Food Chemistry 197 (2016) 1331–1340

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Food Chemistry

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Studying the effect of storage conditions on the metabolite content of red wine using HILIC LC–MS based metabolomics ⇑ Panagiotis Arapitsas a, , Anna Della Corte a, Helen Gika b, Luca Narduzzi a, Fulvio Mattivi a, Georgios Theodoridis c a Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all’Adige, Italy b Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece c Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece article info abstract

Article history: The main aim of this work was to develop an untargeted normal phase LC–MS method, starting from a Received 27 May 2015 targeted method already validated for the analysis of 135 polar metabolites. Since the LC instrument Received in revised form 16 September and column were the same, most of the chromatographic conditions remained identical, while the adap- 2015 tations focused on maintaining the ionic strength of the eluents constant. The sample preparation was Accepted 22 September 2015 simplified and the effectiveness of LC–MS for long batches was evaluated, in order to record the maxi- Available online 25 September 2015 mum number of metabolites with good chromatographic resolution and the best MS stability and accu- racy. The method was applied to study the influence of storage conditions on wine composition. Slightly Chemical compounds studied in this article: sub-optimum storage conditions had a major impact on the polar metabolite fingerprint of the red wines Malvidin 3-glucoside (PubChem CID: 443652) analysed and the markers revealed included phenolics, vitamins and metabolites indentified in wine for Pantothenic acid (PubChem CID: 6613) the first time (4-amino-heptanedioic acid and its ethyl ester). 4-Amino-heptanedioic acid (PubChem CID: Ó 2015 Elsevier Ltd. All rights reserved. 487243) Vitisin A (PubChem CID: 10325504)

Keywords: Holistic, wine metabolomics Wine conservation Method development Primary metabolites Metabolomics Normal phase LC QTOF MS HILIC, Vitis vinifera

1. Introduction technique has been known since the 1960s (Dalgliesh, Horning, Horning, Knox, & Yarger, 1966), major evolution started in around Metabolomics, or metabolic fingerprinting, is used for compre- 2000 and since then the number of publications and active hensive characterisation of molecules in the biological system researchers in the field of metabolomics has increased rapidly investigated. In contrast to targeted methods, where the analyte (Gika, Wilson et al., 2014). Because of the high cost and specific (s) of interest is well known and the scope very specific, in untar- expertise in different fields needed, the first publications in food geted metabolomics the aim is to achieve the widest possible science appeared later (Cubero-Leon, Peñalver, & Maquet, 2014; metabolic coverage in an unsupervised manner. The idea has found Oms-Oliu, Odriozola-Serrano, & Martín-Belloso, 2013). Oenology application in life, environmental, food and other sciences (Baker, and viticulture were among the first sectors in food science to 2011; Gika, Theodoridis, Plumb, & Wilson, 2014; Gika, Wilson, & embark on metabolomics studies (Arapitsas, Speri, Angeli, Theodoridis, 2014; Nicholson & Lindon, 2008). Although the Perenzoni, & Mattivi, 2014; Arapitsas et al., 2012; Castro, Martins, Teixeira, & Silva Ferreira, 2014; Gougeon et al., 2009;

⇑ Corresponding author. Theodoridis et al., 2011). E-mail addresses: [email protected], panagiotis.arapitsas@gmail. com (P. Arapitsas). http://dx.doi.org/10.1016/j.foodchem.2015.09.084 0308-8146/Ó 2015 Elsevier Ltd. All rights reserved. 1332 P. Arapitsas et al. / Food Chemistry 197 (2016) 1331–1340

Since targeted methods have been widely used and taught in packaging have the most direct impact on wine quality and nutri- universities courses of analytical chemistry for many decades, their tional value, so there are various studies in the literature dealing applications to food chemistry, biology or medicine are very with metabolic changes caused in wine by incorrect and sub- advanced and the knowledge and expertise for method develop- optimal conservation conditions (Arapitsas, Speri et al., 2014; ment is well established in the corresponding industries and regu- Ghidossi et al., 2012; González Marco & Ancín Azpilicueta, 2006; latory bodies. Untargeted techniques are relatively new, and so Hernanz et al., 2009; Hopfer, Ebeler, & Heymann, 2012; Loscos, there is still debate about experimental design and method valida- Hernández-Orte, Cacho, & Ferreira, 2010; Monagas, Bartolomé, & tion (among other things). The development of a valid holistic LC– Gémez-Cordovés, 2005; Wirth et al., 2010). MS method is one of the most critical steps in a metabolomics pro- First aim of this work was to adapt the above described targeted ject, because not only the detection of as many metabolites as pos- method and to optimise it to a holistic LC–MS metabolic profiling sible is required, but also high instrumental stability and method. The final goal was to apply the optimised metabolic pro- repeatability during analysis, in order to facilitate chromatographic filing method to the study of a common oenological problem, alignment, the accurate definition of metabolic space and marker namely the effect of typical domestic storage conditions as discovery with as few false negatives/positives as possible. compared to optimum cellar conditions for five red wines and for Researchers are currently developing their methods based on the a period of 24 months. experience obtained in the targeted analysis field. Most efforts are directed at (1) improving metabolite detection and identifica- tion, mainly taking advantage of recent developments in mass 2. Material and methods spectrometry technology (Dunn & Hankemeier, 2013; Holcˇapek, Jirásko, & Lísa, 2012); and (2) data analysis, to look through mas- 2.1. Chemicals sive datasets trying to unravel biochemical phenomena and iden- tify trends in complex data. Metabolomics datasets are massive All chemicals used in this study were of the highest purity grade (especially those generated by LC–MS) and can be an attractive available and purchased from FLUKA Sigma–Aldrich, unless asset for chemometricians, programmers, and statisticians (Dunn otherwise stated. Water purified with a Milli-Q Water Purification & Hankemeier, 2013; Gika, Wilson et al., 2014). This is expected System was used for chromatography and preparation of standard to grow further with the increased availability of data (and meta- solutions. data) open repositories (Franceschi et al., 2014). Finally (3) the sample preparation methods are in most cases as simple as 2.2. Wines possible, to avoid the introduction of bias due to the different extraction efficiency observed for different metabolites for During 2009 five Vitis vinifera L. cv. Sangiovese grape samples example (Mattivi et al., 2012; Noack & Wiechert, 2014; from different areas of Montalcino (Tuscany – Italy) were collected, Theodoridis et al., 2011; Wu, Southam, Hines, & Viant, 2008). in order to cover the whole production area of Brunello di Montal- Hydrophilic interaction chromatography (HILIC) has recently cino. From each selected vineyard, a total of c. 80 kg of grapes was appeared to be the best LC approach for complementing reverse sampled in 20 kg boxes and sent via refrigerated transport (4 °C) to phase (RP) chromatography in global (untargeted) metabolomics the experimental winery at the Edmund Mach Foundation (Tren- fingerprinting. Because of its different selectivity, HILIC offers bet- tino – Italy) on the same day. The grapes from each zone were vini- ter retention for polar metabolites and due to its mechanism it pro- fied separately and after malolactic fermentation (March 2010) the vides better sensitivity (Gika, Theodoridis et al., 2014; Gika, young wine samples were filtered and bottled in 375 mL dark glass Theodoridis, Vrhovsek, & Mattivi, 2012; Spagou et al., 2011; bottles (20 bottles for each wine). Further details and basic Theodoridis et al., 2011; T’kindt, Storme, Deforce, & Van Bocxlaer, oenological parameters and analysis can be found in Arapitsas, 2008). We can divide the published applications of HILIC into Speri et al. (2014). In the Supplementary contents – Table S1 is two main categories: (a) targeted methods focusing on one group described how the bottles of each of the five wines were divided of metabolites (Valls, Millán, Martí, Borràs, & Arola, 2009; and used. Vilhena et al., 2014; Willemse, Stander, & De Villiers, 2013; Zhu et al., 2014) and (b) untargeted methods which follow a holistic approach (Spagou et al., 2011; Theodoridis et al., 2011; T’kindt 2.3. Storage et al., 2008). Our group (Gika et al., 2012) recently developed a tar- geted HILIC–MS/MS method which covers a wide set (135) of pri- After bottling, which was considered as time zero in the exper- mary and secondary polar metabolites belonging to various imental design, two bottles from each wine was stored at 4 °C molecular classes such as sugars, sugar alcohols, organic acids, (time zero or reference samples). The rest of the bottles of each bio-amines, amino acids, etc, which play an important role in food wine were divided into two groups and stored in two different con- and plant chemistry. ditions: (i) one half was stored in such a way as to mimic typical Wine can be stored long time and in contrast to most food, age- home/domestic wine conservation, with variable conditions during ing is positively correlated with quality. Cheap wines are usually the year, with medium temperature around 20–22 °C (only few consumed within a short time, while premium quality wines are summer days temperature arrived at 26–27 °C), in a room with expected to last and even improve with age, and are frequently no light but without temperature or humidity control (domestic) stored for up to several years. However, both products should be and (ii) the other set was stored in a cellar where the temperature stored in ideal conditions to allow consumers to experience equal (15–17 °C) and the humidity (70%) were controlled, stable and or better quality as compared to the legal requirements and optimum for red wine conservation (cellar). For both conditions, winemakers’ standards when the wine leaves the winery. From wine bottles were stored in horizontal position inside typical card- the winery to the consumer many factors can influence wine board wine boxes. Every 6 months two bottles of each wine (two quality and nutritional value, especially storage conditions and from the domestic and two from the optimum conservation condi- duration. Optimum storage conditions reduce the risk of deteriora- tions) were sampled and brought to 4 °C. The final storage time tion in wine quality, gain customers’ confidence and sharpen the was 24 months after time zero, which was the bottling date. All competitive edge in the market. Humidity, temperature, light and samples were analysed together at the end of the experiment. P. Arapitsas et al. / Food Chemistry 197 (2016) 1331–1340 1333

2.4. Sample preparation 12 months, 5 + 5 bottles for 18 months, and 5 + 5 bottles for 24 months (5 domestic and 5 cellar). So in total the sample set On termination of the experiment the samples were kept for included 45 bottles of wine, the QC and the blank. The in-batch two months at 4 °C until analysis. Wines were uncorked under order of all samples analysed was randomised by using the nitrogen atmosphere and an aliquot was transferred into a 15 mL http://www.random.org/sequences/ site. At the beginning of each amber vial (filled to capacity). Then, again under nitrogen atmo- batch one blank and four QCs were injected. After this system equi- sphere, a quality control (QC) pooled sample was prepared by com- libration, one QC was injected every six samples and at the end of bining 0.5 mL of each sample. the batch one blank was injected. For the dilution test, the QC sample was diluted 1:1, 1:2, 1:4, 1:6 and 1:9 v/v with acetonitrile (ACN). In the finally adapted pro- 2.6. Data analysis tocol, 1 mL of each wine sample was diluted with 2 mL ACN (1:2 dilution) and then 40 lL of the internal standard mix was added. Raw data were converted into CDF format (Waters Databridge) Finally 2 mL from each sample/QC was filtered through 0.2 lm and then processed using XCMS for feature extraction, grouping PTFE filters into a 2 mL autosampler amber vial (MS certified) for and alignment. The detailed XCMS parameters can be found in LC/MS analysis. The internal standard mix contained 14 mg of the Supplementary content – Table S2. Data extraction parameters xanthosine and 25 mg of nicotinic acid in 10 mL of MeOH:H O 2 were selected on the basis of the characteristics of chromato- (1:1). For blank samples the same procedure was followed but graphic and mass spectrometric performance. The peak table out- instead of wine, 1 mL of Milli-Q water was used. put of XCMS was then used for statistical analysis with the software EZInfo SIMCA-P version 12.0.0 (Umetrics, Umea, Sweden), 2.5. UPLC–QTOF MS analysis using Pareto normalisation. The PCA plots of EZInfo SIMCA-P were used for quality control of the data sets by checking the distribu- A Waters Acquity UPLC coupled via an electrospray ionisation tion of the QC injections. Orthogonal partial least-squares discrim- (ESI) interface to a Synapt HDMS QTOF MS (Waters, Manchester, inant analysis (OPLS-DA) was used to find features/tentative UK) operating in W-mode and controlled by MassLynx 4.1 was markers contributing to group/condition discrimination, based on used. All samples were analysed with a 2.1 mm  150 mm the t-test with the Mann–Whitney U-test and the S-plot of ACQUITY UPLC 1.7 lm BEH amide column (equipped with an SIMCA-P software. ACQUITY UPLC BEH Amide 1.7 lm VanGuard Pre-column) which Metabolite annotation was performed manually by applying the was maintained at 60 °C and eluted with a multistep gradient over minimum reporting criteria of the four identification levels the course of 30 min at 400 lL/min. The gradient started with a reported by Dunn et al. (2012). For the 1st annotation level, mass 4 min isocratic step at 100% A (acetonitrile–water, 95–5 (v/v), accuracy (with less than 5 ppm error), isotopic distribution, at least 20 mM ammonium formate), then rising to 28% B (acetonitrile– one fragment and retention time were used and compared to those water, 2–98 (v/v), 20 mM ammonium formate) over the next of the authentic standard. For the 2nd annotation level, mass accu- 21 min and finally to 60% B over 5 min. Then the flow rate was racy (with less than 5 ppm error), isotopic distribution, at least one decreased to 250 lL/min in an isocratic step for 0.5 min; after that fragment, literature information about wine metabolome and B was increased to 80% and was kept isocratic for 2 min and finally chromatographic properties, and external databases such as the column was returned to the initial conditions for a 12 min HMDB, Kegg and MassBank were used. For further annotation equilibration. Two cycles of weak and strong solvent washing of exploration also MSe, MS/MS and CAMERA were used. For the the injecting system were carried out between injections. The 3rd annotation level, where only a chemical formula was given, injection volume was 10 lL and the samples were kept at 4 °C only mass accuracy (with less than 5 ppm error) and isotopic dis- throughout the analysis. Mass spectrometric data were collected tribution were used. in positive ESI mode over a mass range of 30–1000 amu, with scan Peak integration of all annotated compounds, internal stan- duration of 0.3 s in centroid mode. The transfer collision energy dards and tentative markers was performed using the TargetLynx and trap collision energy were set at 6 V and 4 V. The source tool of MassLynx Software. Peaks areas emanating from this data parameters were set as follows: capillary 3 kV, sampling cone were also used for quality control of the analysis by evaluating 25 V, extraction cone 3 V, source temperature 150 °C, desolvation the variability of specific metabolites. For this part the raw LC– temperature 500 °C, desolvation gas flow 1000 L/h and nebuliser MS files were used and not the peak table exported from XCMS gas 50 L/h. External calibration of the instrument was performed analysis. at the beginning of each batch of analysis. LockMass calibration was applied using a solution of leucine enkephaline (0.5 mg/L, m/z 556.2771 for positive ion mode) at 0.1 mL/min. 3. Results and discussion

2.5.1. Dilution test 3.1. Method development In order to find the optimum sample dilution 20 injections of each QC dilution were performed, starting with the most diluted In analytical chemistry method development is an important sample. Between each dilution set, a blank sample was injected. process, with which researchers try to determine the minimal combination of analytical steps required for the identification 2.5.2. MS QTOF stability test and quantification of one or more analytes in a specific matrix. In Before running the real samples and in order to evaluate LC–MS targeted methods these steps are well defined, and more or less stability in practical conditions, 50 injections of the QC samples easy to follow and accomplish, since they are based on commercial were analysed, with one blank sample injection between every standards and the final scope is clear and solid. The opposite occurs 10 QC injections. with untargeted methods, because if the researcher focuses on specific analyte(s) a bias towards that class of analytes is intro- 2.5.3. Sample set duced, and the holistic character of the technique is annulled. In The sample set was made up of 5 biological replicates/time the case of untargeted profiling methods, where metabolites are points/conditions; specifically, 5 bottles for time zero (reference), by definition not pre-defined, validation is a complex matter and 5 + 5 bottles for the 6 months time point, 5 + 5 bottles for challenging. It is unfeasible to validate the method which 1334 P. Arapitsas et al. / Food Chemistry 197 (2016) 1331–1340 detects hundreds to thousands of peaks, many of unknown iden- few days to several weeks. The previous buffer concentrations tity. According to a recently published review focused in method were optimised based on the shape of the peaks detected. It was validation strategies in untargeted metabolomics (Naz, Vallejo, observed that for some peaks shape was improved by increasing García, & Barbas, 2014), the recommendations for the method the buffer concentration from 10 to 20 mM. Given that the highest development proposed to select samples minimising the noncon- buffer concentration to dissolve in the eluent A was 20 mM (Gika trolled variations, use a simple sample pre-treatment, use pooled et al., 2012), it was decided to keep both strong and weak eluent QC, sample randomisation, and control sensitivity and repro- at a 20 mM concentration. Both solvents were prepared by using ducibility. For the method validation the same paper recom- the same stock solution of ammonium formate (4 M). For better mended the use of the QC sample injections to measure the control of the ionic strength of the eluent, pH was adjusted to 4.8 repeatability and the drift in signal variations, retention time and before final dilution with the organic solvent (+2% ACN for A and mass accuracy. +95% ACN for B eluent). In HILIC mode and in contrast to reversed The main scope of this project was to study the effect of differ- phase LC, analytes often exhibit strongest retention in their ionised ent storage conditions on the metabolic content of red wine prod- form. This tendency copes well with ESI-MS, since analytes elute ucts. A method previously developed by our group (Gika et al., from the column already ionised. 2012), able to quantify up to 135 polar metabolites, covering a cen- In HILIC mode if the analytes of interest are acids it is better to tral part of the chemical and biological variability of the primary work at high to medium pH (2 pH units above analyte pKa values), metabolites, with 37 mono-, poly-saccharides and reduced while for bases the opposite is suggested: low to medium pH (2 pH monosaccharides (polyols), 37 organic acids, 31 amino acids, 22 units below analyte pKa values). Focusing only on one group of amines and other biomolecules such as nucleosides and vitamins metabolites (acids or bases) cancels the holistic character of an was adapted to an untargeted metabolite profiling method. The untargeted method. Furthermore, the column manufacturer chromatographic apparatus (Waters Acquity UPLC) and the ESI (Waters) advises using intermediate pH values (5–7) for methods source of both systems, UPLC–MS/MS and UPLC–QTOF MS, were targeting various type of analytes. For this reason and after also almost identical, so chromatography and ionisation were expected considering the pKa of ammonium formate, it was decided to to be similar. According to recommendations (Naz et al., 2014), adjust the pH of both eluents to 4.8. having validated a method for selected compounds should gener- The next step to consider was sample preparation. For the tar- ate reliable and acceptable untargeted approach data sets for fur- geted method three different sample dilutions were used for com- ther evaluation. pound quantification, which is logical if one considers the large As it could be anticipated, because of the different nature of the number of metabolites and the wide concentration range in real two approaches some parameters needed to be adapted and opti- samples (Gika et al., 2012). Such a strategy is not an option in mised in order to transfer the targeted method from triple quadru- untargeted methods and only one method for sample preparation pole MS to a QTOF-MS untargeted method. HILIC is a variant of is used. However, sample preparation should be optimised with normal phase liquid chromatography that partly overlaps with two key end-points: (1) to provide the maximum possible number other chromatographic applications such as ion chromatography, of metabolites and (2) to protect the analytical system to maintain and its utilisation has grown in popularity due to the high compat- a stable signal throughout analysis. Given that it is not possible to ibility with mass spectrometry and the need to improve detection measure the actual number of metabolites existing in the sample, of polar metabolites. Other advantages of HILIC are the high sensi- the efficiency of sample preparation is commonly evaluated by the tivity when is used with ESI-MS and the lower back pressure than number of features. A very simple definition of a feature is ‘‘a in reversed-phase LC. Between the problems of HILIC are that is not molecular entity with a unique m/z and retention time”. From as generic as reverse phase because covers smaller number of ana- our experience (Arapitsas et al., 2012; Arapitsas, Speri et al., lytes; and the retention times present smaller reproducibility in 2014; Mattivi et al., 2012) in wine metabolomics simple sample respect reverse phase (Gika et al., 2012; Gika, Wilson et al., 2014; preparation can provide chromatograms with fewer features in T’kindt et al., 2008). The optimised parameters could be divided comparison to typical sample preparation for targeted analysis of into two broad categories: sample preparation and LC–MS set up. wine (e.g. via solid phase extraction, SPE). However, the final result The construction of this untargeted method started from optimisa- of sample preparation is not biased by definition and from the tion of LC–MS set-up, because this is more delicate as compared to beginning in favour of certain molecules. In this project, in agree- sample preparation. A robust LC–MS method is a key feature in ment with previous studies (Arapitsas et al., 2012; Arapitsas, metabolomics for a number of reasons, especially for correct peak Speri et al., 2014) we chose to use only dilution, followed by filtra- alignment. Poor chromatographic repeatability and peak shape in tion prior to injection into LC–MS. This compromise was aimed to untargeted data-sets can prove very difficult to treat: peak align- define the smallest experiment needed to build an holistic view of ment may fail, thus further complicating bioinformatic analysis; the polar metabolome. such problems may increase false positive and false negative In order to select the optimum dilution factor, four parameters results. A common practice to avoid misalignment in untargeted were evaluated: the number of features, the precision of the profile metabolomics is to analyse all samples in a single batch with as acquired over 20 injections (Fig. 1A), the grouping shown by the few interruptions as possible. It therefore becomes priority to keep PCA plot (not shown) and the quality of a few selected peaks by chromatographic system robustness in terms of retention time for visual inspection (Fig. 2). A pooled QC sample of all wine samples long periods, covering at least the whole duration of a batch of the was prepared and diluted with ACN 1:1, 1:2, 1:4, 1:6 and 1:9 v/v. experiment. Then 20 consecutive injections of the undiluted wine and each of To have unvarying retention times in HILIC mode it is very the diluted samples were performed with the optimised and important to preserve constant ionic strength during the measure- adapted LC–MS method. The results of this experiment, shown in ments and throughout the different runs or analytical batches. For Fig. 1A, illustrated that undiluted wine and the two highest dilu- this reason, it was decided to use a stock buffer solution for the tions (1:6 and 1:9 v/v) had a very similar low number of features preparation of both A and B eluent, which is a practice used – (c. 6000); but this number for undiluted wine, as expected from but not common – in targeted analysis. A stock buffer solution our previous experience (Arapitsas, Speri et al., 2014), already offers the chance to prepare and re-prepare eluents with the same decreased considerably after 10 injections. The 1:1 and 1:2 dilu- ionic strength in a convenient, fast and robust way for the whole tions had the highest number of features (between 7000 and length of the metabolomic experiment, which may last from a 7500), with the first showing a slightly higher number of features P. Arapitsas et al. / Food Chemistry 197 (2016) 1331–1340 1335

sequence of 55 injections (50 QC injections with one blank injec- tion every 10 QC injections) the system continued to have accept- able signal stability, both in terms of the number of features and the peak area of selected compounds (Fig. 1B). This experiment also shows that injection of blanks during an untargeted metabolo- mics sequence should be carefully planned. As shown in Fig. 1B, the number of features for the 21st QC injected following a blank injection is much lower as compared to all other QC injections. This could be attributed to chromatographic instability. At the same time it is possible to note a slight perturbation between the groups of QCs divided by the blanks (Fig. 1B). In our experience, this phe- nomenon is regular rather than an exception. If the injection of blanks during a sequence is mandatory, then it is good practice to include 1 or 2 QC injections before the next real sample injec- tion. Moreover, such pilot studies can provide information about the stability of prepared samples remaining in the autosampler, since the QC is a pooled sample of all samples and should have the same metabolic space and physicochemical properties. The results of this study confirmed the excellent stability of the LC–MS system for the length of a typical metabolomic sequence and the integrity of the samples in the autosampler during analysis.

3.2. Wine storage experiment

The method optimisation described above was applied to an Fig. 1. (A) The trend in the number of features for the pooled sample (QC) after 20 experimental design concerning wine storage. The aim of this pro- injections and in different dilutions with acetonitrile. (B) LC–MS stability test of the optimised sample preparation for 55 consecutive injections. ject was to study how the metabolome of wine changes in two dif- ferent conservation conditions over a period of 24 months: (a) but the second showing better stability over the 20 injections. As optimum storage conditions in a wine cellar, as compared to (b) explained above, system stability was given higher priority in this typical domestic storage. For the optimum storage conditions the study and so the 1:2 (wine:ACN v/v) dilution was selected. temperature was kept stable at around 16 °C during all seasons, From the comparison of specific compounds/peaks it was and humidity was around 70%. In both cases the wines were kept observed that peak resolution improved at higher dilution factors, in clean places, away from smells, light or heating systems and probably due to the peak broadening observed with the increase in the bottles were in a horizontal position inside typical cardboard water content in the injection solvent (Fig. 2). Wine contains wine boxes. The latter parameters would seem appropriate for around 85% water and our chromatography starts with 98% ACN. ‘good’ domestic storage, and are often also used in restaurants, In reversed phase inappropriate choice of injection solvent may wine stores, supermarkets and wine distributors, and are certainly result in peak broadening mainly in the first part of the chromatog- not extreme. The only difference for domestic storage was that nei- raphy, but in this HILIC experiment broad peaks and problems ther humidity nor temperature were controlled: temperature was caused by inappropriate injection solvent were observed over the around 20 °C during the winter and between 20 and 27 °C in the whole length of the chromatogram. This was despite the fact that summer period. To increase the biological variability of the sample the injection volume (10 lL) was not high for the flow rate used set, 5 different red wines were used and a different bottle was sam- (400 lL/min). Some examples are presented in Fig. 2, where it is pled for each time point/storage (reference or time zero, 6, 12, 18 clear that with the increase in ACN content (from the top to the and 24 months), and to minimise noncontrolled variations bottom in the figure) the peak shape improved, while in the case (Arapitsas, Speri et al., 2014; Naz et al., 2014) all wines were of sugar alcohols, resolution also increased. The peak shape got Sangiovese. Moreover, all samples were prepared and injected sharper not only for the early eluting uridine, but also for proline, under a randomised sequence to avoid systematic bias due to ana- which elutes in the middle of the chromatogram, and the late lytical variation. Fig. 3 shows a PCA score plot of the HILIC–MS eluting raffinose (Fig. 2). results, based on 6311 features recognised using XCMS. The QCs In the injections of undiluted wine various double peaks were cluster in the middle of the plot and this tight QC cloud is proof observed for the same metabolite (as shown for uridine and proline of good system stability over the whole length of analysis. QC in Fig. 2). This phenomenon actually generated a higher number of injections distribution/variability is the most popular, pragmatic features. Lower peak resolution (broader peaks) was also observed, and reliable solution for controlling the quality and validating which may result in a loss of features for closely-eluting analytes. the repeatability of the untargeted metabolomics dataset. This con- Finally, detector signal saturation can cause problems such as ion/ cept of using the samples themselves to prepare a QC sample, to be signal suppression but also problems in terms of mass resolution. analysed at regular intervals through the analysis is widely The column manufacturer suggests a minimum of 75% ACN in employed in the field. (Arapitsas, Speri et al., 2014; Franceschi the injection solvent. In this study, the best results were found et al., 2014; Gika, Theodoridis et al., 2014; Godzien, Alonso- with 66% ACN (resulting from the dilution of 1:2 v/v wine: ACN), Herranz, Barbas, & Armitage, 2014; Sangster, Major, Plumb, but wine already contains 13% ethanol, which is protic/strong for Wilson, & Wilson, 2006), and it has recently also been introduced HILIC solvent. To avoid precipitation problems, sample filtration to targeted analysis (Ehrhardt, Arapitsas, Stefanini, Flick, & was performed after dilution, just before LC–MS analysis. Mattivi, 2014). In addition, the manual integration of 50 selected The next step was to check system stability/robustness in a peaks (including all markers in Table 1) with TargetLynx showed batch with a higher number of injections, which should be similar excellent stability in terms of retention time (less than 1%CV) to real experiments and could also function as a pilot study. After a and peak area (less than 20%CV). Secondly, there was a clear time 1336 P. Arapitsas et al. / Food Chemistry 197 (2016) 1331–1340

Fig. 2. Selected LC–MS chromatographic graphs for different compounds of the same sample (QC) in various dilutions with acetonitrile (not diluted wine, 1:1, 1:2, 1:4, 1:6 and 1:9 v:v wine:acetonitrile).

Fig. 3. PCA plot of kinetic experiment analysis in the two storage conditions tested. The numbers indicate the length of storage in months (0, 6, 12, 18 and 24 months). The different colours indicate the storage conditions (blue for cellar, red for domestic, green for the reference, and black for the QCs). The different symbols indicate the wine (bottles from 5 wines where used in this study – Supplementary contents Table S1). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) trend for wine stored with home/domestic conservation in PC4 of storage conditions, domestic conservation caused larger overall (horizontally). There was considerable variability for the five dif- changes. The wines stored in optimum conditions remained rela- ferent wines and this was largely seen in PC1. As regards the effect tively close to the time zero samples, while the domestically stored P. Arapitsas et al. / Food Chemistry 197 (2016) 1331–1340 1337

Table 1 Features markers for wine storage experiment (cellar vs domestic) together with their annotation, LC–MS and statistical information.

m/z rt Higher in VIPb Annotation Formula Theoretical Error m/z Comments (min) m/z (ppm)

186.1131 1.02 Domestic 3.08 Unknown 1 C9H15NO3 MS/MS: 170.04 216.1073c 1.31 Cellar 3.76 Unknown 2 Together with m/z 160.1306; MS/MS: 143.04 and 115.02 d 303.0500 1.33 Domestic 3.62 Quercetin C15H10O7 303.0499 À0.25 [M+H]+

174.1494 2.27 Domestic 3.01 Unknown 2 C9H19NO2 174.1494 0.00 Together with m/z 87.0454 234.0803 2.32 Cellar 2.77 Unknown 3

111.0558 2.42 Domestic 2.92 Unknown 4 C5H6N2O 111.0558 0.00 MS/MS: 82.0526

166.0509 3.14 Domestic 6.43 Unknown 5 C8H7NO3 166.0504 À3.01 c d 291.0877 3.22 Cellar 3.07 Catechin C15H14O6 291.0863 À4.78 [M+H]+ c d 347.0771 3.58 Domestic 2.83 Syringetin C17H14O8 347.0761 À2.88 Together with 509.1231; [MÀglucose+H]+

317.0797 3.8 Domestic 2.21 Unknown 7 C18H10N3O3 317.0800 0.95 Together with m/z 214.0173

97.0289 4.11 Domestic 2.98 Unknown 8 C5H4O2 97.0290 1.03

136.0620 4.26 Cellar 8.33 Unknown 9 C5H3N4 +NH3 136.0623 2.20 Together with m/z 119.036 and 94.0404; MS/MS: 119.04 and 94.04

169.0511 4.39 Domestic 6.75 Unknown 10 C9H4N4 169.0514 1.77 MS/MS: 150.05

184.0640 4.51 Cellar 4.83 Unknown 11 C5H13NO4S 184.0643 1.63 Together with m/z 104.1073; sulfonated C5H13NO; MS/MS: 104.10 and 60.05 c a 333.0975 6.67 Domestic 3.24 Malvidin derivative C17H16O7 333.0969 À1.86 Together with m/z 495.1483 and 315.0884 c 204.1241 9.21 Cellar 5.39 Unknown 12 C9H17NO4 204.1236 À2.45 MS/MS: 158.09, 112.07; 4-aminoheptanedioic acid ethyl ester

349.0925 9.44 Cellar 4.34 Unknown 13 C11H20N6OS3 349.0939 4.01 MS/MS: 317.18, 181.04, 139.03, 120.06 c d 220.119 9.61 Cellar 2.93 Pantothenic acid C9H17NO5 220.1179 À4.80 [M+H]+ 232.1188 10.55 Domestic 4.60 Unknown 14 MS/MS: 72.02, 85.01, 103.02 c d 493.134 10.72 Cellar 5.80 Malvidin 3-glucoside C23H25O12 493.1341 0.11 [M+] 363.0685 12.86 Cellar 2.72 Unknown 15 MS/MS: 136.03 347.0949 12.87 Cellar 3.17 Unknown 15

138.0549 13.11 Cellar 8.95 Amino-Benzoic acid C7H7NO2 138.0555 4.35 [M+H]+ c 204.1236 13.91 Cellar 5.40 Unknown 16 C9H17NO4 204.1236 0.00 MS/MS: 165.09, 145.05, 85.00 347.0954 14.05 Domestic 4.63 Unknown 17 c d 561.1239 18.15 Cellar 2.24 Vitisin A C26H25O14 561.1239 À0.03 [M]+ c d 176.0923 18.37 Cellar 6.98 4-Aminoheptanedioic acid C7H13NO4 176.0923 0.00 [M+H]+ c d 158.0817 18.37 Cellar 4.12 4-Aminoheptanedioic acid C7H11NO3 158.0817 0.00 [MÀH2O+H]+ d 130.0862 18.37 Cellar 4.09 4-Aminoheptanedioic acid C6H11NO2 130.0868 4.61 [MÀH2OÀCO+H]+ c d 112.0757 18.37 Cellar 3.94 4-Aminoheptanedioic acid C6H9NO 112.0762 4.46 [MÀH2OÀCOÀH2O+H]+ c 344.0912 20.49 Cellar 7.78 Unknown 18 C11H20KN4O4S 344.0921 2.62 305.1739 and 163.0404; MS/MS: 241.01, 197.01 and 153.01 212.0564 21.78 Domestic 3.00 Unknown 19 282.1013 22.22 Cellar 7.09 Unknown 20 MS/MS: 156.05 and 117.02

286.1403 24.51 Domestic 5.09 Unknown 21 C12H19N3O5 286.1403 0.00 Together with 185.0928; MS/MS: 185.09 and 100.02

a Malvidin 3-glucoside degradation product after hydrogenation; rt: retention time: b VIP: Variable Influence on Projection (cellar versus domestic storage). c Features also markers in the RP LC–MS experiment (Arapitsas, Speri et al., 2014). Unknowns with the same number are features which belong to the same peaks. d 1st level annotation (confirmed by authentic standard). wines were widely distributed and the 18 and 24 months time chromatogram. The confirmed 34 feature-markers correspond to points in particular were clearly further apart from the previous 29 metabolites, since in this list one compound had four features time points (12 and 6 months). This was expected on the basis of and a second compound two features (Table 1). Of these tentative our previous study of the same wines with reverse phase LC–MS markers, phenolic compounds known (Arapitsas, Speri et al., 2014; analysis (Arapitsas, Speri et al., 2014), also providing proof of the Kallithraka, Salacha, & Tzourou, 2009; Monagas et al., 2005; Wirth validity and utility of the method. et al., 2010) to be influenced by storage length and conditions To identify features differentiating the two storage conditions, were identified; these included quercetin, catechin, malvidin the OPLS-DA discrimination analysis tool of SIMCA-P (Umetrics, 3-glucoside and pyranomalvidin 3-glucoside. Quercetin was higher Umea, Sweden) software was applied to the peak table from XCMS for domestic storage as the product of the quercetin 3-glucoside analysis. OPLS-DA analysis highlighted 81 out of a total 6311 fea- hydrolysis in these conditions, while the other three were higher tures as tentative markers for the two conditions (with Variable in the case of optimum cellar storage, in agreement with previous Importance in the Projection or VIP >2). The S-plot of the OPLC- work by our group, where they were found to be more stable at DA analysis is shown in the Supplementary contents – Fig. S1. lower temperatures (Arapitsas, Speri et al., 2014). In our Manual integration of these 81 features from the raw file data RPLC–MS study, carried out with the same sample set, these four using TargetLynx allowed to remove redundancy, decreasing the metabolites together with pantothenic acid (Fig. 4) were found to number of tentative markers to 34. This step helped to detect false be influenced by the storage conditions of the specific sample positives by applying independent evaluation of the markers set. Moreover, the datasets obtained by the two platforms, selected through XCMS and OPLS-DA, since it was applied to raw RPLC–MS and HILIC–MS, shared 12 common features with signifi- data. The manually integrated areas made it possible to confirm cant differences for the two storage conditions (Table 1 and or reject the statistical significance of each marker in distinguish- Arapitsas, Speri et al., 2014). Markers eluting in the solvent front ing the two storage conditions; and to evaluate the instrumental in the RPLC study were retained by the HILIC column and vice variability of each specific marker during the analysis by versa. This confirmed the robustness of the instrumental set-up using the QCs injections. Another way of eliminating false positives and data analysis parameters used in both studies, but also the is visual inspection of the markers’ MS spectrum and LC complementarity of the two platforms. 1338 P. Arapitsas et al. / Food Chemistry 197 (2016) 1331–1340

Fig. 4. Average change, based in 5 different red wines, of 4-amino-heptanedioic (a) and pantothenic acid (b) during storage in the two conditions (cellar vs. domestic ). Peak areas correspond to integrated peaks calculated using TargetLynx.

Using HILIC–MS mode a degradation product of malvidin 4-amino-heptanedioic acid, but only one had 3 ions similar to 3-glucoside was also shown to be higher for domestic storage the acid. The correlation between 4-amino-heptanedioic acid and (Table 1). Based on mass accuracy, isotopic distribution and other unknown 12 (tentatively identified as an ethyl ester of 4-amino- m/z ions of the same peak (see Table 1), we tried to provide heptanedioic acid) across all samples was 0.76, but only 0.22 with the most probable molecular formula for all the ‘unknown’ unknown 16. On the other hand, unknown 16 had a correlation of metabolite markers. Although almost 250 polar commercial stan- 0.81 with pantothenic acid, but is probably just a fragment of the dards were injected into the LC–MS system (Shahaf et al., 2013) main ion, since the loss of one oxygen should not give a more polar including the metabolites of the targeted metabolomics paper metabolite. Table 1 reports the m/z and retention times of all mark- (Gika et al., 2012), the number of unknown markers was 21 (from ers and some extra information (fragments, coelution ions, molec- 29). Since the injected chemical standard – reference material – list ular formulas, etc) which could be interesting for future work in includes almost all commercial available polar primary metabo- the wine metabolomic field. Although some additional hypotheses lites (carbohydrates, amino acids, organic acids, etc.) and many about the structure could be made, in the light of the high possibil- polar secondary metabolites known to be present in wine, it could ity of false positive error when the retention time is unknown be concluded that the metabolic space of wine is much bigger than (Arapitsas, Langridge, Mattivi, & Astarita, 2014), it was preferred is currently known and deserve further investigation. This is not not to speculate further about the possible molecular form of the surprising if we consider that wine is one of the most complex unknown markers. In Fig. 4 the kinetic graphs of pantothenic acid foods as far as the metabolomic profile is concerned, since and 4-amino-heptanedioic acid and their chemical structure are grapes, yeasts, bacteria, fungi, exogenous antioxidants, fining shown. The concentration (expressed in area) of both markers agents and other oenological materials and ageing are involved decreased much faster in domestic conditions. The vitamin pan- in its preparation. tothenic acid concentration in foodstuff is known to be influenced Different primary/secondary polar metabolites, including glu- by storage conditions and especially by temperature. On the other cose, proline, hypoxanthine, xanthine, xanthosine, salicylic acid, hand, the literature lacks information about 4-amino-heptanedioic hexoses, inositols, phenylalanine, betaine, citrulline, uracil, choline, acid, not only as regards its behaviour in food in various storage GABA, etc, did not show any statistically significant variation (data conditions, but in general in relation to its importance, while this not shown). From the molecular formulas (Table 1) it becomes is the first time 4-amino-heptanedioic acid has been found in wine, clear that most of the differentiating metabolites were nitrogen- to our knowledge. contained (e.g. amines). The most promising marker was the peak with a retention time of 18.37 min, since it has one of the highest 4. Conclusions VIP values and 4 features in Table 1. This metabolite, identified (1st level identification) as 4-amino-heptanedioic acid (Fig. 4), should In conclusion, it was shown that a good starting pointing for not be confused with its isomer 2-aminopimelic acid, which eluted developing a good and robust untargeted HILIC LC–MS method 0.5 min later in our chromatographic system. The ethyl ester of 4- was a similar targeted method already validated for its functional- amino-heptanedioic acid was another metabolite marker, higher in ity. By applying this idea, a HILIC–MS metabolomics method for cellar conditions. There were two peaks with the same m/z and wine polar metabolites was developed. The majority of the chro- isotopic distribution between the markers (unknowns 12 matographic parameters were unchanged and for the adaptation and 16), which could correspond to the ethyl ester of only eluent preparation was optimised in order to have constant P. Arapitsas et al. / Food Chemistry 197 (2016) 1331–1340 1339 mobile phase ionic strength. The other key point optimised was Gika, H. G., Theodoridis, G. A., Vrhovsek, U., & Mattivi, F. (2012). Quantitative sample preparation, with the intention of ensuring instrumental profiling of polar primary metabolites using hydrophilic interaction ultrahigh performance liquid chromatography–tandem mass spectrometry. Journal of stability for long batches in terms of chromatographic parameters Chromatography A, 1259(null), 121–127. http://dx.doi.org/10.1016/j.chroma. (peak resolution and retention time) and MS accuracy and sensitiv- 2012.02.010. ity. Although HILIC is considered less robust as compared to RPLC, Gika, H. G., Wilson, I. D., & Theodoridis, G. A. (2014). LC–MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives. here it was possible to have comparable results. 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