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).

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.

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.

Discovery of 7-azaindole derivatives as potent Orai inhibitors showing efficacy in a preclinical model of asthma.

Synthesis and SAR of a series of 7-azaindoles as Orai channel inhibitors showing good potency inhibiting IL-2 production in Jurkat cells is described. Compound 14d displaying best pharmacokinetic properties was further characterized in a model of allergen induced asthma showing inhibition in the number of eosinophils in BALF. High lipophilicity remains as one of the main challenges for this class of compounds.

The in vitro and in vivo profile of aclidinium bromide in comparison with glycopyrronium bromide.

This study characterised the in vitro and in vivo profiles of two novel long-acting muscarinic antagonists, aclidinium bromide and glycopyrronium bromide, using tiotropium bromide and ipratropium bromide as comparators. All four antagonists had high affinity for the five muscarinic receptor sub-types (M1-M5); aclidinium had comparable affinity to tiotropium but higher affinity than glycopyrronium and ipratropium for all receptors. Glycopyrronium dissociated faster from recombinant M3 receptors than aclidinium and tiotropium but more slowly than ipratropium; all four compounds dissociated more rapidly from M2 receptors than from M3 receptors. In vitro, aclidinium, glycopyrronium and tiotropium had a long duration of action at native M3 receptors (>8 h versus 42 min for ipratropium). In vivo, all compounds were equi-potent at reversing acetylcholine-induced bronchoconstriction. Aclidinium, glycopyrronium and ipratropium had a faster onset of bronchodilator action than tiotropium. Aclidinium had a longer duration of action than glycopyronnium (time to 50% recovery of effect [t½ offset] = 29 h and 13 h, respectively); these compare with a t½ offset of 64 h and 8 h for tiotropium and ipratropium, respectively. Aclidinium was less potent than glycopyrronium and tiotropium at inhibiting salivation in conscious rats (dose required to produce half-maximal effect [ED50] = 38, 0.74 and 0.88 µg/kg, respectively) and was more rapidly hydrolysed in rat, guinea pig and human plasma compared with glycopyrronium or tiotropium. These results indicate that while aclidinium and glycopyrronium are both potent antagonists at muscarinic receptors with similar kinetic selectivity for M3 receptors versus M2, aclidinium has a longer dissociation half-life at M3 receptors and a longer duration of bronchodilator action in vivo than glycopyrronium. The rapid plasma hydrolysis of aclidinium, coupled to its kinetic selectivity, may confer a reduced propensity for systemic anticholinergic side effects with aclidinium versus glycopyrronium and tiotropium.

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.

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.

Determination of S-containing drug metabolites from in vitro and in vivo metabolism studies by using LC-ICP/MS.

Recently, liquid chromatography coupled to inductively coupled plasma mass spectrometry (LC-ICP/MS) has been introduced to deal with some applications in the field of pharmaceutical, biomedical, and clinical analysis. In the case of drug research, the number of drugs and their metabolites containing detectable elements is quite limited. In this paper, LC-ICP/MS has been demonstrated to be suitable for the determination of S-containing drugs and their metabolites. In order to minimize the interference of polyatomic oxygen (m/z 32), the indirect detection of S, by means of the SO(+) ion (m/z 48), was optimized. For quantification purposes, it has been encountered that the percentage of organic solvent in the mobile phase strongly affects the sensitivity. Here, corrective strategies based on calibration curves established at different solvent concentrations (solvent-zone quantification) and post-column gradient compensation have been proposed to circumvent sensitivity variations. Results obtained have shown that suitable calibration models have been built for any compound regardless of the solvent percentage at which it is eluted from the chromatographic column. To prove the applicability of this methodology, the metabolism of ethacrynic acid and tiotropium bromide has been studied in vitro and in vivo. In the first case, ethacrynic acid does not contain S in its structure, however, the major route of metabolism for this compound consists of the formation of glutathione adduct and its further degradation. In the second case, tiotropium bromide contains two S atoms in its structure.

Characterization of in vitro metabolic profiles of cinitapride obtained with liver microsomes of humans and various mammal species using UHPLC and chemometric methods for data analysis.

An ultra-high performance liquid chromatographic method has been utilized to obtain metabolic profiles of cinitapride with liver microsomes of humans and various mammal species such as rats, mice, mini pigs, dogs, and monkeys. Metabolites have been generated by incubation of cinitapride in the presence of microsomes using nicotinamide adenine dinucleotide phosphate as a cofactor. Incubation times from 15 to 60 min have been assayed. Cinitapride and its metabolites have been separated by reversed-phase C(18) mode using ammonium formate aqueous solution (pH 6.5) and acetonitrile as the components of the mobile phase. Concentrations of metabolites in the incubated samples have resulted in an excellent source of multivariate data to be used to extract metabolic information. Statistic parameters and principal component analysis have been used to compare the in vitro metabolism of humans with the other species.

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.