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Ultrafast polyphenol metabolomics of red wines using microLC-MS/MS Yan Ma, Nobuo Tanaka, Arpana Vaniya, Tobias Kind, and Oliver Fiehn J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.5b04890 • Publication Date (Web): 23 Dec 2015 Downloaded from http://pubs.acs.org on December 26, 2015

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Journal of Agricultural and Food Chemistry

Ultrafast polyphenol metabolomics of red wines using microLC-MS/MS

Yan Ma(1), Nobuo Tanaka(1,2) , Arpana Vaniya(1), Tobias Kind(1) and Oliver Fiehn(1,3) (1) UC Davis Genome Center – Metabolomics, University of California, Davis, California 95616, United States (2) GL Sciences Inc., Iruma, Saitama 358-0032, Japan (3) Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia

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Abstract

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The taste and quality of red wine is determined by its highly complex mixture of polyphenols

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and many other metabolites. While no single method can fully cover the full metabolome, even

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for polyphenols and related compounds, current methods proved inadequate. We optimized

5

liquid chromatography resolution and sensitivity using 1 mm I.D. columns with microLC pumps

6

and compared data-dependent to data-independent (SWATH) MS/MS acquisitions. A high-

7

throughput microLC-MS method was developed with a 4 min gradient at 0.05 mL/min flow rate

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on a Kinetex C18 column and Sciex TripleTOF mass spectrometry. Using the novel software

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MS-DIAL, we structurally annotated 264 compounds including 165 polyphenols in six

10

commercial red wines by accurate mass MS/MS matching. As proof of concept, multivariate

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statistics revealed the difference in the metabolite profiles of the six red wines and regression

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analysis linked the polyphenol contents to the taste of the red wines.

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Keywords

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red wines, polyphenols, metabolomics, 1 mm I.D. column, microLC-MS/MS

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Introduction

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Red wine is a prime example how food quality is determined by its highly complex mixture of

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metabolites. While all wines are characterized by their abundance of alcohol, sugar and simple

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organic acids, red wine is specifically known for its diversity of phenolic compounds, including

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phenolic acids, flavonoids, anthocyanins and tannins.1 For example, 111 polyphenols were

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reported in red wines according to Phenol-Explorer,2 an online database of polyphenol

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contents in food. All those complex components contribute together to the color, aroma, taste

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and mouthfeel of the red wines. For example, vanillin and 4-allyl-2-methoxyphenol were

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reported to contribute to the wood-vanillin-cinnamon aroma in aged red wines.3

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The polyphenol components in red wines have drawn a lot of interest due to their high

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antioxidant potential.4 One well-known example is resveratrol, which has been studied

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intensively as an anti-aging and anti-carcinogenic agent.5 The phenolic composition also plays

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a great role in the color stability and taste of the red wines.6 To qualitative and quantitatively

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analyze the polyphenols and other components in red wine, reversed-phase high performance

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liquid chromatography (HPLC) methods were developed with either ultraviolet (UV) or mass

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spectrometry (MS) detection.7, 8 However, most of the current methods only measured a few

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abundant components of red wines9, 10 and were inadequate for giving full coverage of these

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complex metabolites. Hence, we set out to develop and apply a suite of advanced

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metabolomic methods to utilize comprehensive profiling of polyphenols and related

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compounds and test these methods by chemometrics.

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Miniaturization of column and instrumentation is one of the leading trends in the field of liquid

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chromatography. Ultra-high pressure liquid chromatography (UHPLC) columns with internal

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diameters (I.D.) of 2.1 mm have long taken over conventional HPLC columns of 4.6 mm for

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their higher sensitivity, faster separation and less consumption of organic solvents.11 For the 3 ACS Paragon Plus Environment


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same reasons, 1 mm I.D. columns have been developed to further improve the sensitivity

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compared to 2.1 mm I.D. columns.12 Consequently, 1 mm I.D. columns have been applied to a

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variety of studies of small molecules such as amino acids,13 flavonoids,14 and antibacterial

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drugs.15

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constraints, because both the flow rate and the extra-column dispersion source need to be

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scaled down for optimal performance as the column volume decreases.16 Recently,

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commercial instruments designed for micro LC performance have become available, such as

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the Eksigent MicroLC 200 system and Dionex UltiMate 3000 Quaternary Micro LC system. A

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previous study showed that the microLC instrument provided significantly higher efficiency for

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1 mm I.D. columns compared to common UHPLC instrument.17 At the same time, more small-

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sized columns have been developed.18, 19 We here evaluated different columns and optimized

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microLC methods for comprehensive polyphenol profiling in red wines.

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Materials and Methods

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Comparison of 1 mm I.D. columns using polyphenol standards. An Eksigent MicroLC 200

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was coupled to a SCIEX TripleTOF 5600 (SCIEX, Redwood City, CA, USA) for MS

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measurements from m/z 60 to 1250 in electrospray positive mode with spray voltage 5.5 kV at

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scan rate of 10 spectra per second. Three reversed-phase C18 columns of 1 mm I.D. and 5

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cm length were examined, including Kinetex C18 2.6 µm (Phenomenex, Torrance, CA, USA),

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InertSustain C18 2.0 µm (GL Sciences, Tokyo, Japan) and Acquity CSH 1.7 µm (Waters,

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Milford, MA, USA). The Kinetex and InertSustain columns were prototype columns, while

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Acquity was a commercially available column. The standard mixture for LC/MS experiments

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contained (1) Umbelliferone, (2) Rutin, (3) Naringin, (4) Quercetin, (5) Naringenin, (6) 6-

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Hydroxyflavone, (7) Genistein, (8) Genistin, (9) Daidzein, (10) Daidzin, (11) Glycitein, (12)

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Glycitin, (13) Chrysin at 1.0 µg/mL each, in 10:90 methanol:water. Mobile phase A was water

However, practical use of 1 mm I.D. columns is still limited due to instrumental


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with 0.1% acetic acid while mobile phase B was acetonitrile with 0.1% acetic acid. The LC

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gradient was 10% to 90% B with different flow rates (0.025, 0.05, 0.075, 0.1 mL/min) and

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gradient time or gradient volume (4 min, 8 min, and 0.6 mL). Injection volume was 0.2 µL. All

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experiments were carried out in duplicate. Data was processed by the SCIEX MultiQuant 2.1

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software.

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LC gradient optimization using a red wine example. To optimize LC gradients for total peak

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discovery, metabolomic tests were performed with wine extracts from a 2012 Petite Sirah

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(Concannon Vineyard, Livermore, CA). 500 µL of raw wine was added to an ice-cold pre-

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mixed extraction solvent consisting of 500 µL water and 500 µL ethyl acetate. Samples were

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shaken for 5 min, centrifuged for 3 min at 16,100 rcf, and then 300 µL of the ethyl acetate

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phase was transferred to a second vial and evaporated to dryness using a speed vacuum

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concentrator. As method blank control, instead of raw wine, 500 µL of water was added to the

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extraction solvent and then the same protocol was applied. On the day of the experiment,

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samples were reconstituted in 150 µL of 90:10 acetonitrile:water, briefly vortexed, sonicated for

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5 min, centrifuged for 3 min at 16,100 rcf then transferred to HPLC vials with inserts. The

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same gradient series and mass spectrometry measurements were applied as to the

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polyphenol standards. The raw WIFF data files were converted to ABF files with Reifycs

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Analysis Base File Converter (Reifycs Inc., Tokyo, Japan) and processed by the freely

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available MS-DIAL20 software (ver. 1.58) (http://prime.psc.riken.jp/Metabolomics_Software/).

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Metabolome composition analysis of six red wines using the optimized microLC-MS/MS

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method. Six bottles of red wines were opened and extracted fresh, including a 2009 Syrah

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(Putah Creek Winery, Solano county, CA), a 2012 Pinot Noir (Black Mountain Vineyard,

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Sonoma County, CA), a 2010 Cabernet Sauvignon (Mosaic Winery, Alexander Valley, Sonoma

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County, CA), a 2012 Zinfandel (Cline Cellars Winery, Sonoma County, CA), a 2012 Petite 5 ACS Paragon Plus Environment


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Sirah (Blue Fin Winery, Napa, CA), and a 2012 Merlot ( Blue Canyon Wine Company,

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Greenfield, CA). A panel of fourteen non-trained adults tasted the remaining wines directly

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afterwards. Overall likeability21 (averaging aroma, flavor and finish) was scored from 1 (worst)

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to 10 (best) for each wine with blinded labels. Raw wines were extracted in the similar way as

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the preliminary test, but instead of reconstituting the extracts with 150 µL solvent, 75 µL was

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used to concentrate the samples. Six technical replicates were prepared for each wine as well

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as the method blank. As result of the microLC-MS method optimization, a gradient of 4 min at

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a flow rate of 0.05 mL/min was selected for data-dependent MS/MS acquisition for these

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wines. To compare the data-dependent and data-independent (SWATH) method, a 12-min

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gradient was used at a flow rate of 0.05 mL/min. MS/MS data was acquired in both positive

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and negative mode at a collision energy of 30 ± 15 V. For data-dependent acquisition of

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MS/MS, precursor ions from m/z 60 to 1250 were fragmented with a cycle time of 0.125 s. For

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data-independent acquisition (SWATH), precursor ions from m/z 60 to 800 were fragmented,

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with a cycle time of 0.4 s. A SWATH window of 50 Da was used.

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Data processing, statistics analysis and visualization. Peak picking and alignment was

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performed by MS-DIAL (ver. 1.58). Representative MS/MS spectra were exported from MS-

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DIAL in MGF format for compound identification. NIST MS PepSearch was used to search the

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MGF file against MS/MS libraries, including NIST14, MassBank,22 ReSpect23 and METLIN.24

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For the remaining unknown compounds, the freely available MS2Analyzer25 software

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(https://sourceforge.net/projects/ms2analyzer/) was used to search for characteristic neutral

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losses for glycosides, including pentosides (132.063 Da), hexosides (162.053 Da),

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deoxyhexosides (146.058 or 164.068 Da), acetylhexosides (204.063 Da), and hexuronide

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(194.043 Da). An m/z window of 0.005 Da and a relative intensity threshold of 0.5 were

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selected as input parameters. Each hit from MS2Analyzer output was queried by its


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neutralized molecular mass in Phenol-Explorer and by its precursor m/z value in the online

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METLIN database to extract potential structure candidates.

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Principle component analysis (PCA) was performed for all the annotated compounds (reverse-

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dot score>800) in positive ionization mode with MetoboAnalyst.26 Before PCA analysis,

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samples were normalized by sum, followed by log transformation and pareto scaling.

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Orthogonal signal correction partial least squares (O-PLS) modeling was performed between

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the average tasting scores and peak intensities of all the annotated compounds using Devium

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(https://github.com/dgrapov/devium). Two latent variables (LVs) were determined based on

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the criteria of maximum Q2 in the range of LV1 to LV10. Twenty permutation tests were used

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to validate the model, and ten top features were selected for each sign. At last, a chemical

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structural (Tanimoto>0.9) and mass spectral similarity (similarity>0.4) network was created for

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all the annotated compounds in positive ionization mode with MetaMapR27 and Cytoscape.28

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MS/MS spectra were pre-treated (m/z rounded up to 0.1, intensity filter>5% of base peak)

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before the analysis to improve the similarity score.

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Results and Discussion

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Method optimization: concepts and methods. A good chromatographic method is essential

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to LC-MS experiments to minimize ion suppression effects, to help distinguish isomers, and to

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increase the number of features detected and annotated in untargeted metabolomics. In this

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work, we used two parameters, peak capacity and peak height, to quantify the performance of

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chromatographic separations. Peak capacity is defined as number of peaks that can be

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separated with unit resolution within the gradient time with a one-channel detector. Peak

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intensity, here defined as peak height, is a measure of method sensitivity. It is noteworthy that

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peak intensity is not only related to sample concentration, injection volume and detector

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sensitivity, but also strongly influenced by chromatographic performance. For example, 7 ACS Paragon Plus Environment


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changing the column internal diameter from 2.1 mm to 1 mm can increase peak intensity by a

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factor of 4.41.12

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However, chromatography is a process involving many factors, including stationary phase,

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particle size, column size, mobile phase, flow rate, gradient, etc. To optimize the microLC-MS

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method for phenolic compounds, we used different stationary phases, particle sizes, flow rates

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and gradients to separate thirteen standard phenol compounds as well as a preliminary red

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wine sample and evaluate the performance of chromatography based on peak capacity, peak

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height and peak numbers extracted from red wine sample.

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Method optimization: column selection with phenol standards. First we compared the

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performance of three 1 mm I.D., 5 cm length columns in terms of peak capacity and intensity:

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the Kinetex C18 (particle size, 2.6 µm), the InertSustain C18 (2.0 µm), and the Acquity CSH

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(1.7 µm) columns. To test the effects of various liquid chromatography conditions on those

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columns, experiments were performed under eleven gradient conditions at flow rates of 0.025

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to 0.1 mL/min. Averaged peak widths and peak capacities were calculated for all the standard

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peaks as in the eq. 1-2.

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Peak width = ( peak width at half height / 2.35) × 4

(1)

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Peak capacity = gradient time / peak width

(2)

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Figure 1 shows extracted ion chromatograms (EICs) of the phenol standards separated by the

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Kinetex column in a 4 min gradient at 0.05 mL/min, with an average peak width of around 2 s.

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Figure 2 shows peak capacities and average peak intensities for the three columns under one

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representative gradient condition (see all the results in Table S1). In general, the tested

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polyphenols were eluted at later retention times using the InertSustain column in comparison

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to the Acquity column, hence, showed increased ionization efficiency and wider peaks 8 ACS Paragon Plus Environment

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because they eluted at higher organic/water solvent ratios. Taking the Kinetex column as an

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example to investigate the chromatography under different LC conditions, we observed the

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following trends: 1) peak capacity increased as the gradient time and flow rate increased; 2)

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peak height, by contrast, decreased as the gradient time and flow rate increased; 3) when the

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gradient volume (flow rate × gradient time) was constant, peak capacity and peak height also

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remained relatively constant. Here we estimated the optimum conditions by calculating and

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comparing the product of the peak capacity and peak height for each gradient condition.

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Based on the calculation (Table S1), an optimum condition for the 1 mm I.D. column was found

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using a 4 min gradient at 0.05 mL/min for the given polyphenol standards. Overall, under the

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conditions employed here, the Kinetex column provided the highest peak capacity and

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intensity among the three columns. We therefore selected this column in all following wine

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polyphenol studies. Other columns may perform better under optimized conditions for each

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column.

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Method optimization: gradient finalization with red wine sample. The same gradient

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experiments were repeated with an extracted test red wine sample to evaluate the effect of the

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LC parameters under realistic conditions. The data was processed by MS-DIAL with an

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intensity threshold of 4,000 counts. The numbers of detected m/z-retention time (rt) features is

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shown in Table 1. The trend was similar to the trend of peak intensities, indicating that peak

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intensity contributed more to the overall m/z-rt count than peak capacity. With a given intensity

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threshold for the data processing, higher peak intensities generally led to more detected peaks

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even if peaks were a little broader, on average, than comparative methods. Hence, the peak

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capacity parameter was less important to overall feature detections because mass

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spectrometers are very effective multi-channel detectors, enabling selective quantification of

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many compounds even if these are co-eluting (except for isomers). We observed that

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separation of isomers was related to the peak capacity, but it was also affected by the 9 ACS Paragon Plus Environment


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selectivity of the stationary phase and mobile phase. In conclusion, the 4 min gradient at flow

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rate of 0.05 mL/min that was selected by polyphenol standards was also confirmed to be

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highly suitable in the comprehensive metabolomics analysis of red wine samples.

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MicroLC-MS/MS analysis of six red wines: Compound identification. Six red wines from

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different grape varieties and brands were analyzed to showcase the application of the

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optimized microLC-MS/MS method. Six replicates of each red wine were extracted with ethyl

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acetate29 and measured in positive and negative electrospray ionization mode. Data-

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dependent MS/MS spectra were acquired to facilitate structural annotation of metabolites. MS-

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DIAL was used for peak detection and alignment, while NIST MS PepSearch was used to

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search the MS/MS spectra against libraries including NIST14, MassBank, ReSpect and

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METLIN. 264 compounds were annotated with a weighted reverse-dot score30 higher than

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800. Among the 264 compounds, 165 metabolites were classified as phenol compounds. The

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full list of annotated compounds is presented in Table S2 including detailed metadata such as

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retention times, precursor m/z, InChIKeys, and intensity tables. To further validate the

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annotation results we confirmed 18 compounds by injecting authentic reference standards

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(see Table 2).

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For many peaks, MS/MS library searches assigned the same compound name to multiple m/z-

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rt features. By definition, compounds that show different physicochemical properties (such as

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retention time) but having the same exact mass are isomers. We further defined here close

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positional isomers if peaks were well separated and MS/MS library queries yielded reverse-dot

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score higher than 800. Among the 264 annotated compounds, 133 were found with one or

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more isomers, due to the complexity of polyphenol structures and high possibility for positional

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isomers. Figure 3 demonstrates the detection of a novel resveratrol isomer in addition to the

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well-known cis- and trans-forms. Other abundant isomers include, for example, iso10 ACS Paragon Plus Environment

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dihydroquercetin,

iso-procyanidin

B2,

and

iso-caffeic

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characterization of such isomers is impossible by MS alone, given the large number of

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theoretically possible positional isomers, the limited availability of authentic standards and the

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complexity of wine samples that forbids easy routes for peak purifications and nuclear

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magnetic resonance (NMR) determinations. For example, for five dihydroquercetin isomers

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detected in the red wines samples, 95 candidate structures were found in the structure

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database ChemSpider, but only one compound is available to be purchased from Sigma-

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Aldrich.

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Although 264 compounds have been annotated using MS/MS library search, more than 1000

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features which are related to true compounds still remain unknown. To further characterize the

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structures or substructures of unknowns, we used MS2Analyzer in combination with online

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databases such as Phenol-Explorer and METLIN. MS2Analyzer is a software tool using

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characteristic neutral losses and product ions for small molecule substructure annotation.

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Since many polyphenols are in glycoside forms, we queried the characteristic neutral losses

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for pentosides, hexosides, deoxyhexosides, acetylhexosides, and hexuronide in all the

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unknown MS/MS spectra with MS2Analyzer. For those MS/MS spectra containing the above

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neutral losses, their precursors were searched in the Phenol-Explorer and METLIN databases.

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Potential candidates with matching accurate mass and substructures for 28 features were

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reported in Table S3. Figure 4 shows an example of MS/MS spectrum, characteristic neutral

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loss, and potential structure of unknown m/z 521.132 eluting at 1.68 min.

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Over the past few years, data independent MS/MS acquisition (DIA, specifically SWATH)

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method has drawn a lot of interests from LC-MS researchers. In contrast to the conventional

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data dependent MS/MS acquisition (DDA) method, DIA allows the acquisition of MS/MS

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spectra for all the MS peaks simultaneously regardless of their peak intensities. However, DIA 11 ACS Paragon Plus Environment

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A

complete

structural


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is not very suitable for the narrow peaks in our 4 min gradient; longer gradients need to be

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applied to ensure there are enough data points in each chromatographic peak so MS/MS

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deconvolution can be performed. To compare DDA and SWATH methods fairly, we extended

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the gradient time to 12 min and analyzed the six red wines using both methods in positive

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mode. After data processing, we observed a similar total number of m/z-rt features detected

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for both methods (1632 for SWATH and 1667 for DDA). However, the SWATH method

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provided more features with MS/MS spectra (1632 VS 1461) and hence more compound

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annotations (207 vs 178 structurally annotated peaks). In summary, SWATH method expands

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the coverage of the untargeted LC-MS profiling experiments, but may need to sacrifice the

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chromatography performance to some extent.

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MicroLC-MS/MS analysis of six red wines: Statistics and Visualization. Using relative

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quantitation and principle component analysis (PCA) clustering of all annotated compounds in

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positive ionization mode (Figure 5), clear differences were observed in the metabolite profiles

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of the six red wines. Loadings for PC1 and PC2 were available in Table S4 in Supporting

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Information. Specifically, Syrah and Cabernet Sauvignon were separated very well from the

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four other wines along PC1, indicating that they have the overall most similar metabolite

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profiles. For example, triethyl citrate and syringic acid were found to be much more abundant

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in Cabernet Sauvignon and Syrah than in other four wines, shown in Figure 6. Similarly, the six

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wines were separated on PC2, but not clearly clustered into two groups like on PC1. Other

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metabolites also showed very large variance among the tested wines. For example, trans-

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resveratrol was up to 6-fold more abundant in Pinot Noir and Merlot than in the other four

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wines, while the novel iso-resveratrol showed a similar pattern but was less abundant in

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Merlot. By contrast, caffeic acid and one of its isomer showed very different distribution

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patterns in the six red wines (Figure 6). To improve the accuracy of relative quantitation, a set

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of internal standards could be developed to normalize the peak intensities. Considering the 12 ACS Paragon Plus Environment

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complexity of compounds measured and their various physical and chemical properties, a

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single internal standard was not adequate to address the difference in the extraction and

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ionization processes,

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One of the potential applications of our untargeted metabolic data was to link the taste of red

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wines to their chemical components. As a prove of concept, we used the subjective tasting

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scores from a panel of fourteen untrained adults (Table S5) and our metabolic data to build an

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orthogonal signal correction partial least squares (O-PLS) model. Q2 and root mean squared

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error of prediction (RMSEP) for LV2 was 0.990 and 0.158, respectively. Features that were

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positively or negatively associated with the tasting scores could be selected, as shown in

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Figure S1 and Table S6 in Supporting Information. The sample size in this sensory study may

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not be sufficient to reveal the true association between the red wine components and overall

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sensory experience; a larger scale study with more red wines and panelists would be needed

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for that purpose.

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In summary, the optimized microLC-MS/MS screening workflow combined a novel LC method

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with chemometrics strategies from untargeted metabolomics, and was applied to the analysis

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of red wines. It has multiple advantages compared to other published LC-MS method in the

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red wine studies, shown in Table 3. First of all, our method is only 5.5 min long including

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column wash and re-equilibration, allowing a cost effective high throughput operation in large

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trials. Secondly, with a flow rate of 0.05 mL/min, each sample just needs 0.275 mL of mobile

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phase, thus it only take 27.5 mL of mobile phase to run 100 sample which saves costs for both

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purchase and discarding organic solvents. Last, with untargeted data acquisition and

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chemometrics method, we are able to structurally annotate more than 200 compounds using

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comprehensive MS/MS libraries in a single run. Compared to other methods focusing on few



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target known compounds, metabolomics is clearly more comprehensive and will build much

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more robust methods for sensory tasting and quality control outputs.

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Supporting Information

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The Supporting Information is available free of charge on the ACS Publications website.

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Table S1. Peak capacities, average peak heights and their products of 13 phenol standards

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separated by three 1 mm I.D. columns on Ekigent MicroLC-MS.

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Table S2. All annotated compound information in the six red wines.

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Table S3. Compound class and tentative structure annotation of unknown compounds in

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glycoside forms.

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Table S4. Loadings for PC1 and PC2 in the PCA analysis.

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Table S5. Range, mean and standard deviation of the tasting scores

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Table S6. Ten most important features positively or negatively correlated to the tasting score,

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selected by the OPLS modeling.

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Figure S1. Orthogonal signal correction partial least squares (O-PLS) selection for the 10

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most important features positively or negatively correlated to the average tasting score of an

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untrained panel of 14 adults.

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Author Information

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Corresponding author

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*(O.F.) Email: [email protected]. Phone: +1-530-754-8258. Fax: +1-530-754-9658

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Funding 14 ACS Paragon Plus Environment

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This work was financially supported by the following grants: NIH U24 DK097154, NSF MCB

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1139644. Wines were exclusively purchased from private funds and did not use any federal

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funds.

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Notes

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The authors declare no competing financial interest.

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Acknowledgement

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The authors thank Masayoshi Ohira of GL Sciences and Tivadar Farkas of Phenomenex for

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providing the prototype columns.

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Figure captions Figure 1. Extracted ion chromatograms of 13 polyphenol reference standards using the 1 mm I.D. Kinetex C18 column and a 4 min acetonitrile/water gradient at 0.05 mL/min flow rate. Sensitivity and peak capacity calculations were performed on the more abundant peaks if minor impurities were detectable as well in the authentic standards chromatograms. Figure 2. Comparison of three 1 mm internal diameter C18 columns with respect to average peak intensities and peak capacities for 13 polyphenol reference standards using a 4 min acetonitrile/water gradient at 0.05 mL/min flow rate. Figure 3. Example of compound annotation using MS/MS matching. Annotation of a novel resveratrol isomer showing (A) the extracted ion chromatogram at m/z 229.085 ([M+H]+) at 1.54 min and (B) matching the accurate mass MS/MS spectra of the new isomer against cisresveratrol authentic standards. Figure 4. Example of substructure annotation for unknown compounds using MS2Analyzer. An unknown MS/MS spectrum with precursor m/z 521.132 at 1.68 min in positive mode was annotated as acetylhexoside for the characteristic neutral loss of 204.067. One potential structure candidate, petunidin 3-O-(6''-acetyl-glucoside), was extracted from Phenol-Explorer using accurate mass match. Figure 5. Principal component analysis (PCA) score plot of tested six red wines. Total quantitative variances of 177 annotated peaks in positive ionization mode were clustered to reveal the difference and relative similarities of metabolite profiles of different red wines. 95% confidence regions (Hotelling’s T2 eclipse) were displayed for each class. Figure 6. Peak intensities of selected compounds contributing to the difference of the metabolite profiles of the six red wines: (A) trans-resveratrol; (B) iso-resveratrol; (C) caffeic 21 ACS Paragon Plus Environment


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acid, negatively correlated to the tasting score; (D) iso-caffeic acid, positively correlated to the tasting score; (E) malathion, a pesticide; (F) triethyl citrate, only present in two of red wines. Figure 7. Chemical structure and mass spectral similarity network (Tanimoto>0.9, spectra similarity>0.4) for compounds annotated in positive ionization mode.

Peak intensity fold

change of Syrah (highest tasting score) versus Pinot Noir (lowest tasting score) was mapped onto the network. OPLS selected features were marked with boarders.

Tables Table 1. Number of peaks detected in one testing red wine sample using Kinetex C18 1.0 mm I.D. column under various LC conditions. The gradient was 10% to 90% mobile phase B (acetonitrile with 0.1% acetic acid) at different flow rate (FR, in mL/min) and gradient time (tG, in min).

tG FR

4

6

8

0.025

571

472

0.050

754

572

0.075

687

575

0.100

664

621

12

24 164

471

551


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Table 2. Example compounds annotated and confirmed by authentic reference standards in the six red wines. All compounds were detected as [M+H]+ except for gallic acid ([M-H]-).

Name Gallic acid (+)-Catechin (-)-Epicatechin Caffeic acid Syringic acid p-Coumaric acid Ferulic acid N-Acetyl-L-phenylalanine Dihydroquercetin DL-Indole-3-lactic acid Isofraxidin Myricetin 3-(2-Hydroxyethyl)indole trans-Resveratrol Quercetin cis-Resveratrol Phloretin Naringenin

Rt (min) 0.70 1.03 1.14 1.18 1.19 1.43 1.49 1.49 1.52 1.57 1.62 1.68 1.79 1.84 1.98 2.03 2.18 2.22

m/z 169.014 291.086 291.087 181.049 199.060 165.054 195.065 208.096 305.066 206.081 223.060 319.045 162.091 229.086 303.050 229.086 275.091 273.076

Error (mDa) -0.1 -0.1 -0.2 0.1 0.2 0.5 0.7 0.6 -0.1 0.6 0.6 0.0 0.4 0.2 -0.2 0.3 0.2 0.2

Table 3. Comparison of the microLC-MS method to recent alternative methods.

Study This study Pati et al. 201431 Cui et al. 201232 Greco et al. 201210 Mirnaghi et al. 20129 Jaitz et al. 20108

LC time (min) 5.5 40 40 27 20 13

flow rate (mL/min) 0.05 0.2 0.8 0.4 0.2 0.4

mobile phase 100 samples (mL) 27.5 800 3200 1080 400 520

compounds annotated 200+ 26 20 13 8 11



Figure graphics Figure 1


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Figure 2

Figure 3

Figure 4



Figure 5

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Figure 7



Table of Contents Graphics


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Ultrafast Polyphenol Metabolomics of Red Wines ... - ACS Publications

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