A triple quadrupole and a hybrid quadrupole orbitrap mass

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Food and Beverage Chemistry/Biochemistry

A triple quadrupole and a hybrid quadrupole orbitrap mass spectrometer in comparison for polyphenol quantitation Chiara Cavaliere, michela antonelli, Anna Laura Capriotti, Giorgia La Barbera, Carmela Maria Montone, Susy Piovesana, and Aldo Laganà J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b07163 • Publication Date (Web): 12 Apr 2019 Downloaded from http://pubs.acs.org on April 12, 2019

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Journal of Agricultural and Food Chemistry

A triple quadrupole and a hybrid quadrupole orbitrap mass spectrometer in comparison for polyphenol quantitation

Chiara Cavaliere, Michela Antonelli, Anna Laura Capriotti, Giorgia La Barbera*, Carmela Maria Montone, Susy Piovesana and Aldo Laganà. Department of Chemistry, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, Rome, Italy

Corresponding author: Giorgia La Barbera, PhD Università di Roma "La Sapienza" Dipartimento di Chimica P.le Aldo Moro 5, 00185 Rome (Italy) Phone: +39 0649913834 E-mail [email protected]

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Abstract

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Liquid chromatography coupled to low resolution mass spectrometry (LRMS) has historically been

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a popular approach for compound quantitation. Recently, high resolution mass spectrometry (HRMS)

4

technical developments led to the introduction of new approaches for quantitative analysis. Whereas

5

the performances of HRMS have been largely assessed for qualitative purposes, there are still

6

questions about its suitability for quantitative analysis. Several papers on LRMS and HRMS

7

comparison have been published, however, none of them was applied to polyphenol quantitation. In

8

this work, a comparison between HRMS, operated in data dependent acquisition mode, and LRMS,

9

operated in selected-reaction-monitoring mode, was performed for polyphenol quantitation in wine.

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The two techniques were evaluated in terms of sensitivity, linearity range, matrix effect and precision,

11

showing the better performances of HRMS. The suitability of HRMS for quantitation purposes as

12

well as qualitative screening makes HRMS the new technique of choice for both targeted and

13

untargeted analysis.

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Keywords:

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high resolution mass spectrometry; low resolution mass spectrometry; polyphenols; quantitation;

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wine.


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Introduction

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In the last decade, targeted screening by means of ultra-high performance liquid chromatography–

19

tandem mass spectrometry (UHPLC–MS/MS) became a popular approach for the analysis and

20

quantitation of several analytes such as drugs1, metabolites2, contaminants3, and phytochemicals4. In

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particular, triple-quadrupole mass spectrometers (QqQ-MS) in selected-reaction-monitoring (SRM)

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mode is considered the work horse for quantitative analysis, because of its good sensitivity and wide

23

dynamic range5. In the last years, however, the development of new improved UHPLC

24

instrumentations, together with the advent of high resolution mass spectrometry (HRMS), led to the

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introduction of new approaches and techniques for targeted quantitative analysis. With the modern

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hybrid high resolution mass spectrometers such as quadrupole-orbitrap-MS (Q-orbitrap-MS) and

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quadrupole-time-of-flight-MS (Q-TOF), new acquisition modes can be chosen to both record high

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resolution full scan and MS/MS spectra, allowing sensitive quantitative and qualitative analysis at the

29

same time6. UHPLC-HRMS technique, showing the versatile capability of performing quantitative

30

and qualitative analyses, can be seen as a true alternative to low resolution QqQ-MS. Indeed, in

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contrast to targeted SRM modes by QqQ-MS, HRMS instrumentations operated in data dependent

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acquisition (DDA) or data independent acquisition (DIA), can be applied for both targeted and

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untargeted screening approaches. A more complete overview of the composition and content of a

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sample extract could be preferred to targeted quantitation methods for assessing a more

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comprehensive characterization of samples.

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HRMS versatility is an unquestionable potentiality which, together with the increasingly availability

37

of new affordable HRMS instrumentations in the market, could shift the attention in favour of HRMS

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for both qualitative and quantitative analysis7–13. However, whereas the potentialities of HRMS for

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qualitative analyses have been largely assessed, there are still remaining questions on its quantitative

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analysis performances, especially when compared with QqQ-MS operated in SRM mode.



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Several papers have been published on the comparison between QqQ-MS and HRMS

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instrumentations for quantitative analysis, showing either better, equal or worse performances of

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HRMS platforms compared to QqQ-MS. Data reported in the literature are often inconsistent with

44

each others, leading to the need of further deeper and comprehensive evaluations. Moreover, whereas

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several studies on comparison between LRMS and HRMS performances have been focused on

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contaminants, drugs and metabolites8,10,11,14,15, no study on the quantitative performances of QqQ-MS

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versus HRMS instruments for polyphenol quantitation has been published in the literature.

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Plants have a very large metabolome including substances that differ in molecular weight, physico-

49

chemical characteristics and dynamic range16,17. Thus, despite the developing of recent

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instrumentations, the quantitative analysis of phytochemicals remains challenging. SRM acquisition

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with QqQ-MS is a well-established procedure which has been largely used for polyphenol

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quantification4. However, the Orbitrap-based instruments can also provide reproducible

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quantification results and large linear range18. Nevertheless, they are rarely exploited for polyphenol

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quantification17.

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The lack of a comparison between QqQ-MS and HRMS platforms for polyphenol analysis is

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surprising considering the importance of polyphenol characterization in vegetable and food matrices,

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and the challenge in the developing of quantitation methods for these complex matrices. However, it

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could be justified by the difficulty in availability of blank samples and the unpredictable matrix effects

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on compound quantification. The standard addition method is the most suitable one to overcome these

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limits. However, the setting of a standard addition method for the comparison of two instruments is

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a particular taught issue, due to the difference of instrumental response. In our opinion, however,

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assessing the performances of LRMS and HRMS for quantitative analysis of polyphenols is a

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particularly interesting and unexplored issue requiring further investigation.

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On this purpose, we aimed at focusing our work on evaluating the performances of a QqQ-MS and a

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hybrid Q-orbitrap-MS for the quantitative analysis of polyphenols in rosé wine. In particular, 50

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polyphenols belonging to different classes such as flavonoids, isoflavones, proanthocyanidins, 4 ACS Paragon Plus Environment

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anthocyanins and phenolic acids were analysed by means of UHPLC coupled by electrospray

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ionization (ESI) to either a TSQ vantage QqQ-MS or a Qexactive hybrid Q-orbitrap-MS. Same

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calibration samples of polyphenols were injected on the two systems performing the same

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chromatographic separation. Method validation was performed in parallel by assessing limit of

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detection (LOD), limit of quantification (LOQ), linearity of dynamic range, matrix effect (ME) and

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precision.

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Materials and methods:

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Standard solutions and samples

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Reagents and materials

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Ultrapure water H2O Optima LC-MS Grade and Acetonitrile Optima LC-MS Grade were purchased

78

from Fisher Scientific (Rodano, Italy). Methanol HiPerSolv CHROMANORM ultra LC-MS and

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Ethanol HiPerSolv CHROMANORM ultra LC-MS were purchased from VWR International (Milan,

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Italy), Formic acid ultrapure for mass spectroscopy was purchased from Fluka analytical. The

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following reference standard compounds were purchased from Sigma-Aldrich (St.Louis, MO, USA):

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biochanin A, caffeic acid, callistephin (pelargonidin-3-glucoside), catechin gallate, cinnamic acid,

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coumaric acid, coumestrol, cyanidin chloride, daidzein, diosmetin, epicatechin, epicatechin gallate,

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equol, ferulic acid, flavone, formononetin, gallic acid, glycitein, hesperetin, kaempferol, kuromanin

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chloride (cyanidin-3-O-glucoside), malvidin 3-galactoside chloride, myricetin, naringenin, peonidin-

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3-O-glucoside chloride, primuletin, procyanidin B1, procyanidin B2, quercetin dihydrate, quercetin-

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3-O-glucoside,

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trihydroxyflavone. The following reference standard compounds were purchased from Extrasynthese

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(Lyon, France): catechin, eriodictyol, genistein, hesperidin (hesperetin-7-O-glucoside), isorhamnetin,

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luteolin, luteolin-7-O-glucoside, malvidin chloride, morin, phloretin, syringetin. The following

resveratrol,

rutin

(quercetin-3-O-rutinoside),


syringic

acid,

taxifolin,


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reference standard compounds were purchased from Fluka Biochemika (Milan, Italy): apigenin,

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apigenin-7-glucoside, chlorogenic acid hemihydrate, genistin (genistein-7-glucoside).

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Rosé wine sample

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Rosé wine, with an alcoholic content of 12.5% (v/v %), was purchased in a common supermarket. As

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soon as the wine bottle was opened, rosé wine was aliquoted under nitrogen flow and the aliquots

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were stored at -20°C. Prior to analysis rosé wine samples were thawed at room temperature, filtered

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through Acrodisc syringe filters with a 0.2 μm GH Polypropilene membrane (Pall, Ann Arbor, MI,

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USA) and either directly analysed or added with the reference standard compounds for building

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matrix matched calibration curve, as reported in the following paragraphs.

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Stock solutions

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Certified 1 gL-1 standard solutions of 50 reference standard compounds were dissolved in methanol

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or acetonitrile. Individual stock solutions were diluted in MeOH at 10 ng µl-1 for direct injection into

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the mass spectrometers. In addition, individual stock solutions were diluted with MeOH at the

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appropriate concentration to prepare a mixture stock solution. Starting from the most concentrated

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mixture stock solution (indicated as 1x), 5x, 10x, 50x, 100x, 500x, 1000x, 5000x, 10000x solutions

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were prepared for successive dilution with MeOH. The concentration of each compound in the

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mixture stock solutions, at several dilution levels, has been reported in Table S1.

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Finally, biochanin A and equol, that were chosen as internal standards for negative and positive

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polarity acquisition, were diluted at 5 ng µl-1 and 2.5 ng µl-1, respectively. All stock solutions were

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stored at –20◦C.

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Calibration curves

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A solvent calibration curve. including 10 dilution levels (included blank sample). was built as follows:

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20 µl of each mixture stock solution reported in the previous paragraph was added to 400 µl of water,

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60 µl of Ethanol and 10 µl of biochanin A C=5 ng µl-1 and 10 µl of equol C=2.5 ng µl-1. In addition, 6 ACS Paragon Plus Environment

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a blank solution was prepared substituting the mixture stock solution with 20 µl of MeOH. The final

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composition of each working solution was H2O/MeOH/EtOH = 80/8/12 (v/v/v).

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A matrix matched calibration curve, including 10 dilution levels, was built as follows: 20 µl of each

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mixture stock solution reported in the previous paragraph was added to 460 µl of wine, and 10 µl of

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biochanin A C=5 ng µl-1 and 10 µl of equol C=2.5 ng µl-1. In addition, a blank solution was prepared

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substituting the mixture stock solution with 20 µl of MeOH. Based on the wine alcoholic

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concentration reported in the label, the final composition of each working solution was

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H2O/MeOH/EtOH = 80.5/8/11.5 (v/v/v).

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The concentration of each compound in the mixture working solutions, at several dilution levels, have

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been reported in Table S1.

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All working solutions were splitted in three aliquots, placed in injection vials and stored at -20°C

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prior to instrumental analysis.

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UHPLC and mass spectrometric conditions

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UHPLC systems

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The MS instruments were connected to two similar LC systems. In particular, the QqQ-MS was

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connected to a UHPLC Ultimate 3000 binary pump (Thermo Fisher Scientific, Brema, Germany),

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whereas the Q-orbitrap-MS was connected to an UHPLC Vanquish binary pump H (Thermo Fisher

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Scientific, Brema, Germany). Both UHPLC systems are equipped with a thermostatted autosampler

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and a thermostatted column compartment. The chromatographic separation was carried out on a

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Kinetex core–shell C18 column (100 mm × 2.1 mm) with particles size of 2.6 μm (Phenomenex,

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Torrance, CA, USA) at 40°C and at a flow rate of 600 μL min−1. The mobile phase was H2O–

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HCOOH (99.9:0.1 v/v; solvent A) and ACN–HCOOH (99.9:0.1 v/v; solvent B); the elution

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gradient was as follows: 5% solvent B for 3 min, 5% solvent B to 15% solvent B in 10 min, 15%



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solvent B to 35% solvent B in 15 min, 35% solvent B to 50% solvent B in 5 min, 99% solvent B

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for 5 min, 5% solvent B for 8 min. The injection volume was 5 μL for all the samples.

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ESI

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The chromatographic systems were coupled to the mass spectrometers by means of the same heated

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ESI source design H-ESI-II (Thermo Fisher Scientific, Brema, Germany). Exact same position (C)

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and conditions were used on the Q-orbitrap-MS and QqQ-MS. In particular, the ESI source

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parameters were set as follows: capillary temperature 275°C in positive mode, 350°C in negative

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mode; sheath gas 50 arbitrary units (a.u.) in positive mode, 55 a.u. in negative mode; auxiliary gas

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15 a.u.; sweep gas 2.25 a.u. in positive mode, 3 a.u. in negative mode; spray voltage 3500 V in

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positive mode, 2500 V in negative mode; auxiliary gas heater temperature 450°C in positive mode,

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300°C in negative mode.

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Q-orbitrap-MS

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The HRMS instrument was a Q Exactive hybrid quadrupole Orbitrap mass spectrometer (Thermo

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Fisher Scientific, Brema, Germany). The detection was conducted in DDA in both positive and

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negative polarity mode. MS data were acquired in the m/z 100–600 with a resolution (full width at

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half maximum, FWHM, at m/z 200) of 35,000. The automatic gain control (AGC) target value was

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200,000 in full-scan mode. The maximum ion injection time was 100 ms. The isolation window

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width was 2 m/z. MS2 fragmentation was performed on the five most intense ions detected in full-

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scan mode with a resolution FWHM of 17,500. The AGC target value was 100,000. Dynamic

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exclusion was set to 3s. An exclusion list was set containing the ions most commonly detected in

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the blank and an inclusion list was set containing the m/z and retention time of the monitored target

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compounds. Fragmentation was achieved in the higher energy collisional dissociation (HCD) cell at

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a normalized collision energy (NCE) of 80. The analytes of interest are reported in Table 1 together

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with their retention time, precursor ion mass and product ion scan. The Q-orbitrap-MS was calibrated

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every two days by using the Pierce LTQ Velos ESI Positive Ion Calibration Solution (Thermo Fisher 8 ACS Paragon Plus Environment

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Scientific, Brema, Germany), and the Pierce ESI Negative Ion Calibration Solution (Thermo Fisher

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Scientific, Brema, Germany) injecting it in infusion at 5 μL min-1. The sweep cone and ion transfer

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tube were washed every day prior to analysis. LC-HRMS data acquisition, peak integration and

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quantitation were performed by using the software Xcalibur v.2.1 (Thermo Fisher Scientific, Brema,

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Germany).

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QqQ-MS

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The QqQ-MS was a TSQ Vantage EMR triple quadrupole mass spectrometer (Thermo Fisher

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Scientific, Brema, Germany). The detection was conducted in SRM acquisition, in both positive and

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negative polarity mode. Two transitions were monitored for each compound at unit resolution and

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using a collision gas pressure of 1 mTorr. Ionization and fragmentation conditions were optimized

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by injecting in the ESI source the individual standard working solutions by direct infusion at a flow

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of 5 µL min-1.

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Briefly, the s-lens parameter was optimized in full scan monitoring the intensity of the precursor ion,

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in order to maximize the precursor ion sensitivity. Then the five most abundant product ions formed

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from the precursor ion were selected and the collision energy optimized per product ion in order to

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maximize the product ion sensitivity. The first most abundant and the second most abundant or

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selective product ion were selected for monitoring the analytes. Moreover, each analyte transitions

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were monitored over 1 minute window time around the analyte retention time, to enhance sensitivity

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and selectivity of the measurements. Cycle time was set to 0.35 s. The optimized SRM parameters

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are reported in Table 2.

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The QqQ-MS was calibrated before ionization and fragmentation parameters optimization and,

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later, once a month by using the vendor calibration mixture solution injecting it in infusion at 5 μL

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min-1. The sweep cone and ion transfer tube were washed every day prior to analysis. LC-MS data

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acquisition, peak integration and quantitation were performed by using the software Xcalibur v.2.1

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(Thermo Fisher Scientific, Brema, Germany). 9 ACS Paragon Plus Environment


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Data acquisition and processing

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LC-MS and LC-HRMS data acquisition, peak integration and quantitation were performed by using

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the software Xcalibur v.2.1 (Thermo Fisher Scientific, Brema, Germany). In particular, peak

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integration for quantification was carried out by means of Xcalibur Quan Browser using the following

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parameters: retention time window 10 s, smoothing points 1, baseline windows 40 (QqQ-MS) and 60

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(Q-orbitrap-MS), area noise factor 5 (QqQ-MS) and 2 (Q-orbitrap-MS), peak noise factor 10,

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minimum peak height S/N 3. One processing method was set for HRMS data where the extracted ion

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chromatograms (XIC) of theoretical m/z ± 5 ppm were used for peak integration. Two processing

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methods were set for QqQ-MS, one based on the highest abundant and one on the second highest

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abundant transition for peak integration.

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Quantitative QqQ-MS and Q-orbitrap-MS performance

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Identification and quantitation of analytes

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Polyphenols under investigation were identified in the spiked matrices by following the Commission

202

Directive 2002/657/EC19. Based on these regulations a minimum number of 4 identification points is

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required for the unequivocal identification of analytes. For LRMS 1 point is assigned for the detection

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of the precursor mass, 1.5 points to the detection of the first transition, 1.5 points to the detection of

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the second transition. For HRMS 2 points are assigned for the detection of the precursor mass, 2.5

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points for the detection of one ion fragment. Therefore, for LRMS, compounds were identified based

207

on the presence of the highest abundant transition and the second highest abundant transition, in

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comparison with the authentic reference standard. For HRMS, compounds were identified based on

209

the detection of both the precursor ion and the most abundant fragment ion with a mass tolerance of

210

5 ppm, in comparison with the authentic reference standard. Other than identifying the analytes based

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on the precursor and fragment ions also the retention time has been taken into consideration.

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Compounds were assessed as unequivocally identified when the FWHM was between 90-110% the

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FWHM of the reference standard compound chromatographic peak. Moreover, also the shift in 10 ACS Paragon Plus Environment

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retention time of the analyte and the reference standard compound was allowed to be not higher than

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5%. The quantification by means of QqQ-MS was accomplished by integrating the peak related to

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the highest intense transition (quantifier), whereas the quantification by Q-orbitrap-MS was

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accomplished by integrating the peak obtained by the extraction of the precursor accurate mass with

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an extraction window of 5 ppm.

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Calibration graphs and linearity range

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A matrix matched (M) calibration curve and a standard in solvent curve (S) were built at 9

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concentration levels. Peak area ratios (compound/internal standards) were considered as response (y)

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and polyphenols concentrations as independent variable (x). A linear regression model with weighting

223

factor 1/x was used to build the calibration curves. Intercept q, slope m, and correlation coefficient

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R2 were calculated. The dynamic linear range was evaluated for each analyte based on the R2 values.

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The mM, qM, RM2, mS, qS and RS2 values related to both matrix matched and standard in solvent

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calibration curves are reported for each analyte in Table S2.

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LOD, LOQ and sensitivity

228

For QqQ-MS, LOD and LOQ calculation was accomplished by integrating the area of the second

229

highest abundant transition and the highest abundant transition of each analyte, respectively. For Q-

230

orbitrap-MS, both LOD and LOQ calculation was accomplished by integrating the peak of the

231

precursor ion of each analyte, obtained with an extraction window of 5 ppm, and showing the presence

232

of the most abundant fragment with a mass tolerance of 5 ppm. The matrix matched and matrix free

233

calibration curves were built as explained before, but considering the peak area without IS

234

normalization as response (Y). LOD and LOQ values were calculated based on the following

235

expressions:

236

LODM= 3 𝛅qM/mM

LOQM = 10 𝛅qM/mM

237

LODS= 3 𝛅qS/mS

LOQS = 10 𝛅qS/mS



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Where 𝛅qM is the standard deviation on the intercept qM and mM is the slope of the matrix matched

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calibration curve, and qS is the standard deviation on the intercept qS and mS is the slope of the

240

standard in solvent calibration curve. LOQ values were used for the comparison of Q-orbitrap-MS

241

and QqQ-MS sensitivity. LODM, LOQM, LODS, LOQS values related to both matrix matched and

242

standard in solvent calibration curves are reported for each analyte in Table S3.

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Matrix effect

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Due to the absence of sample pretreatment recovery values could not be calculated, whereas ME was

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calculated by the following formula: ME = mM/mS * 100

246 247

Where mM and mS are the slopes of the matrix matched and standard in solvent curve, respectively,

248

built as described in paragraph 2.3.2, i.e by the areas normalized for the IS. ME values are reported

249

in Table S4.

250

Precision

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The intra-day and inter-day method precision was calculated at two concentration level, i.e level 4

252

and level 8 of the matrix matched calibration curve. In particular, 5 replicates were run in the same

253

day for evaluating intra-day precision and 5 replicates were run in 5 different days for evaluating

254

inter-day precision. Mean and relative standard deviation (RSD) values, calculated on the area of the

255

integrated peaks of the replicates, were used to assess method precision, that is reported for each

256

analyte in Table S5.

257 258

Results and discussion

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Experimental setup for the comparison


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Nowadays, several quantitative LC-HRMS methods have been already developed and validated for

261

all kinds of analytes and matrices8,20–23. This strongly suggests that HRMS is a fully appropriate

262

technique for absolute quantification. However, a head-to-head comparison between recent HRMS

263

and QqQ-MS platforms is still required in order to assess the performances of the new instruments

264

when compared with the historical work-horse for quantification. Such comparison should be taken

265

with care since many parameters can influence the sensitivity, selectivity and reproducibility of

266

results: the chromatographic method, the position or design of the ionization sources, the MS

267

parameters and acquisition modes as well as the matrix of choice. All the mentioned parameters have

268

been taken in to account in this work, in which an evaluation and comparison of the performance of

269

a QqQ-MS and a hybrid Q-orbitrap-MS for the quantitative analysis of polyphenols in rosé wine was

270

carried out.

271

The chromatographic method was chosen based on a previous comprehensive evaluation carried out

272

by La Barbera et al.24 on the analysis of polyphenols in strawberry. The Kinetex C18 XB column was

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evaluated under several gradient, mobile phases and temperature in order to assure the best

274

chromatographic separation of polyphenol standard compounds and polyphenols in a complex

275

phytochemical mixture. These conditions were kept unchanged in the analysis with both the HRMS

276

platform and LRMS platform. Although the LC chromatographic pumps coupled with the two

277

instruments are slightly different, aside from a constant shift of about 1 minute in the retention time

278

the separation of the 50 standards evaluated in this work gave the same results in terms of peak shape,

279

reproducibility and peak resolution.

280

Concerning the ionization source parameters, also the conditions optimized in the previous work of

281

La Barbera et al. 24 were used for the analysis. These parameters were indeed previously optimized

282

by direct injection combining a flow of 10 µl min-1 of the standard solution at a C=10 ng µl-1 with a

283

flow of 600 µl min-1 of the mobile phases in the initial gradient conditions by means of a tee union.

284

The ionization parameters were kept constant in the HRMS and LRMS platforms which were both

285

equipped with the same exact ESI source, i.e H-ESI II. Also, the distance between the needle and the 13 ACS Paragon Plus Environment


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ion transfer tube was optimized in order to obtain the highest ionization efficiency and was kept

287

unchanged for the two MS systems.

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The same mass spectrometric acquisition mode as used in the work of La Barbera et al.24, i.e. DDA,

289

was chosen for the polyphenol quantification by the HRMS instrument. Although the method

290

presented by La Barbera et al.24 was aimed at performing a comprehensive qualitative analysis of

291

complex phytochemical matrices, it was optimized in order to obtain a minimum of 10 data points

292

per peak and to allow the simultaneous acquisition of MS and MS/MS spectra in a wide m/z range,

293

that it is a primary need for quantitative analysis as well. Several parameters such as dynamic

294

exclusion, the presence of an exclusion list, the number of ions to be fragmented i.e. TOP5 or TOP10,

295

a stepped fragmentation at two energy collisions in contrast to a single energy collision, were

296

evaluated. Also, resolution was set at 35,000 (FWHM at m/z 200) for the MS scan and at 17,500 for

297

the MS2 scan, resulting in a mass error not higher than 3 ppm. Higher resolution for the MS scan did

298

not significantly reduce the mass error, but it decreased the scan rate, resulting in a minor number of

299

data point per chromatographic peak. All the parameters set in the work of La Barbera et al.24 were

300

kept unchanged in this work. However, due to the different purpose of this work, namely the targeted

301

screening and quantification of a limited number of compounds, a smaller mass range acquisition was

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set (100-600 m/z) and an inclusion list containing the precursor mass and retention time of the analytes

303

of interest was introduced to assure their MS/MS spectra acquisition. The choice of the DDA

304

acquisition mode, over the other available acquisition modes that could potentially be used for

305

quantitative analysis in HRMS such as full scan HR, DIA, targeted single ion monitoring (SIM) DDA

306

and parallel reaction monitoring (PRM), was due to several reasons. Firstly, methods implying the

307

acquisition of MS/MS spectra are mandatory in order to remove isobaric interferences and to assure

308

a high confidence of identification. Based on the Commission Directive 2002/657/EC 19, a minimum

309

number of 4 point is required for the unambiguous identification of a compound, which in the case

310

of HRMS platforms requires the detection of both the precursor ion and at least one fragment.

311

Although several articles underscore the quantitative capabilities of HRMS instruments whereas 14 ACS Paragon Plus Environment

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performing MS/MS acquisitions9,10,20, several papers suggests instead that PRM, DIA and other

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acquisition modes including MS/MS acquisition are good methods for quantitative analysis5,25.

314

Unfortunately, official guidelines do not specify the preferred acquisition mode for quantification by

315

HRMS platforms26, and the choice is left to the researchers’ purposes. In our work, the DDA method

316

was chosen because of its ease of use, not requiring any particular optimization prior to analysis.

317

Moreover, DDA is one of the most used acquisition mode by users, allowing both targeted

318

quantification and untargeted identification, when needed. Concerning the QqQ-MS, the scheduled

319

SRM acquisition mode was used for the analysis of polyphenols. Indeed, when the regular SRM

320

acquisition mode was operated, by monitoring two transitions for each analyte during the whole

321

chromatographic run, only one or two data points per chromatographic peak were obtained.

322

Monitoring such a high number of transitions at the same time causes very long scan time not allowing

323

to acquire a sufficient number of data points per peak, losing in sensitivity and goodness of the peak

324

shape and not allowing a reliable quantification. On the contrary, a scheduled SRM acquisition mode,

325

by setting the specific retention time of each analyte with a retention time tolerance of one minute,

326

allowed to increase the number of data points per peaks. In addition, the cycle time was varied to

327

obtain at least 10 points per peaks, exactly like in the acquisition performed with the HRMS

328

spectrometer. As mentioned above, two transitions were chosen for each analyte, the first most intense

329

(quantifier) and the second most intense (qualifier). In the case of the second transition, H2O or CO

330

neutral losses or neutral losses common to other coeluting analytes were avoided when possible, in

331

order to assure the selectivity of the method.

332

In Table 1, the analytes of interests monitored by Q-orbitrap-MS have been reported together with

333

their retention time, precursor ion mass and fragment ion mass. In Table 2 all the mass spectrometric

334

conditions optimized for each compound by QqQ-MS have been reported, i.e retention time,

335

quantifier and qualifier transitions, S-lens and collision energies.

336

Aside from the choice of the proper chromatographic separation and mass spectrometric acquisition

337

modes, also the choice of the matrix was accomplished by taking into consideration several concerns 15 ACS Paragon Plus Environment


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338

and based on both data literature and practical considerations. Wine is known to be a rich source of

339

polyphenols, making their identification and quantitation an appealing topic, as stated by the high

340

number of publication found in the literature27–30. Moreover, its analysis does not require any sample

341

preparation or analytes extraction, minimizing the effect of sample handling errors on the evaluation

342

of the performances of the instruments under investigation. However, the high quantity of

343

polyphenols present in this matrix makes impossible the use of blank samples for developing a

344

standard quantification method. Therefore, standard addition method was chosen because considered

345

the most suitable for the instrumental evaluation. Setting the standard addition method for the

346

comparison of the two instruments was a particular taught issue, due to the high abundance of

347

endogenous polyphenols in the matrix. When using standard addition method, indeed, poor results

348

will be obtained unless the concentration of the spiked analyte is more than five times the analyte

349

concentration (so long as that is consistent with the linear range of the analytical method)31. In order

350

to respect this constrain, several types of red wines were evaluated to assess their phenolic

351

composition. The content of polyphenols in red wine was shown to be too high to allow a proper

352

supplementation in the matrix. The dilution of the sample, at the same time, would have represented

353

an approximation of the real system, not allowing a reliable evaluation of matrix effect. Rosé wine

354

matrix was shown to contain lower quantities of polyphenols but maintaining a quite similar

355

complexity compared to red wine. Therefore, rosé wine was chosen as the most suitable matrix for

356

this study. The polyphenols found to be present in rosé wines showed a very heterogeneous range of

357

signal intensities, requiring the addition of each analyte at a specific concentration. Therefore, the

358

concentrations reported for the stock mixture solution were chosen based on both the initial content

359

of each analyte in the matrix and their signal response. Although these concerns have been taken into

360

account, in some cases, the standard addition was not sufficient to allow a signal 5 times higher than

361

the endogenous sample, at the lowest spiking levels. In these cases, it was not possible to build the

362

matrix matched calibration curve, and the instruments comparison was carried out based on the

363

standard solvent curve. 16 ACS Paragon Plus Environment

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364 365

Method validation for the comparison

366

Quantitative performance of the Q-orbitrap-MS and QqQ-MS was compared by the injection of the

367

exact same spiked samples in the two MS instruments and evaluating methods sensitivity, precision,

368

linear dynamic range, and matrix effect. In order to make such an evaluation, the solvent standard

369

calibration curves and the matrix matched calibration curves were built over four orders of magnitude.

370

The values of the slope, the intercept and the squared correlation coefficient R2 are reported in Table

371

S2. The first information obtained from the calibration curves is that both instruments show a pretty

372

good linear range over the explored order of magnitudes. In particular, coefficient RS2 obtained by

373

the solvent standard calibration curve in positive polarity was in the range 0.967-0.9999 and 0.9754-

374

0.9996 for Q-orbitrap-MS and QqQ-MS, respectively. Coefficient RS2 obtained by the solvent

375

standard calibration curve in negative polarity was in the range 0.970-0.9996 and 0.976-0.9996 for

376

Q-orbitrap-MS and QqQ-MS, respectively. Coefficient RM2 obtained by the matrix matched

377

calibration curve in positive polarity was in the range 0.97-0.9991 and 0.987-0.997 for Q-orbitrap-

378

MS and QqQ-MS, respectively. Finally, coefficient RM2 obtained by the matrix matched calibration

379

curve in negative polarity was in the range 0.979-0.9982 and 0.98-0.9996 for Q-orbitrap-MS and

380

QqQ-MS, respectively. The high abundance of endogenous polyphenols in rosé wine together with

381

the low response of some analytes limited the possibility of exploring higher concentration, due to

382

the need of spiking a too high quantity of reference standard compounds. The explored range of

383

linearity was however higher when compared with previous papers on quantitation in wine28,30. From

384

the comparison, any significant difference was found between the two instruments. Only a slight

385

better performance in favour of QqQ-MS was detected, mostly in negative polarity, for few

386

compounds, showing higher R2 in QqQ-MS than in Q-orbitrap-MS. This finding is confirmed by

387

several previous works on QqQ-MS and HRMS platforms comparison, showing very similar dynamic



Page 18 of 35

388

range and linearity when using the newest HRMS spectrometers 9,21 on the contrary of the “old” Q-

389

TOF MS32.

390

Whereas the linearity did not show any significant differences between the QqQ-MS and Q-orbitrap-

391

MS, the outcome of instrument comparison was sensibly different in the case of sensitivity.

392

Sensitivity was evaluated based on the calculated LOQ values. Indeed, whereas for HRMS extracted

393

accurate mass is used for peak integration and calculation of both LOQ and LOD, for QqQ-MS the

394

highest abundance transition is used for peak integration and LOQ calculation, and the second highest

395

abundant for peak integration and LOD calculation. For this reason, LOD values were not considered

396

to be comparable between the two instruments and LOQ was the only parameters used for the

397

comparison of Q-orbitrap-MS and QqQ-MS sensitivity. LOQS calculated from the solvent standard

398

calibration curve in positive polarity was in the range 2.2-162 µg L-1 and 4.2-645 µg L-1 for Q-

399

orbitrap-MS and QqQ-MS, respectively. LOQS calculated from the solvent standard calibration curve

400

in negative polarity was in the range 0.42-804 µg L-1 and 0.109-150 µg L-1 for Q-orbitrap-MS and

401

QqQ-MS, respectively. LOQM calculated from the matrix matched calibration curve in positive

402

polarity was in the range 1.0-2718 µg L-1 and 6-126 µg L-1 for Q-orbitrap-MS and QqQ-MS,

403

respectively. LOQM calculated from the matrix matched calibration curve in negative polarity was in

404

the range 4.4-4355 µg L-1 and 2.8-12087 µg L-1 for Q-orbitrap-MS and QqQ-MS, respectively.

405

However, although some of the minimum and maximum values of these ranges result to be higher

406

for Q-orbitrap-MS than of QqQ there is to take into account that they are not referred to the same

407

compounds. Most of the compounds with a high value of LOQ for Q-orbitrap-MS were not detected

408

at all by QqQ-MS. The values of LOQS and LOQM both in positive and negative polarity and for

409

both instruments are reported in Table S3. As shown in Table S3, several LOQM values are not

410

reported either because the standard addition was not sufficient to allow the building of the calibration

411

curve, or because, in the case of endogenous compounds, LOQM could not be assessed. In these cases,

412

the comparison of the instruments was accomplished by evaluating the LOQS. As shown in Table S3,

413

almost the totality of the analytes showed lower levels of LOQM and LOQS in the HRMS instrument 18 ACS Paragon Plus Environment

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414

demonstrating its higher performance in terms of sensitivity. This is in contrast with recent works on

415

comparison between the QqQ-MS and HRMS instruments, showing comparable limits of quantitation

416

in the two platforms. In particular, several papers showed that LRMS instruments were more

417

sensitive, equally sensitive, or less sensitive than HRMS instruments depending on the analysed

418

compounds5,10. The lack of an evaluation of QqQ-MS in comparison with HRMS platforms for the

419

analysis of polyphenols could justify the new finding that, in contrast to other similar papers, shows

420

the extremely better performance of HRMS instrumentation. In other works on polyphenol

421

quantitation with either QqQ-MS or HRMS platforms lower, similar or higher values of LOQ were

422

reported. However, these results are not often comparable because of the different analysed matrices

423

and the different approaches used for the calculation of LOQ values. The reported works on

424

polyphenols quantitation in wine, instead, and one specific work in which a multi-residual method

425

was applied for polyphenols quantitation in rosé wine by means of QqQ-MS, reported very similar

426

values of LOQ when compared with our results29,30,33–35.

427

A particularly interesting observation on calculated LOQ values, when the calculation was possible

428

for both the standard in solvent and matrix matched calibration curve, is the higher response of some

429

analytes when spiked in the matrix compared to the solvent, in the QqQ-MS analysis. This

430

observation was confirmed by the calculation of ME, resulting in values higher than 100% for all the

431

analytes in the QqQ-MS and few analytes in Q-orbitrap-MS. In particular, as shown in Table S4,

432

whereas in negative ionization mode Q-orbitrap-MS showed ME ranging from 80% to 100%, in

433

positive ionization mode it showed ME ranging from 100% to 120%. On the contrary, in both positive

434

and negative ionization mode, QqQ-MS resulted in ME much higher than 100% in the range of 120-

435

150%. The phenomenon of ME in HPLC-MS is due to several causes, among which the ionization

436

phenomenon is the most responsible of signal suppression or enhancement. The ionization efficiency,

437

indeed, depends on the physico-chemical properties of the analyte and the other molecules present at

438

the ionization interface. In ESI, the eluent from the chromatographic column is nebulized into charged

439

droplets, where a competition starts between the analyte and co-analyte for the proton transfer to take 19 ACS Paragon Plus Environment


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440

place36,37. Usually, coeluting polar co-analytes and apolar co-analytes should result in analyte signal

441

enhancement and suppression, respectively. Whereas the presence of polar compounds in an

442

acqueous matrix, such as wine, could be the explanation for the signal enhancement revealed for some

443

of the compounds with both QqQ-MS and Q-orbitrap-MS, it does not explain the different percentage

444

of signal enhancement between the two instruments. Considering that the same matrix, the same

445

chromatographic condition and the same ESI source were used for instrumental comparison, this

446

evidence should be explained by a post-interface phenomenon. As demonstrated by Kaufmann et

447

al.38, whereas the ion optics and the first quadrupole will always affect the signal the same way for

448

both techniques used, another source of signal changes can be found in the C-trap present in the Q-

449

orbitrap-MS instrument. Filling the C-trap is limited with both ion population and time, and in the

450

presence of a rich matrix, minor ions can be loss, which can explain the different ME values for Q-

451

orbitrap-MS compared to QqQ-MS. Aside from these observations, considering that acceptable ME

452

values are within 80% and 120%, the comparison of the two instruments resulted in better

453

performances for Q-orbitrap-MS compared to QqQ-MS.

454

The last parameter evaluated for QqQ-MS and Q-orbitrap-MS comparison is the precision, expressed

455

as RSD% calculated on the area of the integrated peaks of the replicates, that is reported for both

456

instruments, in positive and negative polarity mode, and at two concentration levels in Table S5.

457

Intra-day precision and inter-day precision showed RSD values below 5% and 10% for most of the

458

analytes with Q-orbitrap-MS, respectively. On the contrary, several analytes showed values higher

459

than 10% for both intra-day precision and inter-day precision with QqQ-MS. Therefore, on the

460

contrary of previous works showing similar precision in LRMS and HRMS, in this work better

461

performance in terms of precision was shown for the HRMS platform.8,20–22

462

In order to summarize and better visualize the results of comparison, an arbitrary score system was

463

applied on the parameters used for the evaluation. In particular, for range linearity comparison, score

464

2 was assigned when R2 was higher than 0.99, score 1 when R2 was higher than 0.98, score 0 when

465

R2 was lower than 0.98. For LOQ values comparison, score 1 and 0 were assigned to the technique 20 ACS Paragon Plus Environment

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466

showing the lowest and highest LOQ value, respectively. For ME evaluation, score 3 was assigned if

467

the deviation from 100% was lower than 10%, score 2 if the deviation was lower than 20%, score 0

468

if it was higher than 50. Finally, for intra-day precision comparison, score 2 was assigned when

469

RSD% was lower than 5%, score 1 when lower than 10%, and score 0 when higher than 10%, whereas

470

for inter-day precision, score 2 was assigned when RSD% was lower than 10%, score 1 when lower

471

than 20%, and score 0 when higher than 20%. In some cases, due to the lower response of some

472

analytes in QqQ-MS compared to Q-orbitrap-MS, either the endogenous polyphenols could only be

473

detected by Q-orbitrap-MS or the calibration curve could not be built for QqQ-MS. In these cases,

474

because of the impossibility of calculating LOQ, ME, R2 or RSD % values for either one or the other

475

instrument, the score was not assigned to neither of the two instruments, to avoid that sensitivity could

476

affect also the evaluation of the other parameters. The sum of the scores assigned to RS and RM, LOQS

477

and LOQM, ME, intra-day precision and inter-day precision, both in positive and negative ionization

478

mode for each individual analyte, was considered for instrumental comparison. Figure 1 reports the

479

calculated scores for the investigated analytes. As shown in the Figure the total score resulted to be

480

much higher in HRMS instrument with a high contribution of sensitivity, ME and precision. Only

481

linearity was slightly in favor of QqQ-MS as already mentioned above. Furthermore, the analytes

482

reported in Figure 1 were grouped based on the polyphenol class they belong to, in order to detect a

483

specific class dependent trend. Unluckily, no particular trend was observed.

484

The better performance showed for Q-orbitrap-MS in comparison with QqQ-MS can be due to several

485

reasons. Firstly, there is to take into consideration that the showed results are mainly dependent on

486

the acquisition mode chosen for the Q-orbitrap MS. Indeed, other acquisition modes such as full scan

487

HR, DIA, SIM, PRM could have led to different results in terms of either sensitivity or selectivity.

488

The choice of the DDA acquisition mode over the others was due to: the need for allowing analyte

489

identity confirmation by MS/MS spectra; the higher applicability and easy of use of this method in

490

comparison with other methods were prior parameters optimization is needed; the possibility of this

491

method over targeted methods of allowing a retrospective identification of unexpected compounds in 21 ACS Paragon Plus Environment


Page 22 of 35

492

addition to the quantitation of targeted analytes. This choice strongly affected the results of the LRMS

493

and HRMS platforms comparison, leading to the conclusion that that Q-orbitrap-MS operated in DDA

494

mode, shows better performances compared to QqQ-MS operated in SRM mode.

495

In QqQ-MS an SRM acquisition mode by monitoring two transitions was used, because monitoring

496

only one transition might result in false positive identifications for individual compounds and thus at

497

least two transitions are required. However, several limitations become evident: under the constraints

498

of at least two transitions for the identification, SRM methods are typically limited to about 100–150

499

target analytes depending on chromatographic separation, as otherwise accuracy or sensitivity

500

deteriorate due to an insufficient temporal peak resolution or too short acquisition times for the

501

individual MS/MS transitions, respectively. When the number of target analytes is limited the

502

problem can be overcome. In this case, indeed, QqQ analyzer in its SRM mode results the most

503

adequate one for quantitative methods, due to its robustness and allowing a reliable quantification of

504

known compounds.

505

HRMS offers promising solutions to the limitation of analysing high number of analytes. All

506

compounds present in a sample can be determined simultaneously with HRMS instruments operating

507

in full-scan mode and fragmented by means of acquisition modes such as DDA, making no

508

preselection of compounds and associated SRM transitions necessary. In theory, the presence of an

509

unlimited number of compounds can be investigated at proper sensitivity, without requiring the

510

preselection of analytes. However, on qTOF instruments, these capabilities are impaired by the

511

limited sensitivity, which is about 1–2 orders of magnitude lower than those of QqQ-MS instruments

512

in SRM mode, and the limited dynamic range which is about 10-fold below that of QqQ-MS. Thus,

513

qTOF instruments have been used only occasionally for quantification and one established strategy

514

is to use QqQ-MS for quantification and a separate qTOF analytical run for confirmation. The

515

Orbitrap instrument offers a better dynamic range and a sensitivity close to that of many QqQ-MS

516

instruments, thus allowing for quantification and confirmation in a single analytical run39. Several

517

publications describing the new generation of HRMS systems and highlighting differences between 22 ACS Paragon Plus Environment

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40

518

LRMS and HRMS capabilities for targeted quantification

are available, and now show equal or

519

better performances of HRMS platforms compared to QqQ-MS.

520

HRMS allows a robust targeted quantification, but also compound identification, metabolite

521

phenotyping, and retrospective data treatment, which can be a true advantage for research on

522

unexpected unknown compounds. Due to the high-quality information, by combining sensitive full-

523

spectrum data and high mass accuracy, HRMS is a promising technique that has opened new horizons

524

in both identification and quantification of a wide range of known and unknown compounds.

525

In conclusion, in this work, the comparison between a QqQ-MS and a Q-orbitrap-MS for quantitation

526

of polyphenols resulted in better performance of HRMS over LRMS platform in terms of sensitivity,

527

precision and ME. The obtained results confirm the most recent findings suggesting the suitability of

528

HRMS for quantitation purposes. However, in the last published papers, comparable results between

529

low resolution and high resolution mass spectrometers have been reported for quantitation purposes.

530

In this work, HRMS platform showed a definitely better quantitative performance compared to the

531

QqQ-MS, for almost the totality of the investigated analytes. Sensitivity, robustness, user friendliness

532

and versatility makes HRMS platform a key instrument for performing both qualitative and

533

quantitative analysis. The quantitative performance shown with the tested HRMS instrument in this

534

article and many other reports, together with the growing needs for a global metabolite phenotyping,

535

leads to the consideration of HRMS as the new technique of choice for targeted and non targeted, as

536

well as qualitative and quantitative analysis, in several research fields.

537

Abbreviations used

538

Automatic gain control (AGC); Data dependent acquisition (DDA); Data independent acquisition

539

(DIA); Electrospray ionization (ESI); Extracted ion chromatogram (XIC); Full width at half

540

maximum (FWHM); High resolution mass spectrometry (HRMS); Higher energy collisional

541

dissociation (HCD); Limit of detection (LOD); Limit of Quantification (LOQ); Low resolution mass

542

spectrometry (LRMS); Mass spectrometry (MS); Matrix effect (ME); Matrix matched (M); 23 ACS Paragon Plus Environment


Page 24 of 35

543

Normalized collision energy (NCE); Parallel reaction monitoring (PRM); Quadrupole orbitrap mass

544

spectrometer (Q-orbitrap-MS); Quadrupole time-of-flight mass spectrometer (Q-TOF-MS); Relative

545

standard deviation (RSD); Selected reaction monitoring (SRM); Single ion monitoring (SIM);

546

Standard in solvent (S); Tandem mass spectrometry (MS/MS); Triple quadrupole mass spectrometry

547

(QqQ-MS); Ultra-high performance liquid chromatography (UHPLC)

548

Conflict of interest

549

The authors declare no competing financial interest.

550

Supporting information description

551

Table S1 shows the concentration of the investigated analytes at several dilution levels for both the

552

mixture stock solution and the mixture working solution. Table S2 shows the values of the slope,

553

intercept and squared correlation coefficient R2 extrapolated from both the matrix matched and

554

standard in solvent calibration curves, in positive and negative polarity mode, for both instruments.

555

Table S3 shows the values of LODM, LOQM, LODS, LOQS calculated from both matrix matched and

556

standard in solvent calibration curve, in both positive and negative polarity mode, for both

557

instruments. Table S4 shows the matrix effect values calculated for both instruments and in both

558

polarity modes. Table S5 shows the intra-day and inter-day precision expressed as relative standard

559

deviation (RSD), calculated on the area of the integrated peaks of five replicates, for both instruments,

560

in positive and negative polarity mode, at two concentration levels.

561


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(30) (31) (32) (33) (34) (35) (36) (37) (38) (39) (40)


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693

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Figure Captions:

694 695

Figure 1: Scores assigned to the investigated analytes based on the following rules : for range linearity

696

comparison, score 2 when R2 > 0.99, score 1 when R2 > 0.98, score 0 when R2 < 0.98; for LOQ

697

values comparison, score 1 and 0 assigned to the technique showing the lowest and highest LOQ

698

value, respectively; for ME comparison, score 3 if the deviation from 100% was < 10%, score 2 if

699

the deviation < 20%, score 0 if the deviation > 50; for intra-day precision comparison, score 2 when

700

RSD% < 5%, score 1 when RSD% < 10%, and score 0 when RSD% > 10%; for inter-day precision,

701

score 2 when RSD% < 10%, score 1 when RSD% < 20%, score 0 when RSD% > 20%.


Page 29 of 35


Table 1: The Compounds Analysed by Q-orbitrap-MS are Reported Together with Their Retention Time, Molecular Formula, Precursor Ion Mass and Fragment Ion Mass, both in Positive and Negative Polarity. Compounds

Tr

Molecular formula

Precursor ion [M+H]+

Product ion [M+H]+

Precursor ion [M-H]-

Product ion [M-H]-

apigenin

19.9

C15H10O5

271.0601

153.0181

269.0455

117.0336

apigenin glucoside

14.2

C21H20O10

433.1129

153.0183

431.0984

211.0397

biochanin A

26.8

C16H12O5

285.0758

213.0546

283.0612

211.0396

caffeic acid

3.5

C9H8O4

181.0496

89.0390

179.0350

135.0444

callistephin

6.9

C21H20O10

433.1129

121.0286

-

-

catechin

3.1

C15H14O6

291.0863

123.0442

289.0718

109.0285

catechin gallate

11.4

C22H18O10

443.0973

123.0443

441.0827

125.0235

chlorogenic acid

3.7

C16H18O9

355.1024

89.0390

353.0878

85.0284

cinnamic acid

14.2

C9H8O2

149.0597

103.0547

147.0451

90.9703

coumaric acid

7.8

C9H8O2

165.0546

91.0548

163.0401

119.0493

coumestrol

20.0

C15H8O5

269.0445

157.0647

267.0299

266.0225

cyanidin

6.9

C15H10O6

287.0550

121.0287

285.0405

116.7973

daidzein

15.7

C15H10O4

255.0652

91.0546

253.0506

91.0179

diosmetin

21.0

C16H12O6

301.0707

56.9654

299.0561

174.9558

epicatechin

6.2

C15H14O6

291.0863

123.0444

289.0718

109.0285

epicatechin gallate

10.5

C22H18O10

443.0973

123.0443

441.0827

125.0235

equol

18.9

C15H14O3

243.1016

105.0702

241.0870

68.9946

eriodictyol

15.7

C15H12O6

289.0707

153.0181

287.0561

135.0443

ferulic acid

7.9

C10H10O4

195.0652

89.0391

193.0506

133.0287

flavone

26.1

C15H10O2

223.0754

95.0495

-

-

formononetin

22.4

C16H12O4

269.0808

197.0595

267.0663

195.0447

gallic acid

0.7

C7H6O5

-

-

169.0142

125.0235

genistein

19.1

C15H10O5

271.0601

91.0546

269.0455

133.0287

genistin

11.8

C21H20O10

433.1129

91.0547

431.0984

211.0397

glycitein

16.9

C16H12O5

285.0758

242.0562

283.0612

211.0396

hesperetin

20.3

C16H14O6

303.0863

153.0181

301.0718

164.0110

hesperidin

14.9

C28H34O15

-

-

609.1825

301.0718

isorhamnetin

21.0

C16H12O7

317.0656

153.0181

315.0510

63.0229

kaempferol

20.1

C15H10O6

287.0550

153.0181

285.0405

93.0335

kuromanin

5.7

C21H20O11

449.1078

137.0236

447.0933

211.0396

luteolin

17.5

C15H10O6

287.0550

153.0181

285.0405

133.0287

luteolin glucoside

12.3

C21H20O11

449.1078

153.0184

447.0933

133.0287



Page 30 of 35

malvidin

12.4

C17H15O7

331.0818

121.0288

330.0745

114.9507

malvidin galactoside morin

7.9

C23H25O12

493.1346

315.0498

492.1273

214.0269

15.4

C15H10O7

303.0499

121.0288

301.0354

125.0235

myricetin

13.7

C15H10O8

319.0448

153.0183

317.0303

109.0285

naringenin

18.6

C15H12O5

273.0758

153.0181

271.0612

119.0493

peonidin glucoside

8.0

C22H23O11

463.1240

201.0547

462.1168

227.0347

phloretin

19.8

C15H14O5

275.0914

107.0494

273.0768

81.0334

primuletin

30.6

C15H10O3

239.0703

137.0232

-

-

procyanidin B1

2.7

C30H26O12

579.1497

135.0440

577.1352

57.0334

procyanidin B2

5.6

C30H26O12

579.1497

123.0444

577.1352

57.0334

quercetin

17.2

C15H10O7

303.0499

153.0178

301.0354

174.9558

quercitin glucoside

11.9

C21H20O12

465.1028

153.0183

463.0882

271.0253

resveratrol

13.9

C14H12O3

229.0859

107.0495

227.0714

143.0494

rutin

11.8

C27H30O16

611.1607

153.0183

609.1461

271.0253

syringetin

21.1

C17H14O8

347.0761

153.0181

345.0616

203.0346

syringic acid

4.6

C9H10O5

199.0600

125.0235

197.0455

95.0128

taxifolin

9.1

C15H12O7

305.0656

123.0444

303.0510

57.0334

trihydroxyflavone

14.0

C15H10O5

271.0601

141.0699

269.0455

117.0336


Page 31 of 35


Table 2: The Compounds Analysed by QqQ-MS are Reported Together with Their Retention Time, Precursor Ion Mass, S Lens, Quantifier and Qualifier Transition, and the Corresponding Collision Energies, both in Positive and Negative Polarity Mode. Compound

Tr

Precursor S Product ion lens Ionsa + [M+H] (V) [M+H]+

Collision Energy (V)

Precursor S Product ion lens Ions [M-H](V) [M-H]-

apigenin

21

271

140

153.1 119.2

31 33

269.1

130

117.1 151.1

22 23

apigenin glucoside

15.4

433.1

120

271 153.1

19 46

431.1

183

268 269.1

36 27

biochanin a

27.9

285

140

213.0 269.0

39 30

282.9

110

267.9 239.0

23 35

caffeic acid

4

181

55

163.1 89.3

6 32

179

63

135 135

17 27

callistephin

8.2

433.1

131

271.1 121.5

29 53

431.1

155

269 267.9

20 27

catechin

3.6

291.1

100

250.1 139.1

7 14

289

117

245.1 203.1

16 17

catechin gallate

12.7

443.1

110

123.1 139.1

20 24

441.1

116

289 169

19 22

chlorogenic acid

4.7

355.1

150

267 73.3

22 28

353.1

80

191.1 85.1

15 38

cinnamic acid

15.4

149

90

65.4 93.3

25 19

147.1

61

103.2 77.2

12 24

coumaric acid

9

165

63

147.2 91.3

5 22

163

54

118.6 90.5

16 27

coumestrol

21

269

160

241.1 213.1

22 28

267

116

266 211

29 30

cyanidin

8.1

287

115

230.9 148.8

20 22

-

-

-

-

daidzein

16.9

255

170

199.1 137.1

24 27

253

130

208.1 224.1

31 28

diosmetin

22.2

301

110

286 258

27 37

299.1

120

284.1 227.1

21 37

epicatechin

7.2

291

110

139.1 123.2

18 23

289

115

245.1 203.1

15 19

epicatechin gallate

11.7

443.1

150

402 385.8

20 42

441

130

169.1 289.1

24 23

equol

20.0

242.9

90

165.0 135.0

12 22

241

77

121.0 119.1

16 22

eriodictyol

16.9

289.1

120

153.1 163.1

25 19

287.1

90

151.1 135.2

13 27


Collision Energy (V)


Page 32 of 35

ferulic acid

9.2

195

67

177.1 145.1

10 17

193.1

73

134.1 178.2

15 14

flavone

27.2

223.1

115

77.3 121.2

34 25

-

-

-

-

269

139

197.1 253.1

40 30

267

110

251.9 223

22 33

formononeti 23.6 n gallic acid

0.7

-

-

-

-

169

60

125.2 124

17 14

genistein

20.3

271

120

153.1 91.3

27 36

269

107

133 132

34 45

genistin

13.1

433.1

110

271 153

20 48

431.1

152

268 266.8

29 45

glycitein

18.1

285

165

270.1 242.1

25 33

283

89

268 240

19 27

hesperetin

21.4

303.1

115

177.2 153.1

19 22

301.1

120

286.1 136.1

19 31

hesperidin

16.1

-

-

-

-

609.1

210

301.1 286

32 50

isorhamneti n

22.1

317

160

302 152.9

26 35

315

182

300 151.1

23 31

kaempferol

21.2

287

135

153.1 121.1

31 32

285.1

110

185.1 151.1

27 20

kuromanin

6.95

449.1

123

287 213.1

22 56

447

171

285.1 284.1

21 27

luteolin

18.7

287

150

153.1 135.2

32 33

285

125

133 132

37 52

luteolin glucoside

13.6

449.1

115

287 153

19 52

447.1

182

285 283.8

28 40

malvidin

14.8

331

200

315 287

31 32

-

-

-

-

malvidin galactoside

9.4

493.1

140

331 315

22 48

491.1

195

328.1 329.2

27 22

morin

16.5

303

145

153.1 229

28 25

301

117

151.1 125.1

21 22

myricetin

15

319

150

153 217.1

34 34

317.1

120

151.1 179

25 20

narigenin

19.8

273

115

153.1 147.1

26 22

271

110

119.2 151

25 19

peonidin glucoside

9.3

463.1

126

301 389.2

22 16

461.1

128

299 297.9

19 26

phloretin

21

275

95

107.2 169.1

19 14

273.1

97

167.1 123.2

17 24

primuletin

31.6

239

120

137.1 139.1

29 43

-

-

-

-


Page 33 of 35


procyanidin B1

3.6

579.1

140

409.1 427

19 14

577.1

160

289.2 407.1

34 25

procyanidin B2

6.8

579.1

145

427 290.9

15 14

577.1

154

407.1 425.2

25 16

quercetin

18.4

303

150

229 153.1

29 32

301

125

151 179

22 20

quercetin glucoside

13.2

465.1

110

303 153.1

15 49

463.1

140

300 301.1

29 23

resveratrol

15.1

229.1

100 `

107.2 135.2

21 14

227.1

100

185.1 143.2

19 26

rutin

13.2

611.1

115

303 465

21 11

609.1

190

300 271

40 60

syringetin

22.3

347.1

130

287 153.1

22 30

345

150

315 330.1

27 21

syringic acid

5.4

199

68

140.1 125.1

16 27

197

72

182.1 123.2

16 25

taxifolin

10.3

305

110

259 153.1

13 15

303.2

105

285.1 125

14 22

trihydroxyfl 15.3 avone

271

150

141.2 169.1

37 33

269.1

145

240.1 195.1

28 29

a) The product ion in bold is the highest product ion, chosen as the quantifier transition.



Figure 1


Page 34 of 35

Page 35 of 35


Graphic for table of contents


A triple quadrupole and a hybrid quadrupole orbitrap mass

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