Untargeted metabolomic approach based on UHPL-ESI-HRMS to investigate metabolic profiles of different Coffea species and terroir

Untargeted metabolomic approach based on UHPL-ESI-HRMS to investigate metabolic profiles of different Coffea species and terroir | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Untargeted metabolomic approach based on UHPL-ESI-HRMS to investigate metabolic profiles of different Coffea species and terroir Mateus Manfrin Artêncio, Alvaro Luis Lamas Cassago, Renata Kelly Silva, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2828021/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coffee ( Coffea spp) has evolved from an agricultural commodity to a specialty beverage, regarding the product’s trading, appreciation, philosophies, and purposes of consumption. Consequently, part of the coffee industry has focused on the sensory complexion and high-quality to meet engaged consumers. To evaluate the chemical profiles and distinctiveness of natural products from plants, metabolomics has emerged as a valuable tool. In this work, we carried out an untargeted metabolomic approach based on reversed-phase liquid chromatography coupled with mass spectrometry, followed by multivariate statistical analysis to obtain the metabolic fingerprints of 21 coffee samples belonging to two species and five botanical varieties, as follows: C. arabica (var. yellow catuai , yellow bourbon , and yellow obata ) and C. canephora (var. conilon , and robusta ). The samples were obtained in the 2022 Edition of the “Brazilian International Conference of Coffee Tasters”, state of Rondônia, Brazil. Principal Component Analysis and Orthogonal Projections Latent Structures Discriminant Analysis were performed using the metabolomic data, resulting in the discrimination of coffee samples based on their chemical profiles. Caffeine, DIMBOA-Gl, roemerine, and cajanin were determined as chemical markers for C. canephora samples, and toralactone, cnidilide, LysoPC(18:2(9Z,12Z)), Lysophosphatidylcholine(16:0/0:0), and 2,3-Dehydrosilybin for C. arabica samples. In addition to the genetic variability, our results show the possible influence of a terroir factor in the production of secondary metabolites of coffee samples, mainly for individuals of C. canephora . coffee Coffea untargeted metabolomics multivariate statistical analysis terroir Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Coffee ( Coffea spp) is one of the most consumed beverages in the world, a food product that has evolved from an agricultural commodity to a specialty beverage (ICO, 2022 ). According to Cenci, Combes & Lashermes ( 2012 ), the genus Coffea L. is relatively new when compared to other genera, and it includes around a hundred of species adapted to distinct environments. Coffee cultivated in the Brazilian Amazon Rain Forest, Ethiopia’s Kafa region, and in the Spanish Canary Islands are examples of the adaptability of the genus Coffea in different continents. The international coffee trade and industry based its activities mostly on two different cultivars: Coffea arabica L. and C. canephora var. robusta (L. Linden) A. Chev. (Vezzulli et al., 2022 ). Their differences reside on a market/consumer and biological standpoint. For instance, all species belonging to Coffea spp are diploid, except those of C. arabica (Lashermes et al., 1999 ). Specifically, C. arabica has an allotetraploid genome and is originated from the hybridization of the diploid species C. eugenioides S. Moore and C. canephora Pierre ex A. Froehner (Lashermes et al., 1999 ). Regarding C. canephora var. robusta , prior research that focused on coffees’ enzymatic activity and expression showed that this variety accumulates less saccharose than the C. arabica , which is an important precursor of coffee’s taste (Privat et al., 2008 ). Moreover, they present different biochemical ripening processes, and undergo distinct post-harvesting processes (Vezulli et al., 2022). Such physical characteristics are shown in Fig. 1 . Regarding the consumer perspective, differences reside on the organoleptic properties of the beverage (e.g., flavor, aroma, body, mouthfeel, aftertaste, etc.). Usually, consumers prefer coffees brewed with C. arabica beans, because of its taste and high acidity (Perrois et al., 2015 ). Distinctively, C. canephora var. robusta is considered by consumers more bitter and intense (Perrois et al., 2015 ). In short, differences concerning the genetic, biochemical, origin-related and sensory aspects of these two species end up being important sources of diversity in coffees’ chemical profiles, flavors and quality (Lucini, Rocchetti & Trevisan, 2020 ; Artêncio, Giraldi & de Oliveira, 2022 ). Changes in consumer demand for coffee have transformed market configurations, particularly with consumers’ praise for high quality and differentiation, which pushed coffee producers and brands to offer unique sensory characteristics (Guimarães et al., 2019 ; Belchior et al., 2020 ). According to Cassago et al. ( 2021 ), the origin information of coffee (place, region or country of origin) plays an important role in quality communication and product differentiation once it evokes the reputation and image built around the natural features (e.g., climate, soil, altitude, flora, and fauna) and traditions regarding coffee production of a specific geographical origin. Past studies showed that the origin information could impact the expectation and sensory perception of amateur and professional tasters (Artêncio, Giraldi, et al., 2022 ; Artêncio et al., 2023 ). The term terroir , although often related to high-quality wines, is also used in the coffee industry to summarize the complexity of factors and interactions (human and natural) deeply intertwined with the very essence of specialty coffees (van Leeuwen & Seguin, 2006 ). When the nature and quality of a good is essentially due to the place of origin (i.e., due to terroir ), it can be registered as a geographical indication (GI) to differentiate from competing goods (WIPO, 2021). This basic concept underlying GIs is simple and familiar to any consumer who chooses Roquefort over blue cheese or Champagne over other sparkling wines, some well-known examples of names associated throughout the world with products having characteristics linked to that origin (Artêncio et al., 2023 ). As a response to the advances on the sensory complexion and differentiation of food, analytical techniques have been applied to evaluate their quality and composition, such as metabolomics (Rochetti et al., 2020; Gigl et al., 2022 ). The purpose of this set of techniques is to assess the correlation between genotype and phenotype profiles of a biological system, which are influenced by genetic and environmental change (Cassago et al., 2021 ). In the case of coffee, post-harvest processes (e.g., roasting and storage) and brewing methods are included as important sources of sensory variations (Vezzulli et al., 2022 ; Aresta & Zambonin, 2023 ). For instance, metabolomics techniques can be applied to determine and authenticate coffee origin (Jumhawan et al., 2016 ; Giraudo et al., 2019 ), identify sensory quality markers (substances) in coffee (Rochetti et al., 2020), or discriminate coffee brewed by different methods (Bravo et al., 2012 ; Grassi et al., 2023 ). Once the final sensory experience with coffee is a result of primary and secondary metabolites, prior research has explored their influence on quality, particularly in terms of acidity, body, and sweetness, which are considered important elements of coffee quality (SCA, 2015 ; da Rosa et al., 2016 ). For instance, coffees’ bitterness is closely linked to the presence of caffeine (da Rosa et al., 2016 ) and chlorogenic acids (Schenker & Rothgeb, 2017 ), while its acidity is originated from quinic, citric and malic acids (Koshiro et al. 2015 ). To the best of our knowledge, only a few studies in the literature tried to correlate the chemical composition of ground coffee to its sensorial quality. Recently, Rochetti et al. (2020) applied metabolomics to identify a group of chemical markers that allow the correlation between the chemical profile of powdered coffee with its sensory scores, obtained on a tasting competition of C. arabica and C. canephora coffees. Despite considering over 300 coffee origins collected during the competition, the authors have not included coffees from South America, where some important coffee producing countries are located such as Brazil, Colombia, and Peru (ICO, 2022 ). Besides, the authors have not analyzed one important commercial variety of C. canephora , the “conilon” variety. Hence, in this work we carried out a metabolomic study based on ultra-efficient liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-ESI-HRMS) using Brazilian ground coffees obtained from the first “Brazilian International Conference of Coffee Tasters”, which was coordinated by the Brazilian Agricultural Research Corporation (Embrapa), Federal Institute of Espirito Santo (IES), and the Association of the Region of the Forests of Rondônia (Caferon). As suggested in the recently published work of Cassago et al. ( 2021 ), this work presents a contribution of metabolomic data in describing a place’s terroir . Originally, in this study samples of coffee grown in most of various Brazilian producing regions were analyzed, including different Brazilian Geographical Indications (GIs) and the Amazon region. Specifically, our main goals were (1) to carry out untargeted metabolomics to investigate the metabolic profiles of two different Coffea species and their varieties ( C. arabica , C. canephora var. robusta and C. canephora var. conilon ) and (2) to correlate coffee metabolic profiles with their species, geographic origin, and terroir . Figure 2 shows how the processes for the analyses were carried out. 2. Materials And Methods 2.1. Coffee samples Samples consisted of ground coffee obtained in the “Brazilian International Conference of Coffee Tasters”. In total, 21 coffee samples were used in this study: nine of C. arabica , six of C. canephora var. robusta, and four of C. canephora var. conilon from Brazil and two samples of C. arabica from Mexico. Figure 3 illustrates the origin of all coffee samples used in the study. Almost all coffees are considered specialty coffees (except for one, referred here as commodity coffee), meaning that professional coffee tasters have scored with more than 80 points using the Specialty Coffee Association (SCA) protocols and best practices in cupping and grading (SCA, 2015 ). The green coffee beans were roasted in a Probatino roaster (Probat Inc. Germany), based on the SCA recommendations (SCA, 2015 ) in order to achieve a light-medium roast profile (60–55 Agtron units – Agtron, Inc., Reno, NV). Information about samples (e.g., species, variety, origin) is available on Supplementary Material. 2.2. Preparation of extracts The extraction procedure was based on the protocol for large-scale untargeted metabolomics of plant tissues reported by de Vos et al. ( 2007 ), with some modifications supported by previous studies of the AsterBiochem research group (Cassago et. al, 2022 ; Rocchetti et al., 2020 ). A total of 30 mg of powdered coffee from each sample were extracted with 1.5 mL of a methanol-water solution (7:3, v/v) in an ultrasonic bath (10 min at 25°C and 40 kHz). After the extraction, the samples were centrifuged at 13,000 rpm for 10 min. The supernatant was partitioned with 0.5 mL of heptane, and the aqueous layer was filtered through a 0.22 µm PTFE membrane filter. All solvents used were HPLC grade, and a new membrane filter was used for each extraction. 2.3. UHPLC-ESI-HRMS analysis The extracts were analyzed with UHPLC-ESI-HRMS equipped with Accela 1250 quaternary pumps (Thermo Scientific, USA), which were coupled to a mass spectrometer with an Orbitrap analyzer synchronized using the software Xcalibur 2.2 (Thermo Scientific). The chromatographic separation was performed in a Kinetex XB-C18 column (1,7 µm, 150 × 2,1 mm, Phenomenex, USA) connected to a compatible guard column in an oven (35 ºC). Water (A) and methanol (B), both with 0.1% formic acid were used as mobile phase (220 µL/min flow rate). The elution program was 5% B to 95% B in 34 min, isocratic 95% B until 38 min. Injection volume was 5 µL, in triplicate for each sample. Mass spectrometry detection was carried out in both positive and negative ionization modes using the fullscan (resolution of 70,000) method. Total ion current (TIC) chromatograms were obtained over the range of 100–1,500 m/z using a spray voltage of + 3.5 and − 3.0 kV for the positive and negative ionization modes, respectively, and the capillary temperature of 320°C. 2.4. Data pre-processing and multivariate analysis Data obtained from the UHPLC-ESI-HRMS analyses were pre-treated and pre-processed for the multivariate statistical analysis. Using ProteoWizard software (Proteowizard Software Foundation, USA) data were separated according to ionization modes (positive or negative) and converted to a . mzXML file. The data obtained for each ionization mode were processed with MZmine 2.37 (Pluskal et al., 2010 ) to identify: peak detection, peak filtering, chromatogram construction, chromatogram deconvolution, isotopic peak grouping, chromatogram alignment, gap filling, duplicate peaks filter, fragment search, and adducts and peak identities. The following MZmine parameters were used for data preprocessing: noise level at 1.0E5; Lorentzian function as peak shape algorithm (resolution of 70,000); minimum peak height at 5.0E5; m/z tolerance at 0.002 m/z or 5.0 ppm; and a retention time tolerance of 0.3 min. After pre-processing, data of both ionization modes were exported as .csv tables with rows representing plant extracts and columns representing ion peak areas associated with a given mass and retention time values. Before statistical analyses, peaks detected in the blank (extraction solvent) and the final stages of the chromatographic runs were removed from the original matrix. Multivariate statistical analyses were carried out using the software SIMCA (v. 13.0.3.0, Umetrics, Sweden). First, an unsupervised multivariate statistical analysis (PCA) was performed. After this exploratory analysis, the supervised method orthogonal partial least squares - discriminant analysis (OPLS-DA) was used to identify the X variables (chromatographic peaks) which were correlated with the Y response variables (plant origin information and plant taxonomic information). In metabolomics studies, OPLS-DA can be applied to data classification with one or more classes (Cassago et al., 2022 ; Padilla-González et al., 2021 ; Gallon et al., 2018 ) For data analysis, Pareto-type normalization was used (data scaling using the square root of the standard deviation) to normalize peak area values, corresponding to the X variables (van den Berg et al., 2006 ). Additionally, the OPLS-DA model validation parameters (goodness-of-fit - R 2 Y - together with goodness-of-prediction - Q 2 Y) were checked. Thereafter, the OPLS-DA model was checked for outliers, CV-ANOVA p-value threshold 200) was performed to exclude model overfitting (Rocchetti et al., 2021 ). The permutation plot shows the correlation coefficient between the original Y-variable and the permuted Y-variable on the X-axis versus the cumulative R 2 and Q 2 on the Y-axis, and then originates a regression curve, where the intercept is the measure of the overfitting. 2.5. Compounds’ annotation and identification Following Freitas et al. ( 2021 ) and Cassago et al. ( 2022 ), the identification of metabolites by UHPLC-ESI-HRMS presented in this study is in accordance with the classification of identification levels used in prior metabolomics studies (Blaženović et al., 2018 ; Sumner et al., 2007 ), where: level 0 = isolated compounds; level 1 = identified compounds; level 2 = annotated compounds; level 3 = putative compounds; level 4 = unknown compounds. Mass accuracy was calculated in Microsoft Excel (Microsoft Corp., USA) and tolerance was established at 5.0 ppm (Brenton and Godfrey, 2010 ). For annotated compounds (level 2), an in-house database was built, which was used the following steps: i. data collection using SciFinder Scholar (Chemical Abstract Service, USA); ii. drawing of the chemical structures with MarvinSketch 18.24 (ChemAxon Ltd., Hungary); iii. and database creation, handling, and visualization with JChem for Excel (ChemAxon, Hungary), a plugin that transforms chemical structures in . mol format into their corresponding SMILES codes and calculates their exact masses (Cassago et al., 2022 ; Freitas et al., 2021 ). 3. Results And Discussion 3.1. Chromatographic analyses This work reports a chemical study on the secondary metabolites of coffee beans from two different species belonging to the genus Coffea ( C. arabica and C. canephora ) and their varieties. Based on the analysis of the metabolic fingerprints, it is possible to observe that, despite some similarities, chromatograms of the two species and their respective varieties present important chemical differences. Therefore, it becomes important to analyze the chemical fingerprints (metabolic profiles) of all species and varieties using multivariate statistical tools to launch new ideas on this subject. Zidorn ( 2019 ) proposed the term “chemophenetics” to describe chemosystematic studies where the aim is to explain a matrix of natural products from a given taxon and use them for a phenetic characterization of clades found by methods based on DNA-sequence (Zidorn, 2019 ). In the literature, several studies have been using chemophenetic and chemotaxonomic approaches (de Brito et al., 2021 ), including different plant families such as Euphorbiaceae (Lan et al., 2020 ; Rivière et al., 2012), Myrtaceae (Santos et al., 2020 ), and Asteraceae (Ccana-Ccapatinta et al., 2020 ), and the genus Ananas (Cassago et al., 2022 ). Cassago et al. ( 2022 ) showed in their work the metabolic profiles of several samples of Ananas , and how their secondary metabolites were used to discriminate species and varieties from Ananas using multivariate statistical analysis. Furthermore, the chemical composition of Ananas leaves could be used to assist in the taxonomic classification of individuals belonging to this genus (Cassago et al, 2022 ). Our study shows that there are notable differences in the chemical composition of the species and varieties in Coffea ground beans. Based on results, it could be inferred that such metabolites may act as markers, which are responsible for the chemical differentiation of Coffea species. Additional studies are needed to confirm this hypothesis, with additional analysis using multivariate statistics. 3.2. Identified and annotated compounds Annotations were made using the chemical structures library of Coffea created from the compounds already described in the literature. The annotations were based on the comparison of the monoisotopic mass in high resolution; overall, 33 chemical structures were annotated. Via injections of pure compounds, using the same chromatographic methodology used for the coffee extracts, it was possible to identify two compounds: caffeine and trans -chlorogenic acid. The identification of compounds in LC-MS-based metabolomics can be assessed using five confidence levels, where 0 is the highest and, therefore, compounds for which there is the most confidence in their identification, followed by the levels 1 to 4, with decreasing confidence levels (Blaženović et al., 2018 ). Using this approach, it was possible to identify 31 compounds at level 2, and two at level 1. Table 1 shows a list of annotated and identified compounds in the coffee extracts samples numbered according to their elution order and associated with their respective protonated molecules ([M + H] + ) and adducts ([M + H + ACN] + , [M + NH 4 ] + , and ([M + Na] + ) (in positive mode), retention times (RT), tentative identification names, molecular formulae, and confidence levels. Table 1 Compounds identified in Coffea extracts analyzed by UHPLC-ESI-HRMS with m/z values in positive ionization modes (and adducts), molecular formulae, retention times, tentative identification, and respective identification confidence levels. ID Average m/z Adduct type MOLECULAR FORMULA Rt (min) Tentative identification name Level 1 206.886 [M + H]+ C 12 H 15 NO 2 1.77173 dehydrosalsolidine 2 2 176.011 [M + H]+ C 10 H 9 NO 2 1.95744 3-Indoleacetic acid 2 3 220.119 [M + H]+ C₉H₁₇NO₅ 2.04989 calcium (+)-pantothenate 2 4 204.102 [M + H]+ C 12 H 13 NO 2 4.23221 3-indolebutyric acid 2 5 266.138 [M + H]+ C 14 H 20 NO 4 6.91403 caffeoylcholine 2 6 181.097 [M + H]+ C 10 H 12 O 4 10.383 2-(4-hydroxyphenyl)ethyl acetate 2 7 183.092 [M + H]+ C 12 H 10 N 2 11.953 harman 2 8 195.087 [M + H]+ C 8 H 10 N 4 O 2 12.0071 caffeine 1 9 337.091 [M + Na]+ C 17 H 14 O 6 14.2779 cirsimaritin 2 10 177.054 [M + H]+ C 9 H 8 N 2 O 2 14.2906 2-(hydroxymethyl)-4(3H)-quinazolinone 2 11 177.055 [M + NH4]+ C 7 H 13 NO 3 14.2956 calystegine A6 2 12 211.144 [M + H]+ C 11 H 18 N 2 O 2 14.3319 L , L -Cyclo(leucylprolyl) 2 13 195.087 [M + H]+ C 12 H 18 O 2 14.3978 cnidilide 2 14 351.107 [M + Na]+ C 15 H 22 O 7 16.0538 3-(4-hydroxyphenyl)-3-oxopropyl beta-D-glucopyranoside 2 15 499.123 [M + H]+ C 16 H 26 N 4 O 10 S 2 16.8039 N , N’ -Bis(gamma-glutamyl)cystine 2 16 396.090666 [M + Na]+ C 15 H 19 NO 10 18.8455 DIMBOA-Glc 2 17 351.134 [M + H]+ C 20 H 18 N 2 O 4 19.945 Na- p -hydroxycoumaroyltryptophan 2 18 179.154 [M + H + ACN]+ C 8 H 9 NO 23.6158 2-phenylacetamide 2 19 169.086 [M + H]+ C 8 H 12 N 2 O 2 26.046 pyridoxamine 2 20 301.141 [M + H]+ C 14 H 20 O 7 26.0942 4-methoxybenzyl glucoside 2 21 295.185 [M + Na]+ C 15 H 12 O 5 27.4901 toralactone 2 22 493.376 [M + Na]+ C 31 H 46 O 2 27.8341 vitamin K1 epoxide-1,4-diol 2 23 520.339 [M + H]+ C 15 H 10 O 8 28.7922 3,3’,4’,5,5’,8-hexahydroxyflavone 2 24 520.339 [M + H]+ C 26 H 48 NO 7 P 28.8002 lysoPC(18:2(9Z,12Z)) 2 25 324.289 [M + NH4]+ C 18 H 17 NO 2 28.913 (R)-roemerine 2 26 496.34 [M + H]+ C 24 H 50 NO 7 P 29.5094 lysophosphatidylcholine(16:0/0:0) 2 27 257.12592 [M + H]+ C 16 H 16 O 3 29.6467 terostilbene 2 28 323.305 [M + Na]+ C 16 H 12 O 6 30.0606 cajanin 2 29 353.266 [M + H]+ C 16 H 18 O 9 30.1509 hlorogenic acid 1 30 562.31439 [M + H]+ C 31 H 39 N 5 O 5 30.8672 ergocorninine 2 31 380.297 [M + Na]+ C 15 H 19 NO 9 32.5027 HMBOA-Glc 2 32 499.426 [M + NH4]+ C 25 H 20 O 10 32.7455 2,3-dehydrosilybin 2 33 521.408 [M + H]+ C 32 H 50 O 4 33.1785 tsugaric acid A 2 [Table 1 ] With the annotations from the Coffea chemical structure library, caffeine was identified in all samples, which according to da Silva Portela et al. ( 2021 ) is a specific and typical compound found in coffee (da Silva Portela et al., 2021 ). Also, a conjugate of hydroxycinnamic amino acids (Na- p -hydroxy-coumaroyl-tryptophan) was identified. This chemical class has previously been reported as a potential marker to discriminate coffee cultivars (Asamenew et al., 2019 ). In order to identify the distribution of compounds in the analyzed samples, multivariate statistic was performed on data obtained from LC-MS-based metabolomics. The analyses and related discussion are presented below. 3.3. Multivariate statistics PCA analysis was performed for the matrices obtained from the positive ionization mode from LC-MS, where the variable “region of production” was informed to the system to assist in the interpretation of the data. The matrix has samples of leaf extracts in rows (X variables) and data from areas of chromatographic bands are in columns (Y variables). Based on the data obtained, PC1 corresponds to 55.5% and PC2 to 10.1%, thus, the two PCs together correspond to 65.6% of the total PCA model (Fig. 4 below). As showed in Fig. 4 , the PC1 was able to separate the samples by Coffea species. On the left side of PC1 are the C. arabica samples, while the C. canephora samples stand on the right side. Furthermore, PC2 distinguished C. canephora samples in the two varieties, in the first quadrant we see samples of the conilon variety (light green triangles), and in the fourth quadrant the samples of the robusta variety (orange squares) are observed. According to the results, there is evidence to support that coffees’ genetic factor has a great influence on the production of secondary metabolites, and that the genetic variability between C. canephora var. robusta and C. canephora var. conilon is higher than the genetic variability among C. arabica varieties (Souza et al., 2013 ; Ky et al., 2001 ). In accordance with Akpertey et al. ( 2022 ), the conilon and robusta varieties are considered two heterotic groups with distinct and complementary characteristics within the C. canephora species. Furthermore, using the technique of single nucleotide polymorphisms of molecular markers, Alkimim et al. ( 2018 ) showed higher genetic distance between groups of conilon and robusta ( C. canephora ) than among varieties within C. arabica species (Akpertey et al., 2022 ; Alkimim et al., 2018 ). In the case of C. canephora , allogamy and gametophytic self-incompatibility are responsible for the high heterogeneity and genetic variability of the species (Machado et al., 2022 ). It is important to mention that one of the samples behaved as an outlier in the statistical analysis (i.e., the individuals are not contained within the region of the Hotelling T 2 ellipse). As expected, it was the case of the commodity coffee represented by magenta stars in Fig. 4 . This coffee was not scored as highly as the others by the professional tasters (final score bellow 80 points) and, consequently, it was distinguished as a commodity coffee (a uniform product that is interchangeable with other regular coffees). Similar to other studies (see Maeztu et al., 2001 ; Bressanello et al., 2021 ) this result evidences that the sensory evaluation of professional tasters can be associated with the coffee’s metabolomic profile, once the commodity coffee does not present the grain quality and flavor (metabolites) that were responsible for a higher quality in other samples. In this case, a loading analysis of the PCA (Supplementary Material, Figure S1) was proposed. The presence of possible markers was verified in the groupings shown by PC1 and PC2. The analysis is performed by superimposing the quadrants of the score and the loading plot to correlate the clusters with the metabolites. With the value of the loading plot on the axes, it is possible to verify which substances are more relevant to the observed clusters. Based on PC1 data, caffeine, DIMBOA-Gl, roemerine, and cajanin were determined as markers for C. canephora samples, while toralactone, cnidilide, LysoPC(18:2(9Z,12Z)), lysophosphatidylcholine(16:0/0:0), and 2,3-dehydrosilybin for C. arabica samples. Next, a supervised analysis by OPLS-DA was carried out with the purpose of classifying the coffee samples and explore a possible terroir effect by identifying what substances were correlated in the formation of clusters. As shown by Vezzulli et al. ( 2022 ), production practices and environmental conditions can influence the plant’s organoleptic properties. In the case of coffee, the terroir is the result of the unique combination of the Coffea species and variety planted, environmental and agricultural parameters, and the harvest and post-harvest methods employed (Lucini et al., 2020 ; Williams et al., 2022 ). The OPLS-DA using positive ionization mode on LC-MS is presented in Fig. 5 . The OPLS-DA model in Fig. 5 shows a very high goodness-of-fit (R 2 X = 0.929; R 2 Y = 0.841 and Q 2 = 0.720). R 2 Y and Q 2 parameters higher than 0.5 and close to 1.0 revealed the high correlation (R 2 Y) and predictive power (Q 2 ) of the model. Validation of the OPLS-DA models was carried out through the obtention of a significant CV-ANOVA p-value (p-value = 1.00 × 10 − 11 and p-value = 2.77 × 10 − 26 respectively for the OPLS-DA model in Fig. 5 ) and the absence of overfitting was confirmed by permutation tests (200 permutations). Figure S2 (see the Supplementary Material) present the permutation test model for the OPLS-DA using LC-MS positive mode of ionization. Based on the OPLS-DA score plot illustrated in Fig. 5 , it is possible to group individuals according to the metabolic fingerprints of their respective terroir . Once again, the commodity coffee sample is identified as an outlier. Different from the C. arabica variety (left side of Fig. 5 ), whose groups were close to each other, the groups of the C. canephora variety (right side of Fig. 5 ) are located far away from each other, occupying two different sections. Coffee is a climate-sensitive perennial crop likely to be susceptible to changes in climate, soil, hydric, altitude, and human conditions (Pham et al., 2019 ). On the purpose of identifying the impact of terroir in both species, two separate analyses were conducted, one solely with the C. arabica samples, while the other, only with C. canephora samples. The C. arabica samples were proven to be chemically distinct among themselves. Figure 6 illustrates the results obtained from the OPLS-DA analysis performed only with the C. arabica specie. The OPLS-DA score plot in Fig. 6 shows independent clusters of C. arabica coffee that could be due to terroir effects of the different Brazilian regions. Despite acknowledging that a complete discussion about all possible factors comprising the terroir of each region is virtually impossible, we examined some important aspects of it. Starting with soil composition, the coffee cultivation in Goiás, Minas Gerais and São Paulo lie on a latosol base, relatively high in iron and aluminum oxides (do Amaral et al., 2004 ; IAC, 2023). The C. arabica coffee samples of the Brazilian state of Goiás (blue hexagons) come from the agricultural area of the city of Cristalina, in the east portion of the state. This area lies in a highland above 1,000 m of altitude, in a biome known as “cerrado” or Brazilian savannah. The climate of this region varies according to the altitude, from the tropical Aw climate to the subtropical Cwa and Cwb climates according to the Köppen climate classification. Because of the severe droughts and high temperatures, much of the vegetation is of tall bushes and small trees with twisted branches scattered among carpets of grasses (Almeida et al., 2005 ). The C. arabica coffee production in Minas Gerais (orange squares in Fig. 6 ) is carried out in higher altitudes (ranging from 700 to 1,100 m) and in a variation of the “cerrado” climate with temperatures averaging between 22 and 27°C. This coffee production region is known as “Cerrado Mineiro” (Cerrado Mineiro, 2023 ). The samples from São Paulo come from the Alta Mogiana region (purple stars in Fig. 6 ), which lies above 800 m of altitude with milder weather and excellent thermal and hydric conditions for coffee flowering and growth (de Souza Rolim et al., 2020 ). According to the Köppen climate classification, the climate in Alta Mogiana varies between subtropical climates (Cwa and Cwb) with a quite narrow annual temperature averaging between 18 and 24°C. The flora is mostly of tropical forest, reminiscent of the Atlantic Forest (de Souza Rolim et al., 2020 ). Alta Mogiana and the Cerrado Mineiro were registered as Brazilian geographical indications (GI). The GIs are collectively owned and act as a marketing tool in product branding and differentiation (important assets to fight price fluctuations) (Cassago et al., 2021 ; Cerrado Mineiro, 2023 ). The Mexican C. arabica coffee samples originated in the southern state of Chiapas (dark green circles and magenta pentagon in Fig. 6 ). The coffees analyzed are cultivated in the rural areas of Santa Cruz and Jaltenango cities, which are only 150 km apart. However, the samples constitute two independent groups in the OPLS-DA score plot, meaning that their chemical composition differs. The landscape of Chiapas is mostly composed of mountains and highlands, which interfere in wind, temperature, and cloud conditions (Ochoa-Gaona & González-Espinosa). This means that even neighbor cities may present completely different edaphoclimatic features and, consequently, agronomy practices (Cassago et al., 2021 ). Similarly, differences in the metabolomic profiles of the C. canephora coffee samples were observed in results of the OPLS-DA analysis using only C. canephora samples. Results are illustrated in Fig. 7 . Different from the C. arabica samples, two distinct C. canephora varieties ( robusta and conilon ) were analyzed. Hence, besides the aforementioned genetic effects, the two Brazilian federation states where these coffees were produced are located in regions with completely different environmental and human conditions, which indicate a possible terroir effect in the formation of metabolites (Cassago et al., 2021 ; Artêncio, Casssago, et al., 2022; Williams et al., 2022 ). Both regions are registered as geographical indications (Ministério da Agricultura e Pecuária, 2022 ). Precisely, the C. canephora var. robusta coffee samples (orange squares in Fig. 7 ) from the state of Rondônia are cultivated in a transition area between two morphoclimatic zones, the Amazonian and the Cerrado, a tropical rainforest and savannah climates, respectively (Marques et al., 2020 ). In this area, coffee is cultivated in latosol, acrisol and nitisol soils, all found in tropical regions with differences in their contents, particularly clay (IUSS Working Group WRB, 2015 ). Coffee cultivation in Rondônia is situated in North of Brazil, while coffee production in Espírito Santo is located in Southeast, in the Brazilian Atlantic coast, in a morphoclimatic zone called “Mares de Morros”, where a subtropical highland variety of the oceanic climate and tropical flora prevails (Ab’Sáber, 2012). The predominant soil in Espírito Santo is the red or yellowish-colored oxisol, which presents a high concentration of minerals such as iron and aluminum (de Cunha et al., 2016 ). In addition to the natural differences mentioned, the cultivation practices applied to the coffee samples also vary among the states. The C. canephora var. robusta of Rondônia is cultivated around the natural vegetation of the West portion of the Amazonian Forest, in a sustainable agroforestry system. The C. robusta samples used in our analysis were cultivated by indigenous producers of the Cinta-Larga, Tupari e Suruí ethnicities, whose cultivation practices focus on sustainability and involve a close relationship with the environment. The engagement of indigenous people is essential for social inclusion and prosperity of local communities in Brazilian agriculture and food market. On the other hand, the production of C. canephora var. conlion coffee in Espírito Santo is carried out in medium and large family properties, with the use of mechanization and different irrigation systems to prevent bean development and harvest from draught (Venancio et al., 2020 ). Coffee is the primary agricultural product of Espírito Santo and the main contributor to employment opportunities (Incaper, 2023 ). These metabolomic differences impact the sensory perception of foods and beverages (Cassago et al., 2021 ; Artêncio et al., 2023 ). For instance, although professional tasters used the broader term “sweet” to describe all coffees from Minas Gerais (represented by the purple pentagons in Fig. 6 ), differences in the use of more specific flavor descriptors were observed. In this case, some samples were described as “caramelly” and “honey” while others as “chocolatey”. In the case of C. canephora , the flavor nuances sensed in the C. canephora var. robusta coffee from Rondônia were described as “fine wine” and “buttery”; while the flavor notes of the C. canephora var. conlion variety from Espírito Santo were defined using terms like “dry berries” and “bright”. It is also worth mentioning that the distance between the commodity coffee and the other groups regardless of species and variety, is reflected in sensory quality. In the OPLS-DA, the value of importance that each variable must explain X and its correlation with Y is called VIP (Variable Importance in the Projection). VIP values greater than 1.0 are more relevant to explain the answer (Y), as they provide greater reliability in determining the most important variables in separating groups (SIMCA-P Manual 13.0.3). Based on the OPLS-DA analysis carried out with the coffee sample extracts (Fig. 5 ) it allowed us to determine the variables correlated to the clusters, based on their VIP values. Based on the OPLS-DA loading plot (Figure S3, Supplementary Material) and the samples’ VIP values, the variables (substances of the TIC) that most influenced the formation of groups were identified. Moreover, the variables that presented a VIP value greater than 1.0 were determined as possible markers for clusters. Table 2 details the variables that were identified and determined as markers for different species of Coffea . Table 2 Identified variables (compounds) responsible for the groupings in the OPLS-DA analysis of all coffee samples and their respective VIP values. Compound VIP caffeine 4.72583 chlorogenic acid 3.33315 HMBOA-Glc 2.28673 LysoPC(18:2(9Z,12Z)) 2.1005 2,3-Dehydrosilybin 1.84786 3-Indoleacetic acid 1.73559 4-methoxybenzyl glucoside 1.66728 3,3',4',5,5',8-Hexahydroxyflavone 1.63826 toralactone 1.53175 2-(hydroxymethyl)-4(3H)-quinazolinone 1.51197 calystegine A6 1.29546 2-O-glucopyranosyl-4-hydroxy-7-methoxy-1,4-benzoxazin-3-one 1.28939 cnidilide 1.22662 lysophosphatidylcholine(16:0/0:0) 1.19352 (R)-roemerine 1.19333 cajanin 1.04053 [Table 2 ] According to the results of the OPLS-DA analysis, caffeine is one of the main markers to group C. canephora samples. Whereas, for the C. canephora var. robusta , the most relevant markers are roemerine, cajanin, and 2-(hydroxymethyl)-4(3H)-quinazolinone. In the case of C. canephora var. conilon , calystegine A6, DIMBOA-Glc and HMBOA-Glc are the main markers in group formation. Finally, for the C. arabica samples, the compounds most responsible for the formation of groups are toralactone, cnidilide, LysoPC(18:2(9Z,12Z)), lysophosphatidylcholine(16:0/0:0), and 2,3-edhydrosilybin. 4. Conclusions The main aim of this work was to analyze the metabolic profiles of coffee samples collected during the “Brazilian International Conference of Coffee Tasters” (2022 Edition) using LC-MS-based metabolomics. Our data showed that the secondary metabolites have wherewithal to discriminate the samples by species and variety. In accordance with previous studies, secondary metabolites enabled the acknowledgement of genetic and terroir influences, especially in the case of C. canephora samples cultivated in different Brazilian regions. Hence, besides the taxonomic and morphologic characteristics that differentiate the species C. arabica and C. canephora , metabolomic studies can help producers and researchers in differentiating coffees according to their chemical fingerprints, which would impact positively in the promotion of new specialty coffees and, consequently, the socioeconomic conditions of producers. Declarations Conflicts of interest: on behalf of all authors, the corresponding author declares that there is no conflict of interest. Availability of data and material: data is available at Supplementary Material. Code availability: not applicable. Authors' contributions: all the authors have read, approved, and made substantial contributions for the manuscript. Additional declarations for articles in life science journals that report the results of studies involving humans and/or animals: not applicable. Ethics approval: not applicable. Consent to participate: not applicable. Consent for publication: We, the author and co-authors, state that this article is original and has not been submitted for publication in any other journal, whether in part or in its entirety. We assign the copyright of the referred article to European Food Research and Technology as well as the consent for publication. Acknowledgements The authors thank the following organizations for the support during the First Brazilian Meeting of Coffee Tasters: EMBRAPA Rondônia, Federal Institute of Espirito Santo (IES), and the Association of the Region of the Forests of Rondônia (Caferon). Funding information This work was supported by the National Council for Scientific and Technological Development (CNPq, grant #308141/2019-9), and the Coordination for the Improvement of Higher Education Personnel (CAPES, Financial Code 001). Data availability statement The data that support the findings of this study are available from the corresponding author upon reasonable request. 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Proposed minimum reporting standards for chemical analysis. Metabolomics , 3(3), 211-221. https://doi.org/10.1007/s11306-007-0082-2 Williams, S. D., Barkla, B. J., Rose, T. J., & Liu, L. (2022). Does coffee have terroir and how should it be assessed? Foods , 11(13), 1907. https://doi.org/10.3390/foods11131907 van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC genomics , 7, 142. https://doi.org/10.1186/1471-2164-7-142 van Leeuwen, C., & Seguin, G. (2006). The concept of terroir in viticulture. Journal of Wine Research , 17(1), 1-10. https://doi.org/10.1080/09571260600633135 Venancio, L. P., Filgueiras, R., Mantovani, E. C., do Amaral, C. H., da Cunha, F. F., dos Santos Silva, F. C., ... & Cavatte, P. C. (2020). Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Espírito Santo State, Brazil. Scientific Reports , 10(1), 19719. Vezzulli, F., Rocchetti, G., Lambri, M., & Lucini, L. (2022). Metabolomics Combined with Sensory Analysis Reveals the Impact of Different Extraction Methods on Coffee Beverages from Coffea arabica and Coffea canephora var. robusta . Foods , 11(6), 807. https://doi.org/10.3390/foods11060807 WIPO (World Intellectual Property Organization) (2021). Geographical Indications: an introducion. https://www.wipo.int/edocs/pubdocs/en/wipo_pub_952_2021.pdf. Accessed 29 March 2023. Zidorn, C. (2019). Plant chemophenetics − A new term for plant chemosystematics/plant chemotaxonomy in the macro-molecular era. Phytochemistry , 163, 147-148. https://doi.org/10.1016/j.phytochem.2019.02.013 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2828021","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":193165119,"identity":"8d601e1d-cb6d-413a-914d-4c05a56565d8","order_by":0,"name":"Mateus Manfrin Artêncio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHADxgcMCQw2IEbjAfwqmeEMA6CWNJCWBhK0MDAcBjPxatFtP3/wMc8fhmj+2c1sHx7uOG+3tv0w0JYam2hcWszOJDMb87Yx5M64c5h5RuKZ28nbziQCtRxLy23ApeVAMps0bwNDbsON/MMMiW23k80OALUwNhzGreX8Y/bfQIflzr+RzAzUci7Z7PxDAlpuJLMx87Ax5G6AaDlgZ3aDkC03HhtLzm2TyN0I0ZKcYHYDaEsCPr+cT3z44c0fm9x5QC2MP9vs7M3Opz988KHGBqcWKJCAsxLBKhPwK0cF9qQoHgWjYBSMgpEBAM7EY8q2SUObAAAAAElFTkSuQmCC","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":true,"prefix":"","firstName":"Mateus","middleName":"Manfrin","lastName":"Artêncio","suffix":""},{"id":193165122,"identity":"84965799-772f-4667-9e75-f0ee6a7aa216","order_by":1,"name":"Alvaro Luis Lamas Cassago","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Alvaro","middleName":"Luis Lamas","lastName":"Cassago","suffix":""},{"id":193165124,"identity":"90ddc2b3-407a-4af0-8b42-8c93dbf0dab0","order_by":2,"name":"Renata Kelly Silva","email":"","orcid":"","institution":"Embrapa - Brazilian Agricultural Research Corporation","correspondingAuthor":false,"prefix":"","firstName":"Renata","middleName":"Kelly","lastName":"Silva","suffix":""},{"id":193165126,"identity":"b9e18264-07a1-4e29-9cce-50e8cc18a6cd","order_by":3,"name":"Janaina de Moura Engracia Giraldi","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Janaina","middleName":"de Moura Engracia","lastName":"Giraldi","suffix":""},{"id":193165127,"identity":"81523066-a785-449a-b6c6-a455d5a0aaa1","order_by":4,"name":"Fernando Batista Da Costa","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"Batista Da","lastName":"Costa","suffix":""}],"badges":[],"createdAt":"2023-04-17 15:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2828021/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2828021/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":36145685,"identity":"a41a9e0e-0528-4980-ab7a-22fca67b1411","added_by":"auto","created_at":"2023-04-21 23:11:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2015358,"visible":true,"origin":"","legend":"\u003cp\u003eIndividuals representing \u003cem\u003eC. arabica\u003c/em\u003e (A) and \u003cem\u003eC. canephora\u003c/em\u003e (B). Beans from both coffees species (C), on the right a roasted \u003cem\u003eC. arabica\u003c/em\u003e bean, and on the left a roasted \u003cem\u003eC. canephora \u003c/em\u003ebean.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2828021/v1/acc6ab471a0d7c5f286831c3.png"},{"id":36145687,"identity":"d2c6fdbe-105e-4ad0-9fe0-c4308bd9709a","added_by":"auto","created_at":"2023-04-21 23:11:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1732749,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the coffee analysis carried out using UHPLC-ESI-HRMS.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2828021/v1/f39d443188f2df48427f0cb2.png"},{"id":36146468,"identity":"0bedebf3-3dae-4634-aa1a-9e0ade9a976f","added_by":"auto","created_at":"2023-04-21 23:19:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":359397,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the samples’ geographical origins. (1) Goiás, (2)\u003c/p\u003e\n\u003cp\u003eEspírito Santo, (3) São Paulo, (4) Minas Gerais, (5) Rondônia, and (6) Chiapas.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2828021/v1/59b75c9076d309d843dd7699.png"},{"id":36146465,"identity":"fb8c9f4b-7041-4a57-b932-3f672c655f5c","added_by":"auto","created_at":"2023-04-21 23:19:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":130766,"visible":true,"origin":"","legend":"\u003cp\u003ePCA 2D plot (R\u003csup\u003e2\u003c/sup\u003e = 0.895 and Q\u003csup\u003e2 \u003c/sup\u003e= 0.659) based on metabolic fingerprinting in positive ionization mode of the two \u003cem\u003eCoffea\u003c/em\u003e species from different regions analyzed by UHPLC-ESI-HRMS.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2828021/v1/501ece77dd34c4fc4ac543d0.png"},{"id":36147418,"identity":"c771e575-e94a-4af5-b394-d27fea7cb9fd","added_by":"auto","created_at":"2023-04-21 23:27:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105919,"visible":true,"origin":"","legend":"\u003cp\u003eOPLS-DA 2D plot based on metabolic fingerprinting in positive ionization mode of the two \u003cem\u003eCoffea\u003c/em\u003especies from different regions analyzed by UHPLC-ESI-HRMS.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2828021/v1/b72c922d9f0f72e577848700.png"},{"id":36145682,"identity":"8a587c07-e82e-4981-b67c-429dbd04ae62","added_by":"auto","created_at":"2023-04-21 23:11:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":94233,"visible":true,"origin":"","legend":"\u003cp\u003eOPLS-DA 2D plot (R\u003csup\u003e2\u003c/sup\u003eX = 0.927, R\u003csup\u003e2\u003c/sup\u003eY = 0.952 and Q\u003csup\u003e2\u003c/sup\u003e = 0.836) based on metabolic fingerprinting in positive ionization mode of the \u003cem\u003eC. arabica\u003c/em\u003e specie from different regions analyzed by UHPLC-ESI-HRMS.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2828021/v1/5e7d2d5611007f9f9abfd607.png"},{"id":36147417,"identity":"c891eef8-d946-443c-9bf3-705875671c35","added_by":"auto","created_at":"2023-04-21 23:27:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":86057,"visible":true,"origin":"","legend":"\u003cp\u003eOPLS-DA 2D plot (R\u003csup\u003e2\u003c/sup\u003eX = 0.842, R\u003csup\u003e2\u003c/sup\u003eY = 0.975 and Q\u003csup\u003e2\u003c/sup\u003e = 0.950) based on metabolic fingerprinting in positive ionization mode of \u003cem\u003eC. canephora\u003c/em\u003e from three different regions analyzed by UHPLC-ESI-HRMS.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-2828021/v1/a0f4189bd927d741add91838.png"},{"id":36675007,"identity":"0317e499-eeb2-4ed9-8775-272661596ff9","added_by":"auto","created_at":"2023-05-06 19:59:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3730765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2828021/v1/530a42b1-319c-4894-bdd5-4d8337ad64c1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Untargeted metabolomic approach based on UHPL-ESI-HRMS to investigate metabolic profiles of different Coffea species and terroir","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoffee (\u003cem\u003eCoffea\u003c/em\u003e spp) is one of the most consumed beverages in the world, a food product that has evolved from an agricultural commodity to a specialty beverage (ICO, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Cenci, Combes \u0026amp; Lashermes (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), the genus \u003cem\u003eCoffea\u003c/em\u003e L. is relatively new when compared to other genera, and it includes around a hundred of species adapted to distinct environments. Coffee cultivated in the Brazilian Amazon Rain Forest, Ethiopia\u0026rsquo;s Kafa region, and in the Spanish Canary Islands are examples of the adaptability of the genus \u003cem\u003eCoffea\u003c/em\u003e in different continents.\u003c/p\u003e\n\u003cp\u003eThe international coffee trade and industry based its activities mostly on two different cultivars: \u003cem\u003eCoffea arabica\u003c/em\u003e L. and \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003erobusta\u003c/em\u003e (L. Linden) A. Chev. (Vezzulli et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Their differences reside on a market/consumer and biological standpoint. For instance, all species belonging to \u003cem\u003eCoffea\u003c/em\u003e spp are diploid, except those of \u003cem\u003eC. arabica\u003c/em\u003e (Lashermes et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). Specifically, \u003cem\u003eC. arabica\u003c/em\u003e has an allotetraploid genome and is originated from the hybridization of the diploid species \u003cem\u003eC. eugenioides\u003c/em\u003e S. Moore and \u003cem\u003eC. canephora\u003c/em\u003e Pierre ex A. Froehner (Lashermes et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). Regarding \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003erobusta\u003c/em\u003e, prior research that focused on coffees\u0026rsquo; enzymatic activity and expression showed that this variety accumulates less saccharose than the \u003cem\u003eC. arabica\u003c/em\u003e, which is an important precursor of coffee\u0026rsquo;s taste (Privat et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). Moreover, they present different biochemical ripening processes, and undergo distinct post-harvesting processes (Vezulli et al., 2022). Such physical characteristics are shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eRegarding the consumer perspective, differences reside on the organoleptic properties of the beverage (e.g., flavor, aroma, body, mouthfeel, aftertaste, etc.). Usually, consumers prefer coffees brewed with \u003cem\u003eC. arabica\u003c/em\u003e beans, because of its taste and high acidity (Perrois et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Distinctively, \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003erobusta\u003c/em\u003e is considered by consumers more bitter and intense (Perrois et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). In short, differences concerning the genetic, biochemical, origin-related and sensory aspects of these two species end up being important sources of diversity in coffees\u0026rsquo; chemical profiles, flavors and quality (Lucini, Rocchetti \u0026amp; Trevisan, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Art\u0026ecirc;ncio, Giraldi \u0026amp; de Oliveira, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eChanges in consumer demand for coffee have transformed market configurations, particularly with consumers\u0026rsquo; praise for high quality and differentiation, which pushed coffee producers and brands to offer unique sensory characteristics (Guimar\u0026atilde;es et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Belchior et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to Cassago et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), the origin information of coffee (place, region or country of origin) plays an important role in quality communication and product differentiation once it evokes the reputation and image built around the natural features (e.g., climate, soil, altitude, flora, and fauna) and traditions regarding coffee production of a specific geographical origin. Past studies showed that the origin information could impact the expectation and sensory perception of amateur and professional tasters (Art\u0026ecirc;ncio, Giraldi, et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Art\u0026ecirc;ncio et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe term \u003cem\u003eterroir\u003c/em\u003e, although often related to high-quality wines, is also used in the coffee industry to summarize the complexity of factors and interactions (human and natural) deeply intertwined with the very essence of specialty coffees (van Leeuwen \u0026amp; Seguin, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). When the nature and quality of a good is essentially due to the place of origin (i.e., due to \u003cem\u003eterroir\u003c/em\u003e), it can be registered as a geographical indication (GI) to differentiate from competing goods (WIPO, 2021). This basic concept underlying GIs is simple and familiar to any consumer who chooses Roquefort over blue cheese or Champagne over other sparkling wines, some well-known examples of names associated throughout the world with products having characteristics linked to that origin (Art\u0026ecirc;ncio et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAs a response to the advances on the sensory complexion and differentiation of food, analytical techniques have been applied to evaluate their quality and composition, such as metabolomics (Rochetti et al., 2020; Gigl et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The purpose of this set of techniques is to assess the correlation between genotype and phenotype profiles of a biological system, which are influenced by genetic and environmental change (Cassago et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the case of coffee, post-harvest processes (e.g., roasting and storage) and brewing methods are included as important sources of sensory variations (Vezzulli et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Aresta \u0026amp; Zambonin, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, metabolomics techniques can be applied to determine and authenticate coffee origin (Jumhawan et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Giraudo et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), identify sensory quality markers (substances) in coffee (Rochetti et al., 2020), or discriminate coffee brewed by different methods (Bravo et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Grassi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOnce the final sensory experience with coffee is a result of primary and secondary metabolites, prior research has explored their influence on quality, particularly in terms of acidity, body, and sweetness, which are considered important elements of coffee quality (SCA, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; da Rosa et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). For instance, coffees\u0026rsquo; bitterness is closely linked to the presence of caffeine (da Rosa et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and chlorogenic acids (Schenker \u0026amp; Rothgeb, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), while its acidity is originated from quinic, citric and malic acids (Koshiro et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo the best of our knowledge, only a few studies in the literature tried to correlate the chemical composition of ground coffee to its sensorial quality. Recently, Rochetti et al. (2020) applied metabolomics to identify a group of chemical markers that allow the correlation between the chemical profile of powdered coffee with its sensory scores, obtained on a tasting competition of \u003cem\u003eC. arabica\u003c/em\u003e and \u003cem\u003eC. canephora\u003c/em\u003e coffees. Despite considering over 300 coffee origins collected during the competition, the authors have not included coffees from South America, where some important coffee producing countries are located such as Brazil, Colombia, and Peru (ICO, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Besides, the authors have not analyzed one important commercial variety of \u003cem\u003eC. canephora\u003c/em\u003e, the \u0026ldquo;conilon\u0026rdquo; variety.\u003c/p\u003e\n\u003cp\u003eHence, in this work we carried out a metabolomic study based on ultra-efficient liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-ESI-HRMS) using Brazilian ground coffees obtained from the first \u0026ldquo;Brazilian International Conference of Coffee Tasters\u0026rdquo;, which was coordinated by the Brazilian Agricultural Research Corporation (Embrapa), Federal Institute of Espirito Santo (IES), and the Association of the Region of the Forests of Rond\u0026ocirc;nia (Caferon). As suggested in the recently published work of Cassago et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), this work presents a contribution of metabolomic data in describing a place\u0026rsquo;s \u003cem\u003eterroir\u003c/em\u003e. Originally, in this study samples of coffee grown in most of various Brazilian producing regions were analyzed, including different Brazilian Geographical Indications (GIs) and the Amazon region. Specifically, our main goals were (1) to carry out untargeted metabolomics to investigate the metabolic profiles of two different \u003cem\u003eCoffea\u003c/em\u003e species and their varieties (\u003cem\u003eC. arabica\u003c/em\u003e, \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003erobusta\u003c/em\u003e and \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003econilon\u003c/em\u003e) and (2) to correlate coffee metabolic profiles with their species, geographic origin, and \u003cem\u003eterroir\u003c/em\u003e. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows how the processes for the analyses were carried out.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Coffee samples\u003c/h2\u003e\n \u003cp\u003eSamples consisted of ground coffee obtained in the \u0026ldquo;Brazilian International Conference of Coffee Tasters\u0026rdquo;. In total, 21 coffee samples were used in this study: nine of \u003cem\u003eC. arabica\u003c/em\u003e, six of \u003cem\u003eC. canephora\u003c/em\u003e var. robusta, and four of \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003econilon\u003c/em\u003e from Brazil and two samples of \u003cem\u003eC. arabica\u003c/em\u003e from Mexico. Figure 3 illustrates the origin of all coffee samples used in the study.\u003c/p\u003e\n \u003cp\u003eAlmost all coffees are considered specialty coffees (except for one, referred here as commodity coffee), meaning that professional coffee tasters have scored with more than 80 points using the Specialty Coffee Association (SCA) protocols and best practices in cupping and grading (SCA, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The green coffee beans were roasted in a Probatino roaster (Probat Inc. Germany), based on the SCA recommendations (SCA, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) in order to achieve a light-medium roast profile (60\u0026ndash;55 Agtron units \u0026ndash; Agtron, Inc., Reno, NV). Information about samples (e.g., species, variety, origin) is available on Supplementary Material.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Preparation of extracts\u003c/h2\u003e\n \u003cp\u003eThe extraction procedure was based on the protocol for large-scale untargeted metabolomics of plant tissues reported by de Vos et al. (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), with some modifications supported by previous studies of the AsterBiochem research group (Cassago et. al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rocchetti et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). A total of 30 mg of powdered coffee from each sample were extracted with 1.5 mL of a methanol-water solution (7:3, v/v) in an ultrasonic bath (10 min at 25\u0026deg;C and 40 kHz). After the extraction, the samples were centrifuged at 13,000 rpm for 10 min. The supernatant was partitioned with 0.5 mL of heptane, and the aqueous layer was filtered through a 0.22 \u0026micro;m PTFE membrane filter. All solvents used were HPLC grade, and a new membrane filter was used for each extraction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. UHPLC-ESI-HRMS analysis\u003c/h2\u003e\n \u003cp\u003eThe extracts were analyzed with UHPLC-ESI-HRMS equipped with Accela 1250 quaternary pumps (Thermo Scientific, USA), which were coupled to a mass spectrometer with an Orbitrap analyzer synchronized using the software Xcalibur 2.2 (Thermo Scientific). The chromatographic separation was performed in a Kinetex XB-C18 column (1,7 \u0026micro;m, 150 \u0026times; 2,1 mm, Phenomenex, USA) connected to a compatible guard column in an oven (35 \u0026ordm;C). Water (A) and methanol (B), both with 0.1% formic acid were used as mobile phase (220 \u0026micro;L/min flow rate). The elution program was 5% B to 95% B in 34 min, isocratic 95% B until 38 min. Injection volume was 5 \u0026micro;L, in triplicate for each sample.\u003c/p\u003e\n \u003cp\u003eMass spectrometry detection was carried out in both positive and negative ionization modes using the fullscan (resolution of 70,000) method. Total ion current (TIC) chromatograms were obtained over the range of 100\u0026ndash;1,500 m/z using a spray voltage of +\u0026thinsp;3.5 and \u0026minus;\u0026thinsp;3.0 kV for the positive and negative ionization modes, respectively, and the capillary temperature of 320\u0026deg;C.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Data pre-processing and multivariate analysis\u003c/h2\u003e\n \u003cp\u003eData obtained from the UHPLC-ESI-HRMS analyses were pre-treated and pre-processed for the multivariate statistical analysis. Using ProteoWizard software (Proteowizard Software Foundation, USA) data were separated according to ionization modes (positive or negative) and converted to a .\u003cem\u003emzXML\u003c/em\u003e file. The data obtained for each ionization mode were processed with MZmine 2.37 (Pluskal et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) to identify: peak detection, peak filtering, chromatogram construction, chromatogram deconvolution, isotopic peak grouping, chromatogram alignment, gap filling, duplicate peaks filter, fragment search, and adducts and peak identities. The following MZmine parameters were used for data preprocessing: noise level at 1.0E5; Lorentzian function as peak shape algorithm (resolution of 70,000); minimum peak height at 5.0E5; \u003cem\u003em/z\u003c/em\u003e tolerance at 0.002 \u003cem\u003em/z\u003c/em\u003e or 5.0 ppm; and a retention time tolerance of 0.3 min. After pre-processing, data of both ionization modes were exported as .csv tables with rows representing plant extracts and columns representing ion peak areas associated with a given mass and retention time values. Before statistical analyses, peaks detected in the blank (extraction solvent) and the final stages of the chromatographic runs were removed from the original matrix.\u003c/p\u003e\n \u003cp\u003eMultivariate statistical analyses were carried out using the software SIMCA (v. 13.0.3.0, Umetrics, Sweden). First, an unsupervised multivariate statistical analysis (PCA) was performed. After this exploratory analysis, the supervised method orthogonal partial least squares - discriminant analysis (OPLS-DA) was used to identify the X variables (chromatographic peaks) which were correlated with the Y response variables (plant origin information and plant taxonomic information). In metabolomics studies, OPLS-DA can be applied to data classification with one or more classes (Cassago et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Padilla-Gonz\u0026aacute;lez et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gallon et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eFor data analysis, Pareto-type normalization was used (data scaling using the square root of the standard deviation) to normalize peak area values, corresponding to the X variables (van den Berg et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Additionally, the OPLS-DA model validation parameters (goodness-of-fit - R\u003csup\u003e2\u003c/sup\u003eY - together with goodness-of-prediction - Q\u003csup\u003e2\u003c/sup\u003eY) were checked. Thereafter, the OPLS-DA model was checked for outliers, CV-ANOVA p-value threshold\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for model significance, and a permutation testing (N\u0026thinsp;\u0026gt;\u0026thinsp;200) was performed to exclude model overfitting (Rocchetti et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The permutation plot shows the correlation coefficient between the original Y-variable and the permuted Y-variable on the X-axis versus the cumulative R\u003csup\u003e2\u003c/sup\u003e and Q\u003csup\u003e2\u003c/sup\u003e on the Y-axis, and then originates a regression curve, where the intercept is the measure of the overfitting.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Compounds\u0026rsquo; annotation and identification\u003c/h2\u003e\n \u003cp\u003eFollowing Freitas et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Cassago et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), the identification of metabolites by UHPLC-ESI-HRMS presented in this study is in accordance with the classification of identification levels used in prior metabolomics studies (Blaženović et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sumner et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), where: level 0\u0026thinsp;=\u0026thinsp;isolated compounds; level 1\u0026thinsp;=\u0026thinsp;identified compounds; level 2\u0026thinsp;=\u0026thinsp;annotated compounds; level 3\u0026thinsp;=\u0026thinsp;putative compounds; level 4\u0026thinsp;=\u0026thinsp;unknown compounds. Mass accuracy was calculated in Microsoft Excel (Microsoft Corp., USA) and tolerance was established at 5.0 ppm (Brenton and Godfrey, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor annotated compounds (level 2), an in-house database was built, which was used the following steps: i. data collection using SciFinder Scholar (Chemical Abstract Service, USA); ii. drawing of the chemical structures with MarvinSketch 18.24 (ChemAxon Ltd., Hungary); iii. and database creation, handling, and visualization with JChem for Excel (ChemAxon, Hungary), a plugin that transforms chemical structures in .\u003cem\u003emol\u003c/em\u003e format into their corresponding SMILES codes and calculates their exact masses (Cassago et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Freitas et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results And Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Chromatographic analyses\u003c/h2\u003e\n \u003cp\u003eThis work reports a chemical study on the secondary metabolites of coffee beans from two different species belonging to the genus \u003cem\u003eCoffea\u003c/em\u003e (\u003cem\u003eC. arabica\u003c/em\u003e and \u003cem\u003eC. canephora\u003c/em\u003e) and their varieties. Based on the analysis of the metabolic fingerprints, it is possible to observe that, despite some similarities, chromatograms of the two species and their respective varieties present important chemical differences.\u003c/p\u003e\n \u003cp\u003eTherefore, it becomes important to analyze the chemical fingerprints (metabolic profiles) of all species and varieties using multivariate statistical tools to launch new ideas on this subject. Zidorn (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) proposed the term \u0026ldquo;chemophenetics\u0026rdquo; to describe chemosystematic studies where the aim is to explain a matrix of natural products from a given taxon and use them for a phenetic characterization of clades found by methods based on DNA-sequence (Zidorn, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the literature, several studies have been using chemophenetic and chemotaxonomic approaches (de Brito et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), including different plant families such as Euphorbiaceae (Lan et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rivi\u0026egrave;re et al., 2012), Myrtaceae (Santos et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Asteraceae (Ccana-Ccapatinta et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the genus \u003cem\u003eAnanas\u003c/em\u003e (Cassago et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Cassago et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed in their work the metabolic profiles of several samples of \u003cem\u003eAnanas\u003c/em\u003e, and how their secondary metabolites were used to discriminate species and varieties from \u003cem\u003eAnanas\u003c/em\u003e using multivariate statistical analysis. Furthermore, the chemical composition of \u003cem\u003eAnanas\u003c/em\u003e leaves could be used to assist in the taxonomic classification of individuals belonging to this genus (Cassago et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOur study shows that there are notable differences in the chemical composition of the species and varieties in \u003cem\u003eCoffea\u003c/em\u003e ground beans. Based on results, it could be inferred that such metabolites may act as markers, which are responsible for the chemical differentiation of \u003cem\u003eCoffea\u003c/em\u003e species. Additional studies are needed to confirm this hypothesis, with additional analysis using multivariate statistics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Identified and annotated compounds\u003c/h2\u003e\n \u003cp\u003eAnnotations were made using the chemical structures library of \u003cem\u003eCoffea\u003c/em\u003e created from the compounds already described in the literature. The annotations were based on the comparison of the monoisotopic mass in high resolution; overall, 33 chemical structures were annotated. Via injections of pure compounds, using the same chromatographic methodology used for the coffee extracts, it was possible to identify two compounds: caffeine and \u003cem\u003etrans\u003c/em\u003e-chlorogenic acid.\u003c/p\u003e\n \u003cp\u003eThe identification of compounds in LC-MS-based metabolomics can be assessed using five confidence levels, where 0 is the highest and, therefore, compounds for which there is the most confidence in their identification, followed by the levels 1 to 4, with decreasing confidence levels (Blaženović et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Using this approach, it was possible to identify 31 compounds at level 2, and two at level 1.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows a list of annotated and identified compounds in the coffee extracts samples numbered according to their elution order and associated with their respective protonated molecules ([M\u0026thinsp;+\u0026thinsp;H]\u003csup\u003e+\u003c/sup\u003e) and adducts ([M\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;ACN]\u003csup\u003e+\u003c/sup\u003e, [M\u0026thinsp;+\u0026thinsp;NH\u003csub\u003e4\u003c/sub\u003e]\u003csup\u003e+\u003c/sup\u003e, and ([M\u0026thinsp;+\u0026thinsp;Na]\u003csup\u003e+\u003c/sup\u003e) (in positive mode), retention times (RT), tentative identification names, molecular formulae, and confidence levels.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCompounds identified in \u003cem\u003eCoffea\u003c/em\u003e extracts analyzed by UHPLC-ESI-HRMS with m/z values in positive ionization modes (and adducts), molecular formulae, retention times, tentative identification, and respective identification confidence levels.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003eAverage m/z\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003eAdduct type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eMOLECULAR FORMULA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003eRt (min)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003eTentative identification name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e206.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e15\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e1.77173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003edehydrosalsolidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e176.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e1.95744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e3-Indoleacetic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e220.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC₉H₁₇NO₅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e2.04989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003ecalcium (+)-pantothenate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e204.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e4.23221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e3-indolebutyric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e266.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e14\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eNO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e6.91403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003ecaffeoylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e181.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e10.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e2-(4-hydroxyphenyl)ethyl acetate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e183.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e11.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003eharman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e195.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eN\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e12.0071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003ecaffeine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e337.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;Na]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e17\u003c/sub\u003eH\u003csub\u003e14\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e14.2779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003ecirsimaritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e177.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e9\u003c/sub\u003eH\u003csub\u003e8\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e14.2906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e2-(hydroxymethyl)-4(3H)-quinazolinone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e177.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;NH4]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e7\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e14.2956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003ecalystegine A6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e211.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e11\u003c/sub\u003eH\u003csub\u003e18\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e14.3319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e\u003cem\u003eL\u003c/em\u003e,\u003cem\u003eL\u003c/em\u003e-Cyclo(leucylprolyl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e195.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e18\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e14.3978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003ecnidilide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e351.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;Na]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e22\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e16.0538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e3-(4-hydroxyphenyl)-3-oxopropyl beta-D-glucopyranoside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e499.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e26\u003c/sub\u003eN\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003eS\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e16.8039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e,\u003cem\u003eN\u0026rsquo;\u003c/em\u003e-Bis(gamma-glutamyl)cystine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e396.090666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;Na]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e19\u003c/sub\u003eNO\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e18.8455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003eDIMBOA-Glc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e351.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e18\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e19.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003eNa-\u003cem\u003ep\u003c/em\u003e-hydroxycoumaroyltryptophan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e179.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H\u0026thinsp;+\u0026thinsp;ACN]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e23.6158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e2-phenylacetamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e169.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e26.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003epyridoxamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e301.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e14\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e26.0942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e4-methoxybenzyl glucoside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e295.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;Na]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e27.4901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003etoralactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e493.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;Na]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e31\u003c/sub\u003eH\u003csub\u003e46\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e27.8341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003evitamin K1 epoxide-1,4-diol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e520.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eO\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e28.7922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e3,3\u0026rsquo;,4\u0026rsquo;,5,5\u0026rsquo;,8-hexahydroxyflavone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e520.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e26\u003c/sub\u003eH\u003csub\u003e48\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e28.8002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003elysoPC(18:2(9Z,12Z))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e324.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;NH4]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e18\u003c/sub\u003eH\u003csub\u003e17\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e28.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e(R)-roemerine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e496.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e24\u003c/sub\u003eH\u003csub\u003e50\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e29.5094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003elysophosphatidylcholine(16:0/0:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e257.12592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e29.6467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003eterostilbene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e323.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;Na]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e30.0606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003ecajanin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e353.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e18\u003c/sub\u003eO\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e30.1509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003ehlorogenic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e562.31439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e31\u003c/sub\u003eH\u003csub\u003e39\u003c/sub\u003eN\u003csub\u003e5\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e30.8672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003eergocorninine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e380.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;Na]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e19\u003c/sub\u003eNO\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e32.5027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003eHMBOA-Glc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e499.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;NH4]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e25\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e32.7455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003e2,3-dehydrosilybin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 2.3411%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.699%;\"\u003e\n \u003cp\u003e521.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.5975%;\"\u003e\n \u003cp\u003e[M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.7391%;\"\u003e\n \u003cp\u003eC\u003csub\u003e32\u003c/sub\u003eH\u003csub\u003e50\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 6.8004%;\"\u003e\n \u003cp\u003e33.1785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 42.3634%;\"\u003e\n \u003cp\u003etsugaric acid A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 4.4593%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e[Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003eWith the annotations from the \u003cem\u003eCoffea\u003c/em\u003e chemical structure library, caffeine was identified in all samples, which according to da Silva Portela et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) is a specific and typical compound found in coffee (da Silva Portela et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Also, a conjugate of hydroxycinnamic amino acids (Na-\u003cem\u003ep\u003c/em\u003e-hydroxy-coumaroyl-tryptophan) was identified. This chemical class has previously been reported as a potential marker to discriminate coffee cultivars (Asamenew et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In order to identify the distribution of compounds in the analyzed samples, multivariate statistic was performed on data obtained from LC-MS-based metabolomics. The analyses and related discussion are presented below.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Multivariate statistics\u003c/h2\u003e\n \u003cp\u003ePCA analysis was performed for the matrices obtained from the positive ionization mode from LC-MS, where the variable \u0026ldquo;region of production\u0026rdquo; was informed to the system to assist in the interpretation of the data. The matrix has samples of leaf extracts in rows (X variables) and data from areas of chromatographic bands are in columns (Y variables). Based on the data obtained, PC1 corresponds to 55.5% and PC2 to 10.1%, thus, the two PCs together correspond to 65.6% of the total PCA model (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e below).\u003c/p\u003e\n \u003cp\u003eAs showed in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the PC1 was able to separate the samples by \u003cem\u003eCoffea\u003c/em\u003e species. On the left side of PC1 are the \u003cem\u003eC. arabica\u003c/em\u003e samples, while the \u003cem\u003eC. canephora\u003c/em\u003e samples stand on the right side. Furthermore, PC2 distinguished \u003cem\u003eC. canephora\u003c/em\u003e samples in the two varieties, in the first quadrant we see samples of the \u003cem\u003econilon\u003c/em\u003e variety (light green triangles), and in the fourth quadrant the samples of the \u003cem\u003erobusta\u003c/em\u003e variety (orange squares) are observed. According to the results, there is evidence to support that coffees\u0026rsquo; genetic factor has a great influence on the production of secondary metabolites, and that the genetic variability between \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003erobusta\u003c/em\u003e and \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003econilon\u003c/em\u003e is higher than the genetic variability among \u003cem\u003eC. arabica\u003c/em\u003e varieties (Souza et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ky et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn accordance with Akpertey et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), the \u003cem\u003econilon\u003c/em\u003e and \u003cem\u003erobusta\u003c/em\u003e varieties are considered two heterotic groups with distinct and complementary characteristics within the \u003cem\u003eC. canephora\u003c/em\u003e species. Furthermore, using the technique of single nucleotide polymorphisms of molecular markers, Alkimim et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) showed higher genetic distance between groups of \u003cem\u003econilon\u003c/em\u003e and \u003cem\u003erobusta\u003c/em\u003e (\u003cem\u003eC. canephora\u003c/em\u003e) than among varieties within \u003cem\u003eC. arabica\u003c/em\u003e species (Akpertey et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Alkimim et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the case of \u003cem\u003eC. canephora\u003c/em\u003e, allogamy and gametophytic self-incompatibility are responsible for the high heterogeneity and genetic variability of the species (Machado et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIt is important to mention that one of the samples behaved as an outlier in the statistical analysis (i.e., the individuals are not contained within the region of the Hotelling T\u003csup\u003e2\u003c/sup\u003e ellipse). As expected, it was the case of the commodity coffee represented by magenta stars in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. This coffee was not scored as highly as the others by the professional tasters (final score bellow 80 points) and, consequently, it was distinguished as a commodity coffee (a uniform product that is interchangeable with other regular coffees). Similar to other studies (see Maeztu et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bressanello et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) this result evidences that the sensory evaluation of professional tasters can be associated with the coffee\u0026rsquo;s metabolomic profile, once the commodity coffee does not present the grain quality and flavor (metabolites) that were responsible for a higher quality in other samples.\u003c/p\u003e\n \u003cp\u003eIn this case, a loading analysis of the PCA (Supplementary Material, Figure S1) was proposed. The presence of possible markers was verified in the groupings shown by PC1 and PC2. The analysis is performed by superimposing the quadrants of the score and the loading plot to correlate the clusters with the metabolites. With the value of the loading plot on the axes, it is possible to verify which substances are more relevant to the observed clusters. Based on PC1 data, caffeine, DIMBOA-Gl, roemerine, and cajanin were determined as markers for \u003cem\u003eC. canephora\u003c/em\u003e samples, while toralactone, cnidilide, LysoPC(18:2(9Z,12Z)), lysophosphatidylcholine(16:0/0:0), and 2,3-dehydrosilybin for \u003cem\u003eC. arabica\u003c/em\u003e samples.\u003c/p\u003e\n \u003cp\u003eNext, a supervised analysis by OPLS-DA was carried out with the purpose of classifying the coffee samples and explore a possible \u003cem\u003eterroir\u003c/em\u003e effect by identifying what substances were correlated in the formation of clusters. As shown by Vezzulli et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), production practices and environmental conditions can influence the plant\u0026rsquo;s organoleptic properties. In the case of coffee, the \u003cem\u003eterroir\u003c/em\u003e is the result of the unique combination of the \u003cem\u003eCoffea\u003c/em\u003e species and variety planted, environmental and agricultural parameters, and the harvest and post-harvest methods employed (Lucini et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Williams et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The OPLS-DA using positive ionization mode on LC-MS is presented in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe OPLS-DA model in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows a very high goodness-of-fit (R\u003csup\u003e2\u003c/sup\u003eX\u0026thinsp;=\u0026thinsp;0.929; R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;=\u0026thinsp;0.841 and Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.720). R\u003csup\u003e2\u003c/sup\u003eY and Q\u003csup\u003e2\u003c/sup\u003e parameters higher than 0.5 and close to 1.0 revealed the high correlation (R\u003csup\u003e2\u003c/sup\u003eY) and predictive power (Q\u003csup\u003e2\u003c/sup\u003e) of the model. Validation of the OPLS-DA models was carried out through the obtention of a significant CV-ANOVA p-value (p-value\u0026thinsp;=\u0026thinsp;1.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e and p-value\u0026thinsp;=\u0026thinsp;2.77 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;26\u003c/sup\u003e respectively for the OPLS-DA model in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) and the absence of overfitting was confirmed by permutation tests (200 permutations). Figure S2 (see the Supplementary Material) present the permutation test model for the OPLS-DA using LC-MS positive mode of ionization.\u003c/p\u003e\n \u003cp\u003eBased on the OPLS-DA score plot illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, it is possible to group individuals according to the metabolic fingerprints of their respective \u003cem\u003eterroir\u003c/em\u003e. Once again, the commodity coffee sample is identified as an outlier. Different from the \u003cem\u003eC. arabica\u003c/em\u003e variety (left side of Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), whose groups were close to each other, the groups of the \u003cem\u003eC. canephora\u003c/em\u003e variety (right side of Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) are located far away from each other, occupying two different sections.\u003c/p\u003e\n \u003cp\u003eCoffee is a climate-sensitive perennial crop likely to be susceptible to changes in climate, soil, hydric, altitude, and human conditions (Pham et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the purpose of identifying the impact of \u003cem\u003eterroir\u003c/em\u003e in both species, two separate analyses were conducted, one solely with the \u003cem\u003eC. arabica\u003c/em\u003e samples, while the other, only with \u003cem\u003eC. canephora\u003c/em\u003e samples.\u003c/p\u003e\n \u003cp\u003eThe \u003cem\u003eC. arabica\u003c/em\u003e samples were proven to be chemically distinct among themselves. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the results obtained from the OPLS-DA analysis performed only with the \u003cem\u003eC. arabica\u003c/em\u003e specie.\u003c/p\u003e\n \u003cp\u003eThe OPLS-DA score plot in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows independent clusters of \u003cem\u003eC. arabica\u003c/em\u003e coffee that could be due to \u003cem\u003eterroir\u003c/em\u003e effects of the different Brazilian regions. Despite acknowledging that a complete discussion about all possible factors comprising the \u003cem\u003eterroir\u003c/em\u003e of each region is virtually impossible, we examined some important aspects of it.\u003c/p\u003e\n \u003cp\u003eStarting with soil composition, the coffee cultivation in Goi\u0026aacute;s, Minas Gerais and S\u0026atilde;o Paulo lie on a latosol base, relatively high in iron and aluminum oxides (do Amaral et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; IAC, 2023). The \u003cem\u003eC. arabica\u003c/em\u003e coffee samples of the Brazilian state of Goi\u0026aacute;s (blue hexagons) come from the agricultural area of the city of Cristalina, in the east portion of the state. This area lies in a highland above 1,000 m of altitude, in a biome known as \u0026ldquo;cerrado\u0026rdquo; or Brazilian savannah. The climate of this region varies according to the altitude, from the tropical Aw climate to the subtropical Cwa and Cwb climates according to the K\u0026ouml;ppen climate classification. Because of the severe droughts and high temperatures, much of the vegetation is of tall bushes and small trees with twisted branches scattered among carpets of grasses (Almeida et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe \u003cem\u003eC. arabica\u003c/em\u003e coffee production in Minas Gerais (orange squares in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) is carried out in higher altitudes (ranging from 700 to 1,100 m) and in a variation of the \u0026ldquo;cerrado\u0026rdquo; climate with temperatures averaging between 22 and 27\u0026deg;C. This coffee production region is known as \u0026ldquo;Cerrado Mineiro\u0026rdquo; (Cerrado Mineiro, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe samples from S\u0026atilde;o Paulo come from the Alta Mogiana region (purple stars in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), which lies above 800 m of altitude with milder weather and excellent thermal and hydric conditions for coffee flowering and growth (de Souza Rolim et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to the K\u0026ouml;ppen climate classification, the climate in Alta Mogiana varies between subtropical climates (Cwa and Cwb) with a quite narrow annual temperature averaging between 18 and 24\u0026deg;C. The flora is mostly of tropical forest, reminiscent of the Atlantic Forest (de Souza Rolim et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAlta Mogiana and the Cerrado Mineiro were registered as Brazilian geographical indications (GI). The GIs are collectively owned and act as a marketing tool in product branding and differentiation (important assets to fight price fluctuations) (Cassago et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cerrado Mineiro, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Mexican \u003cem\u003eC. arabica\u003c/em\u003e coffee samples originated in the southern state of Chiapas (dark green circles and magenta pentagon in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The coffees analyzed are cultivated in the rural areas of Santa Cruz and Jaltenango cities, which are only 150 km apart. However, the samples constitute two independent groups in the OPLS-DA score plot, meaning that their chemical composition differs. The landscape of Chiapas is mostly composed of mountains and highlands, which interfere in wind, temperature, and cloud conditions (Ochoa-Gaona \u0026amp; Gonz\u0026aacute;lez-Espinosa). This means that even neighbor cities may present completely different edaphoclimatic features and, consequently, agronomy practices (Cassago et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSimilarly, differences in the metabolomic profiles of the \u003cem\u003eC. canephora\u003c/em\u003e coffee samples were observed in results of the OPLS-DA analysis using only \u003cem\u003eC. canephora\u003c/em\u003e samples. Results are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eDifferent from the \u003cem\u003eC. arabica\u003c/em\u003e samples, two distinct \u003cem\u003eC. canephora\u003c/em\u003e varieties (\u003cem\u003erobusta\u003c/em\u003e and \u003cem\u003econilon\u003c/em\u003e) were analyzed. Hence, besides the aforementioned genetic effects, the two Brazilian federation states where these coffees were produced are located in regions with completely different environmental and human conditions, which indicate a possible \u003cem\u003eterroir\u003c/em\u003e effect in the formation of metabolites (Cassago et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Art\u0026ecirc;ncio, Casssago, et al., 2022; Williams et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Both regions are registered as geographical indications (Minist\u0026eacute;rio da Agricultura e Pecu\u0026aacute;ria, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003ePrecisely, the \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003erobusta\u003c/em\u003e coffee samples (orange squares in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) from the state of Rond\u0026ocirc;nia are cultivated in a transition area between two morphoclimatic zones, the Amazonian and the Cerrado, a tropical rainforest and savannah climates, respectively (Marques et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this area, coffee is cultivated in latosol, acrisol and nitisol soils, all found in tropical regions with differences in their contents, particularly clay (IUSS Working Group WRB, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Coffee cultivation in Rond\u0026ocirc;nia is situated in North of Brazil, while coffee production in Esp\u0026iacute;rito Santo is located in Southeast, in the Brazilian Atlantic coast, in a morphoclimatic zone called \u0026ldquo;Mares de Morros\u0026rdquo;, where a subtropical highland variety of the oceanic climate and tropical flora prevails (Ab\u0026rsquo;S\u0026aacute;ber, 2012). The predominant soil in Esp\u0026iacute;rito Santo is the red or yellowish-colored oxisol, which presents a high concentration of minerals such as iron and aluminum (de Cunha et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn addition to the natural differences mentioned, the cultivation practices applied to the coffee samples also vary among the states. The \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003erobusta\u003c/em\u003e of Rond\u0026ocirc;nia is cultivated around the natural vegetation of the West portion of the Amazonian Forest, in a sustainable agroforestry system. The \u003cem\u003eC. robusta\u003c/em\u003e samples used in our analysis were cultivated by indigenous producers of the Cinta-Larga, Tupari e Suru\u0026iacute; ethnicities, whose cultivation practices focus on sustainability and involve a close relationship with the environment. The engagement of indigenous people is essential for social inclusion and prosperity of local communities in Brazilian agriculture and food market.\u003c/p\u003e\n \u003cp\u003eOn the other hand, the production of \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003econlion\u003c/em\u003e coffee in Esp\u0026iacute;rito Santo is carried out in medium and large family properties, with the use of mechanization and different irrigation systems to prevent bean development and harvest from draught (Venancio et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Coffee is the primary agricultural product of Esp\u0026iacute;rito Santo and the main contributor to employment opportunities (Incaper, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThese metabolomic differences impact the sensory perception of foods and beverages (Cassago et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Art\u0026ecirc;ncio et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, although professional tasters used the broader term \u0026ldquo;sweet\u0026rdquo; to describe all coffees from Minas Gerais (represented by the purple pentagons in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), differences in the use of more specific flavor descriptors were observed. In this case, some samples were described as \u0026ldquo;caramelly\u0026rdquo; and \u0026ldquo;honey\u0026rdquo; while others as \u0026ldquo;chocolatey\u0026rdquo;. In the case of \u003cem\u003eC. canephora\u003c/em\u003e, the flavor nuances sensed in the \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003erobusta\u003c/em\u003e coffee from Rond\u0026ocirc;nia were described as \u0026ldquo;fine wine\u0026rdquo; and \u0026ldquo;buttery\u0026rdquo;; while the flavor notes of the \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003econlion\u003c/em\u003e variety from Esp\u0026iacute;rito Santo were defined using terms like \u0026ldquo;dry berries\u0026rdquo; and \u0026ldquo;bright\u0026rdquo;. It is also worth mentioning that the distance between the commodity coffee and the other groups regardless of species and variety, is reflected in sensory quality.\u003c/p\u003e\n \u003cp\u003eIn the OPLS-DA, the value of importance that each variable must explain X and its correlation with Y is called VIP (Variable Importance in the Projection). VIP values greater than 1.0 are more relevant to explain the answer (Y), as they provide greater reliability in determining the most important variables in separating groups (SIMCA-P Manual 13.0.3). Based on the OPLS-DA analysis carried out with the coffee sample extracts (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) it allowed us to determine the variables correlated to the clusters, based on their VIP values.\u003c/p\u003e\n \u003cp\u003eBased on the OPLS-DA loading plot (Figure S3, Supplementary Material) and the samples\u0026rsquo; VIP values, the variables (substances of the TIC) that most influenced the formation of groups were identified. Moreover, the variables that presented a VIP value greater than 1.0 were determined as possible markers for clusters. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e details the variables that were identified and determined as markers for different species of \u003cem\u003eCoffea\u003c/em\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIdentified variables (compounds) responsible for the groupings in the OPLS-DA analysis of all coffee samples and their respective VIP values.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVIP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecaffeine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.72583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echlorogenic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.33315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHMBOA-Glc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.28673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLysoPC(18:2(9Z,12Z))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,3-Dehydrosilybin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.84786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-Indoleacetic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.73559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4-methoxybenzyl glucoside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.66728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,3\u0026apos;,4\u0026apos;,5,5\u0026apos;,8-Hexahydroxyflavone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etoralactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-(hydroxymethyl)-4(3H)-quinazolinone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecalystegine A6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-O-glucopyranosyl-4-hydroxy-7-methoxy-1,4-benzoxazin-3-one\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.28939\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecnidilide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.22662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elysophosphatidylcholine(16:0/0:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(R)-roemerine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecajanin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e[Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\n \u003cp\u003eAccording to the results of the OPLS-DA analysis, caffeine is one of the main markers to group \u003cem\u003eC. canephora\u003c/em\u003e samples. Whereas, for the \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003erobusta\u003c/em\u003e, the most relevant markers are roemerine, cajanin, and 2-(hydroxymethyl)-4(3H)-quinazolinone. In the case of \u003cem\u003eC. canephora\u003c/em\u003e var. \u003cem\u003econilon\u003c/em\u003e, calystegine A6, DIMBOA-Glc and HMBOA-Glc are the main markers in group formation. Finally, for the C. \u003cem\u003earabica\u003c/em\u003e samples, the compounds most responsible for the formation of groups are toralactone, cnidilide, LysoPC(18:2(9Z,12Z)), lysophosphatidylcholine(16:0/0:0), and 2,3-edhydrosilybin.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe main aim of this work was to analyze the metabolic profiles of coffee samples collected during the \u0026ldquo;Brazilian International Conference of Coffee Tasters\u0026rdquo; (2022 Edition) using LC-MS-based metabolomics. Our data showed that the secondary metabolites have wherewithal to discriminate the samples by species and variety. In accordance with previous studies, secondary metabolites enabled the acknowledgement of genetic and \u003cem\u003eterroir\u003c/em\u003e influences, especially in the case of \u003cem\u003eC. canephora\u003c/em\u003e samples cultivated in different Brazilian regions. Hence, besides the taxonomic and morphologic characteristics that differentiate the species \u003cem\u003eC. arabica\u003c/em\u003e and \u003cem\u003eC. canephora\u003c/em\u003e, metabolomic studies can help producers and researchers in differentiating coffees according to their chemical fingerprints, which would impact positively in the promotion of new specialty coffees and, consequently, the socioeconomic conditions of producers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eon behalf of all authors, the corresponding author declares that there is no conflict of interest.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003edata is available at Supplementary Material.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003enot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eall the authors have read, approved, and made substantial contributions for the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional declarations for articles in life science journals that report the results of studies involving humans and/or animals:\u0026nbsp;\u003c/strong\u003enot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003enot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eWe, the author and co-authors, state that this article is original and has not been submitted for publication in any other journal, whether in part or in its entirety. We assign the copyright of the referred article to European Food Research and Technology as well as the consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the following organizations for the support during the First Brazilian Meeting of Coffee Tasters: EMBRAPA Rond\u0026ocirc;nia, Federal Institute of Espirito Santo (IES), and the Association of the Region of the Forests of Rond\u0026ocirc;nia (Caferon).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Council for Scientific and Technological Development (CNPq, grant #308141/2019-9), and the Coordination for the Improvement of Higher Education Personnel (CAPES, Financial Code 001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAb\u0026apos;S\u0026aacute;ber, A. 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Plant chemophenetics \u0026minus; A new term for plant chemosystematics/plant chemotaxonomy in the macro-molecular era. \u003cem\u003ePhytochemistry\u003c/em\u003e, 163, 147-148. https://doi.org/10.1016/j.phytochem.2019.02.013\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"coffee, Coffea, untargeted metabolomics, multivariate statistical analysis, terroir","lastPublishedDoi":"10.21203/rs.3.rs-2828021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2828021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoffee (\u003cem\u003eCoffea \u003c/em\u003espp) has evolved from an agricultural commodity to a specialty beverage, regarding the product’s trading, appreciation, philosophies, and purposes of consumption. Consequently, part of the coffee industry has focused on the sensory complexion and high-quality to meet engaged consumers. To evaluate the chemical profiles and distinctiveness of natural products from plants, metabolomics has emerged as a valuable tool. In this work, we carried out an untargeted metabolomic approach based on reversed-phase liquid chromatography coupled with mass spectrometry, followed by multivariate statistical analysis to obtain the metabolic fingerprints of 21 coffee samples belonging to two species and five botanical varieties, as follows: \u003cem\u003eC. arabica\u003c/em\u003e (var. \u003cem\u003eyellow catuai\u003c/em\u003e, \u003cem\u003eyellow bourbon\u003c/em\u003e, and \u003cem\u003eyellow obata\u003c/em\u003e) and \u003cem\u003eC. canephora \u003c/em\u003e(var. \u003cem\u003econilon\u003c/em\u003e, and \u003cem\u003erobusta\u003c/em\u003e). The samples were obtained in the 2022 Edition of the “Brazilian International Conference of Coffee Tasters”, state of Rondônia, Brazil. Principal Component Analysis and Orthogonal Projections Latent Structures Discriminant Analysis were performed using the metabolomic data, resulting in the discrimination of coffee samples based on their chemical profiles. Caffeine, DIMBOA-Gl, roemerine, and cajanin were determined as chemical markers for \u003cem\u003eC. canephora\u003c/em\u003e samples, and toralactone, cnidilide, LysoPC(18:2(9Z,12Z)), Lysophosphatidylcholine(16:0/0:0), and 2,3-Dehydrosilybin for \u003cem\u003eC. arabica\u003c/em\u003esamples. In addition to the genetic variability, our results show the possible influence of a terroir factor in the production of secondary metabolites of coffee samples, mainly for individuals of \u003cem\u003eC. canephora\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Untargeted metabolomic approach based on UHPL-ESI-HRMS to investigate metabolic profiles of different Coffea species and terroir","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-21 23:11:02","doi":"10.21203/rs.3.rs-2828021/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c782281f-3aa2-4443-9e54-4a2d9adf98c9","owner":[],"postedDate":"April 21st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-06T19:59:21+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-21 23:11:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2828021","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2828021","identity":"rs-2828021","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}