartridges, and 6 mL of methanol as eluent, allowed to achieve acceptable recoveries for the selected polyphenols. The obtained HPLC-UV fingerprints were subjected to an exploratory principal component analysis (PCA) and a classificatory partial least squares-discriminant analysis (PLS-DA) to evaluate their viability as sample chemical descriptors for authentication purposes. Both HPLC-UV fingerprints resulted to be appropriate to discriminate between blossom honeys and honeydew honeys. However, a superior performance was accomplished with off-line SPE HPLC-UV polyphenolic fingerprints, being able to differentiate among the different blossom honey samples under the study (orange/lemon blossom, rosemary, thyme, eucalyptus, and heather). In general, this work demonstrated the feasibility of HPLC-UV fingerprints, especially those obtained after off-line SPE polyphenolic isolation and extraction, to be employed as honey chemical descriptors to address the characterization and classification of honey samples according to their botanical origin.

High-Throughput Flow Injection Analysis-Mass Spectrometry (FIA-MS) Fingerprinting for the Authentication of Tea Application to the Detection of Teas Adulterated with Chicory.

Tea is a broadly consumed beverage worldwide that is susceptible to fraudulent practices, including its adulteration with other plants such as chicory extracts. In the present work, a non-targeted high-throughput flow injection analysis-mass spectrometry (FIA-MS) fingerprinting methodology was employed to characterize and classify different varieties of tea (black, green, red, oolong, and white) and chicory extracts by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Detection and quantitation of frauds in black and green tea extracts adulterated with chicory were also evaluated as proofs of concept using partial least squares (PLS) regression. Overall, PLS-DA showed that FIA-MS fingerprints in both negative and positive ionization modes were excellent sample chemical descriptors to discriminate tea samples from chicory independently of the tea product variety as well as to classify and discriminate among some of the analyzed tea groups. The classification rate was 100% in all the paired cases-i.e., each tea product variety versus chicory-by PLS-DA calibration and prediction models showing their capability to assess tea authentication. The results obtained for chicory adulteration detection and quantitation using PLS were satisfactory in the two adulteration cases evaluated (green and black teas adulterated with chicory), with calibration, cross-validation, and prediction errors below 5.8%, 8.5%, and 16.4%, respectively. Thus, the non-targeted FIA-MS fingerprinting methodology demonstrated to be a high-throughput, cost-effective, simple, and reliable approach to assess tea authentication issues.

Differential mobility spectrometry coupled to mass spectrometry (DMS-MS) for the classification of Spanish PDO paprika.

Ion mobility spectrometry (IMS) has proved its huge potential in many research areas, especially when hyphenated with chromatographic techniques or mass spectrometry (MS). However, focusing on food analysis, and particularly in classification and authentication issues, very few applications have been reported. In this study, differential mobility spectrometry coupled to mass spectrometry (DMS-MS) is presented for the first time as an alternative and high-throughput technique for food classification and authentication purposes using a fingerprinting strategy. As a study case, 70 Spanish paprika samples (from La Vera, Murcia, and Mallorca) were analyzed by DMS-MS to address their classification -using partial least squares regression-discriminant analysis (PLS-DA)- and authentication -through soft independent modeling of class analogy (SIMCA). As a result, after external validation, complete sample classification according to their geographical origin and excellent La Vera and Mallorca sample authentication were reached.

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.

Total Polyphenol Content in Food Samples and Nutraceuticals: Antioxidant Indices versus High Performance Liquid Chromatography.

Total polyphenol content and antioxidant capacity were estimated in various food and nutraceutical samples, including cranberries, raspberries, artichokes, grapevines, green tea, coffee, turmeric, and other medicinal plant extracts. Samples were analyzed by using two antioxidant assays-ferric reducing antioxidant power (FRAP) and Folin-Ciocalteu (FC)-and a reversed-phase high-performance liquid chromatography (HPLC), with a focus on providing compositional fingerprints dealing with polyphenolic compounds. A preliminary data exploration via principal component analysis (PCA) revealed that HPLC fingerprints were suitable chemical descriptors to classify the analyzed samples according to their nature. Moreover, chromatographic data were correlated with antioxidant data using partial least squares (PLS) regression. Regression models have shown good prediction capacities in estimating the antioxidant activity from chromatographic data, with determination coefficients (R2) of 0.971 and 0.983 for FRAP and FC assays, respectively.

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.

Sensitive UHPLC-MS/MS method for the determination of tacrolimus in minipig whole blood.

Background: Tacrolimus, a potent immunosuppressant drug widely used systemically to reduce the risk of organ rejection in transplants, has been repositioned for topical treatment of atopic dermatitis. Results & methodology: This work describes the optimization of a new method for the determination of tacrolimus in whole blood after topical administration. Sample treatment consisted of an automated procedure based on protein precipitation followed by solid-phase extraction. The present method showed good performance with quantitation limits of 10 pg ml-1 and intra- and interday precision and accuracy lower than 15 and 10%, respectively. Conclusion: A new highly sensitive UHPLC-MS/MS method has been developed enabling a better characterization of the minipig blood plasma pharmacokinetic behavior of tacrolimus after topical administration.

Liquid chromatography coupled to mass spectrometry for metabolite profiling in the field of drug discovery.

INTRODUCTION: The identification and characterization of the metabolites during the early stages of discovery and development of new drug candidates are essential to establish the metabolic clearance as well as the potential pharmacological and/or toxicological effects. Hence, feasible methods of analysis, preferably rapid and simple, are required to satisfy the increasing demand of metabolite profiling studies. Areas covered: This paper reviews the topic of metabolite profiling in drug discovery based on liquid chromatography, with especial emphasis on chromatographic modes and detectors. Features and possibilities of the different options are critically discussed. Expert opinion: High performance analytical techniques are fundamental to gain unambiguous information on metabolites of new drugs. In this regard, liquid chromatography hyphenated to mass spectrometric detection is the most popular approach. The diversity of chromatographic modes and the great variety of separation columns available offer innumerable analytical possibilities to characterize and quantify compounds with a broad range of physicochemical properties.

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.

Strategies for metabolite profiling based on liquid chromatography.

This paper aims at covering the principal strategies based on liquid chromatography (LC) for metabolite profiling in the field of drug discovery and development. The identification of metabolites generated in the organism is an important task during the early stages of preclinical research to define the most proper strategy for optimizing, adjusting metabolic clearance and minimizing bioactivation. An early assessment of the metabolite profile may be critical since metabolites can contribute to pharmacological and/or toxicological effects. The study of metabolites first involves their synthesis/generation and their further characterization and structural elucidation. For such a purpose, both in vitro and in vivo methods are commonly used for the generation of the corresponding metabolites. Next, analytical methods are used to tackle identification and characterization studies. Among the arsenal of techniques available in our labs, we will focus on LC, especially coupled to mass spectrometry (LC-MS), as one of the most powerful approaches for metabolite identification, characterization and quantification. Here, the topic of metabolite profiling based on LC will be addressed and representative examples of different possibilities will be discussed.