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Rapid quantitation of 12 volatile aldehyde compounds in wine by LCQQQ-MS: A combined measure of free and hydrogen-sulfite bound forms Xinyi Zhang, Nikolaos Kontoudakis, and Andrew C. Clark J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b07021 • Publication Date (Web): 27 Feb 2019 Downloaded from http://pubs.acs.org on March 10, 2019

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Journal of Agricultural and Food Chemistry

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Rapid quantitation of 12 volatile aldehyde compounds in wine by LC-

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QQQ-MS: A combined measure of free and hydrogen-sulfite bound

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forms

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Xinyi Zhang*†ǂ, Nikolaos Kontoudakis†ǂ, Andrew C. Clark†ǂ

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†

National Wine and Grape Industry Centre, Wagga Wagga, NSW 2678, Australia

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ǂ

School of Agricultural and Wine Sciences, Charles Sturt University, Locked Bag 588, Wagga Wagga,

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NSW 2678, Australia

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* Corresponding author. Tel.: +612-6933-4082

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E-mail address: [email protected]

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Other author email addresses: [email protected] (X. Zhang), [email protected] (N.

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Kontoudakis), [email protected] (A.C. Clark).

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1 ACS Paragon Plus Environment


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Abstract

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This work outlines a rapid novel methodology for the direct quantitation of 12 volatile aldehyde

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compounds related to oxidative off-flavours in wine, by measuring the combined free and hydrogen

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sulfite-bound forms of each aldehyde compounds, comprising of four general aldehydes, four Strecker

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aldehydes and four (E)-2-alkenals. The methodology requires minimal preparation of wine samples: the

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addition of internal standards and 6 g/L sulfur dioxide, and filtration prior to quantitation by liquid

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chromatography-triple quadrupole-mass spectrometer. Overall, the limit of detection, limit of

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quantification, accuracy (recovery, 82% – 119%) and precision (repeatability and reproducibility, RSD

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≤ 10%) were satisfactory to enable routine measurement of the 12 aldehyde compounds in wine. The

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methodology was applied to 20 commercial white and red wines from various varieties and vintages. A

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general trend of higher concentrations of the aldehyde compounds in white wines compared to red

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wines was observed.

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Keywords: wine, oxidation, volatile aldehyde compounds, sulfur dioxide

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Introduction

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Oxygen can greatly influence the development of wine. Adequate oxygen supply is critical for fault-

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free fermentation,1 and can influence aroma evolution,2 colour stabilization3,4 and astringency

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reduction5,6 during ageing. Excessive oxygen exposure leads to an increased risk of microbial and

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chemical spoilage, including wine oxidation.1 Oxidized wine is characterised by colour and aroma

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deterioration,7-9 such as the formation of brown pigments,10 the loss of desirable fruity attributes,11,12

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and the generation of undesirable oxidation-related odorants, such as carbonyl compounds, especial

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aldehydes.9 Depending on their molecular structure and concentration,13 aldehydes can give either

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pleasant or undesirable nuances to wine, generally described as hay, old wood,7 papery, fusel,14 farm-

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feed and cooked vegetables.15 Acetaldehyde (apple/woody),16 methional (boiled vegetable/rotten

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potato) and phenylacetaldehyde (honey)14 are important examples of aldehyde compounds with the

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potential to induce off-flavours. Apart from being generated as yeast metabolites during fermentation,17

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aldehydes can also be produced via oxidation of their corresponding alcohols, other precursors, or from

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Strecker degradation of amino acids.2,18,19

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Aldehydes compounds are reactive molecules in wine that are known to react with phenolic

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compounds20 and hydrogen sulfite (HSO3-), an equilibrium form of sulfur dioxide (SO2).21,22 After

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reacting with hydrogen sulfite, aldehyde compounds form α-hydroxyalkylsulfonates,22 which are non-

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volatile and hence the detrimental aromas of aldehydes in wine are repressed.23 However, the reaction

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between aldehydes and hydrogen sulfite is reversible. As the SO2 in wine is gradually depleted due to

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reactions with oxidative products or storage at high temperature during ageing,17,24 the bound aldehydes

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can be released progressively:23,25 the weaker binders first (e.g. benzaldehyde)1 and stronger binders

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last (e.g. acetaldehyde).26 Therefore, the concentrations of SO2 and hydrogen sulfite-bound aldehyde 3 ACS Paragon Plus Environment


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compounds can have significant impact on the evolution of wine aroma and the development of

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oxidative off-flavours in wine during ageing.27 The accurate quantification of total aldehyde

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compounds is particularly important in the understanding of potential wine aroma alterations during

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bottle ageing.

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There are several established methods to determine the concentrations of total volatile aldehyde

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compounds in wine. Most of them involve the hydrolysis of the hydrogen sulfite-aldehyde adduction

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products (i.e. α-hydroxyalkylsulfonate) under alkaline or acidic conditions,28,29 derivatisation with o-

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(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride (PFBHA)30,31 or 2,4-

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dinitrophenylhydrazine (2,4-DNPH),17,28,29,32 and quantification with mass spectrometry (MS),29,31

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diode array detector (DAD)28,32 or nuclear magnetic resonance (NMR).26 The concentrations of bound

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aldehyde compounds can also be indirectly determined via calculation using the dissociation constants

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in conjunction with the measured concentrations of free aldehydes and free surrogate aldehydes.27

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The aim of this work is to establish a novel methodology to determine the total concentrations of a

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range of aldehyde compounds in wine by enabling direct measurement of the hydrogen sulfite-bound

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forms of the corresponding aldehydes. The method includes the conversion of the free aldehyde

74

compounds into the bound form by adding SO2 during the sample preparation prior to analysis.

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Quantification was performed on LC-QQQ-MS in multiple reaction monitoring (MRM) mode. This

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method was validated in both white and red wines.

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Materials and methods

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Chemicals and wines 4 ACS Paragon Plus Environment

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All reagents were of analytical or HPLC grade unless otherwise stated. 5-methylfurfural (≥98.5%),

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benzaldehyde (≥99%), 3-(methylthio)-propionaldehyde (methional) (≥97%), furfural (≥99%), 2-

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methylpropanal (≥99%), 3-methylbutanal (≥97%), 2-phenylacetaldehyde (≥95%), (E)-2-heptenal

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(≥95%) and nonanal (≥95%) were supplied by Sigma-Aldrich (Castle Hill, NSW, Australia). (E)-2-

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hexenal (≥98%), hexanal (≥98%), (E)-2-octenal (≥95%), and (E)-2-nonenal (≥97%) were purchased

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from Alfa Aesar (Gymea, NSW, Australia). 4,5-Dimethylfurfural (≥97%), hydrocinnamaldehyde

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(≥98.5%), 3-(methylthio)butanal (≥96%), 3,5,5-trimethylhexanal (≥97%) from Sigma-Aldrich,

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benzaldehyde-d6 (≥99.9%), furfural-d4 (≥99.2%), 3-methylbutanal-d2 (≥99.5%) and hexanal-d12

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(≥98.6%) from CDN isotopes (Pointe-Claire, Quebec, Canada) were used as internal standard (IS).

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Sodium metabisulphite (Na2S2O5) was provided by Ajax Finechem (Taren Point, NSW, Australia).

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Ultrapure water (18.2 MΩ cm) utilised was generated from a Milli-Q Plus purification system (Merck

92

Millipore, Bayswater, VIC, Australia). Individual stock solutions of each aldehyde compound were

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prepared in ethanol, stored at – 80 °C and used within 3 months of preparation. Regenerated cellulose

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0.2 µm membrane syringe filters (Phenex) were supplied by Phenomenex (Lane Cove West, NSW,

95

Australia).

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The model wine used in this study consisted of 12% (v/v) aqueous ethanol, 0.011 M potassium

98

hydrogen tartrate and 0.007 M tartaric acid. The final optimised method was applied to ten white and

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ten red Australian wines made from different varieties (Shiraz, Cabernet Sauvignon, Merlot, Pinot

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Noir, Chardonnay, Sauvignon Blanc, Riesling and Semillon) and vintages (2007 – 2017). All of the

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measurements were conducted in triplicate.

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LC-QQQ-MS analysis conditions



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LC-QQQ-MS analysis was performed on an Agilent 6400 series (Agilent Technologies Australia,

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Mulgrave, VIC, Australia) LC system, including a thermostated autosampler (G1367E) with a 100 µL

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injection loop, and a binary pump (G1316C) connected to an Agilent 6470 Triple Quad LC/MS system.

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The temperature of the autosampler was set at 20 °C, and sample injection volume was 1.5 µL.

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Aldehyde compounds binding to hydrogen sulfite (i.e. hydroxyalkylsulfonate) were separated by a

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ACQUITY UPLC BEH C18 column (Waters Australia, Rydalmere, NSW, Australia) at 30 °C by

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passage of the mobile phase A (0.04% formic acid in water) and B (0.04% formic acid in methanol) at

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the flow rate of 0.3 mL/min and following the schedule: 0 min, 100% A; 5 min, 100% A; 15 min, 60%

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A; 25 min, 0% A; 27 min, 0% A; 30 min, 100% A; 35 min, 100% A. Total running time was 40 min.

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Analysis performed on the triple quadrupole was carried out in MRM and negative ionization mode.

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All of the monitored transitions and optimised parameters are listed in Table 1. Data was acquired and

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analysed through Agilent MassHunter Workstation software, version B.09.00.

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Optimization of the sample pre-treatment

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Calculation of the required amount of SO2 for sample preparation

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The amount of SO2 required to ensure gross binding of free aldehydes in wine was calculated from the

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dissociation constants (𝐾𝑑) of the addition products of hydrogen sulfite and aldehyde compounds (i.e.

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hydroxyalkylsulfonates) (Eq. 1). The calculation was performed to ensure that less than 5% of the total

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aldehydes in wine would be in free fraction (Eq. 2). 𝐾𝑑 =

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[𝑎𝑙𝑑𝑒ℎ𝑦𝑑𝑒 ― 𝐻𝑆𝑂3― ]

[𝐻𝑆𝑂3― ] = 𝐾𝑑 ∙

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[𝑓𝑟𝑒𝑒 𝑎𝑙𝑑𝑒ℎ𝑦𝑑𝑒][𝐻𝑆𝑂3― ]

[𝑎𝑙𝑑𝑒ℎ𝑦𝑑𝑒 ― 𝐻𝑆𝑂3― ] [𝑓𝑟𝑒𝑒 𝑎𝑙𝑑𝑒ℎ𝑦𝑑𝑒]

where 6 ACS Paragon Plus Environment

(1) (2)

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[𝑓𝑟𝑒𝑒 𝑎𝑙𝑑𝑒ℎ𝑦𝑑𝑒] [𝑎𝑙𝑑𝑒ℎ𝑦𝑑𝑒 ― 𝐻𝑆𝑂3― ]

= 0.05

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According to de Azevedo,22 of the aldehydes to be quantified, benzaldehyde had the weakest binding

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strength with hydrogen sulfite (i.e. highest 𝐾𝑑 of 2.83 × 10-3). The calculated amount of SO2 required

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to ensure 95% of benzaldehyde in bound form was 3.7 g/L. Considering other wine components may

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bind part of the added SO2, a concentration of 6 g/L of SO2 aqueous solution was selected as the

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standard amount for sample preparation to ensure an excess amount of SO2 in wine samples.

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Optimization of pH and assessment of sample stability

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The pH of separate of model wine batches, containing 50 mg/L free SO2 (unrelated to the 6 g/L SO2

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added during sample preparation for LC-QQQ-MS analysis), were adjusted to 3.0, 3.2, 3.5, 3.8 and 4.0

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with tartaric acid (1 M) or NaOH (1 M). Then, 1.0 mL of each of the model wine samples were spiked

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with IS and analytes to achieve the final concentration as follows: 5-methylfurfural (56.9 µg/L),

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benzaldehyde (28.2 µg/L), methional (30.3 µg/L), furfural (107.9 µg/L), 2-methylpropanal (12.9 µg/L),

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3-methylbutanal (31.5 µg/L), 2-phenyacetaldehyde (58.1 µg/L), (E)-2-hexenal (7.3 µg/L), (E)-2-

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heptanal (5.9 µg/L), hexanal (28.2 µg/L), (E)-2-octenal (6.5 µg/L), nonanal (12.0 µg/L), and (E)-2-

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nonenal (5.6 µg/L). Each sample was then analysed as described in Section 2.2.

143 144

To examine the stability of samples, 1.0 mL of model wine sample at pH of 3.5 was prepared as

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described above and injected over 3 days. On the first day, the sample was injected every 3 hours for

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24 hours consecutively. The sample was then injected again on consecutive days, at the same time as

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the first injection.

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Optimised sample preparation



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1.0 mL of wine sample was spiked with 10 µL of an IS solution consisting of 15.0 mg/L 4,5-

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dimethylfurfural, 7.3 mg/L hydrocinnamaldehyde, 13.0 mg/L benzaldehyde-d6, 9.6 mg/L 3-

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(methylthio)butanal and 11.0 mg/L hexanal-d12. Then 45 µL of 200 g/L Na2S2O5 aqueous solution

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(final concentration of SO2 at 6 g/L) was added to the wine. Afterward, the sample was syringe filtered

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and analysed by LC-QQQ-MS. When analysing multiple prepared samples, the samples were stored in

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the LC autosampler at 20 °C prior to analysis.

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Method validation procedure

158 159

Linearity and selection of IS

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Linearity was determined by spiking wine samples (model wine, 4 white and 4 red wines made from

161

different varieties) with 8 levels of aldehyde concentrations, covering concentrations from 0 to 1700

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µg/L, a concentration range which has been previously reported for these compounds in wine31 (Table

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2). For the purposes of determining linearity, the aldehyde concentrations naturally present in the white

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and red wines were set to 0 µg/L. The concentration of each added analyte was then plotted against the

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peak area ratio of the analyte to internal standard. The potential internal standard compounds tested in

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this trial included 4,5-dimethylfurfural, hydrocinnamaldehyde, 3-(methylthio)butanal, 3,5,5-

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trimethylhexanal, benzaldehyde-d6, furfural-d4, 3-methylbutanal-d2 and hexanal-d12 (Table 1 and 2).

168 169

Limit of detections (LOD) and limit of quantification (LOQ)

170

Limit of detections (LODs) and quantifications (LOQs) were generally calculated as the lowest

171

concentration of each analyte showing signal-to-noise ratio of greater than 3 and 10 respectively. In this

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study, the LODs trials were carried out in both white and red wine matrices. However, due to the

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operation of the MS in MRM mode, the baseline noise at retention times corresponding to 58 ACS Paragon Plus Environment

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methylfurfural, benzaldehyde, methional, furfural, 2-methylpropanal and 3-methylbutanal was

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negligible. Therefore, the LODs of these analytes were determined by injecting a series of samples

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containing decreasing concentrations of the analytes until there was no significant difference (p ≤ 0.05)

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in signal intensity between consecutive standards.31

178 179

Recovery, repeatability and reproducibility

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Triplicate white and red wine samples were spiked with all 13 analytes at two concentrations (Table 3),

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which corresponded to relatively high and low concentrations of the aldehydes in wine. Recoveries

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were determined from the analysis of the spiked and unadulterated wine samples, and calculated from

183

the calibration curves established in real wine matrices.

184 185

For repeatability, one white and one red wine sample were spiked with all 13 analytes and analysed 5

186

times consecutively within one day. The concentrations were at intermediate points of quantitation

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calibration curves (see Section 2.3.2), and chosen to reflect typical levels found in oxidized wines. For

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reproducibility, three sets of triplicate spiked white and red wine samples were prepared and analysed

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on three different days within one week. Both repeatability and reproducibility results were expressed

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in terms of their respective relative standard deviations (RSD).

191 192

Statistical analysis

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Statistical analyses were carried out on software Excel 2013 (Microsoft, Redmond, Washington, USA)

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and SPSS (IBM, Armonk, New York, USA). The significant differences for response intensity of the

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analytes in the pH optimisation experiment were determined by Tukey’s test with a 95% confidence

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level. Student’s t-tests at 95% confidence level were performed to compare the slopes of calibration



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curves of each analyte in different wine matrices. Quoted uncertainty is the standard deviation of three

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replications within one treatment.

199 200

Results and discussions

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Optimization of sample pre-treatment

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Assessment of linearity

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The linearity of the 13 analyte calibration curves for this methodology was examined in model, white

206

and red wines respectively. Most analytes displayed good linearity in the three tested matrices, as the

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coefficient of determination (R2) values ranged from 0.981 to 0.999 (Table 2), except for methional in

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model wine (0.966). For this methodology, the ISs were selected based on their ability to provide

209

consistent calibration slopes over different wine matrices (Table 2). However, 5-methylfurfural

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displayed significantly different slopes for the calibration curves among the three matrices with all of

211

the eight potential IS compounds (supplementary Table 1). Therefore, none of the tested IS compounds

212

could offset matrix effects for this analyte. In addition, significantly different calibration curve slopes

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for certain analytes, such as furfural, (E)-2-hexenal, (E)-2-octenal, (E)-2-nonenal (higher sensitivity in

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model wine) and methional (lower sensitivity in model wine), were observed between model and real

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wines, but not between white and red wines. Due to this similarity, one calibration curve in either white

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or red wine was sufficient for the quantitation of both red and white wine samples. Based on these

217

results, the calibration curve constructed in Chardonnay wine was subsequently used for quantitative

218

calculation. Different set of ISs were able to overcome the different calibration curve slopes between

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model and real wines to some extent, but better validation results obtained when using calibrations

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established in real wines (see Section 3.2, model wine calibration data not shown). The reason for the 10 ACS Paragon Plus Environment

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different sensitivity for certain analytes when analysed in model wine versus real wines (Table 2) is

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uncertain. The co-elution of the extra components of real wine, compared to model wines, with the

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measured analytes and/or internal standards may lead to suppression or enhancement effects during MS

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ionisation processes, but further work is required to confirm such effect.

225 226

Optimization of pH

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The mobile phase for the chromatographic gradient had a pH of 2.5 within the first 15 min and 10 of

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the 13 aldehyde compounds eluted during this period. The remaining three aldehydes eluted when the

229

pH of the mobile phase increased from 2.5 to 4.4 within 22.5 min (Table 1). Despite these changes in

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pH during the chromatographic gradient, all of the analytes produced well-resolved MS signals (Figure

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1). However, many of the early (i.e. within the first 3 min) eluting analytes could potentially co-elute

232

with polar wine components, such as organic acids that are at g/L concentration levels in wine. The

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latter components could induce localised pH changes in the mobile phase and cause potential drift in

234

the retention times and/or impact analyte ionization in the MS detector. To investigate the impact of

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sample pH on the chromatographic retention times and MS sensitivity, the analytes were analysed after

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addition to a model wine, containing tartaric acid, at a varied pH values (i.e. pH 3.0 to 4.0).

237 238

Despite varying the sample pH from 3.0 to 4.0, the retention times of analytes remained unchanged as

239

indicated in Table 1, demonstrating that sample pH had no impact on elution time and the detectability

240

of the analytes. However, it was evident that there was a significant influence of sample pH on MS

241

response intensity for certain analytes, with the intensity increasing with pH, particularly for

242

phenylacetaldehyde, hexanal (including its deuterated analogue), nonanal and the internal standard

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hydrocinnamaldehyde (Figure 2). However, the degree of variation of the MS response intensity as a

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function of the average signal was small. Nonanal had the largest variation (RSD 10.6%), followed by 11 ACS Paragon Plus Environment


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methional, (E)-2-heptenal and hexanal (RSD 7.5 – 6.6%), while the majority of the analytes had minor

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variations of less than 5% RSD. The reason for the impact of the sample pH on the analyte response

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intensity was uncertain. Changing the pH of the wine would alter the balance of the SO2 equilibrium,

248

and hence hydrogen sulfite concentration, but given the pKa1 and pKa2 values of 1.8 and 7.0,33

249

respectively, the variation of hydrogen sulfite over the pH range 3 to 4 would be minimal. For example,

250

the hydrogen sulfite would vary from 93 to 99 % of the total added SO2 from pH 3 to 4, which in turn

251

would equate to 1.86 to 1.98 g/L of the added SO2. This remains in vast excess of the sub-mg/L

252

combined concentration of the aldehyde compounds. Given the minor influence on the analyte signal

253

intensity within the relatively large pH range of 3 – 4, it was decided not to adjust wine pH prior to

254

analysis for the remainder of this study. To limit the influence of any pH dependence on the result, both

255

wines and calibration standards could be adjusted to the same pH value.

256 257

Stability of prepared samples

258

The stability of the analytes (i.e. α-hydroxyalkylsulfonates) after sample pre-treatment was examined

259

and the result was expressed as recovery percentage in comparison with the freshly prepared sample

260

measured on the first injection (Supplementary Table 3). All of the analytes were relatively stable over

261

the first 9 hours (recovery variation < 18%), and most remained stable up to 12 hours. The exception

262

were 2-phenylacetaldehyde and nonanal, which had increased recoveries of 124% and 127%

263

respectively by 12 hours (Supplementary Table 3). Therefore, wine samples can be analysed

264

immediately by LC-QQQ-MS after sample preparation, or alternatively the prepared samples are stable

265

for up to 9 – 12 hours, depending on the specific analyte.

266 267

Method validation procedure

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Limit of detection (LOD) and limit of quantification (LOQ)

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The results of the LOD and LOQ in wine matrices are listed in Table 2. The LODs of the 13 analytes

271

ranged from 0.10 to 3.46 µg/L, which are mostly lower than the respective odour detection thresholds

272

for the free form of the aldehyde in water or model wine solution as summarised by Mayr.31 The

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exception was (E)-2-nonenal that has a LOD of 0.18 µg/L (Table 2), marginally higher than the quoted

274

odour threshold of 0.17 µg/L for the free form in water.31

275 276

Accuracy

277

The accuracy of the methodology was assessed in terms of recovery experiments and conducted using

278

internal standard-based calibration curve constructed in Chardonnay (Table 3). The average and

279

standard deviation of the recovery results of each analyte at the two concentration in both white and red

280

wines were calculated (Table 3). In general, this method gave very satisfactory performance according

281

to recovery, repeatability and reproducibility test results. The overall average recovery result for each

282

compound was within the range of 97% to 114% and the standard deviation was less than 10%, except

283

for 2-methylpropanal (14%) and methional (16%). The larger uncertainty in recovery for the latter two

284

compounds was largely due to higher recoveries in red wine compared to white wine for a given

285

concentration (Supplementary Table 4), and this was a difference that was not observed for the other

286

compounds. However, the individual recoveries for these particular compounds, across different wine

287

colours and concentrations, were still within the range of 95-127%, which was considered an

288

acceptable recovery range. No significant differences were observed at high versus low concentrations

289

for a given aldehyde within red or white wines (Supplementary Table 4). Consequently, one calibration

290

curve in real a wine matrix, regardless white or red wine, was adequate for the analysis for all wine

291

samples. Repeatability showed a RSD lower than 3.8% and 8.0% in white and red wine, while the

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corresponding reproducibilities were below 8.4% and 2.9% (RSD) respectively (Table 3). 13 ACS Paragon Plus Environment


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Analysis of wines

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Twenty Australian commercial wines (ten white and ten red wines) made from various varieties and

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vintages were analysed with this methodology to quantify the concentrations of 12 aldehyde

297

compounds (Table 4). Higher concentrations of furfural, 2-methylpropanal and methional were found

298

in the four older white wines (i.e. vintage after 2015), while (E)-2-octenal and (E)-2-nonenal showed

299

higher concentrations in the younger white wines (i.e. vintages 2016 and 2017). The four youngest red

300

wines (i.e. vintage 2017) contained higher concentrations of benzaldehyde and 3-methylbutanal, but the

301

highest concentrations of hexanal, nonanal, 2-methylpropanal and (E)-2-nonenal were detected in the

302

red wine samples from vintages 2016 to 2011. Interestingly, the oldest red wine (i.e. wine sample red

303

10, vintage 2007) had lower concentrations of these aldehydes. This is probably due to the ability of

304

phenolic compounds to react with aldehydes via cycloaddition or polymerisation.20 The vastly lower

305

concentrations of phenolic compounds in white compared to red wine also most likely explains why

306

most aldehyde compounds, such as hexanal, methional, furfural, 3-methylbutanal and (E)-2-octenal,

307

showed higher concentrations in the white wines compared to the red wines, regardless of vintage. In

308

addition to the direct reaction of aldehydes with phenolic compounds, the phenolics in red wine are

309

also suggested to provide certain antioxidant capacity to restrict the production of aldehyde

310

compounds.34 The concentrations of the (E)-2-alkenals, apart from (E)-2-nonenal, were below their

311

LODs in all wines. The concentrations of these aldehydes can be low after fermentation due to

312

reduction by yeast to their corresponding aldehydes. In addition, they can also react with nucleophiles,

313

such as glutathione to form alternate products.35

314 315

In conclusion, the use of SO2 to induce the aldehyde compounds in wine to form their hydrogen sulfite-

316

bound form provided a method for the rapid measurement of the total concentrations of 12 oxidative14 ACS Paragon Plus Environment

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related volatile aldehyde compounds in wine. The preparation of samples is simple, requires no

318

incubation time, and the utilisation of LC-QQQ-MS offers an accurate quantification for the target

319

analytes. The added advantage is that after sample preparation, the analytes are no longer volatile and

320

errors of measurement due to analyte volatilisation during sample storage prior to analysis are

321

minimised. The results of linearity, repeatability and reproducibility provide confidence in the

322

reliability and efficiency of this method. The established method was applied to measure the

323

concentrations of the 12 aldehyde compounds in 20 commercial wines and the result are consistent

324

with previously reported concentration range for these analytes. Comparison of different methods to

325

quantify total aldehyde compounds in wine will be an area of future work.

326 327

Acknowledgements

328

This work was conducted as part of a PhD program at the National Wine and Grape Industry Centre

329

(NWGIC) supported by a Charles Sturt University Postgraduate Research Scholarship and an

330

International Tuition Payment scholarship. Further stipend and operating support was provided by

331

Wine Australia (AGW Ph1509). The NWGIC is a research centre within CSU in alliance with the

332

Department of Primary Industries New South Wales (NSW) and the NSW Wine Industry Association.

333

Technical assistance from Michael Loughlin during LC-QQQ-MS analysis was greatly appreciated.

334 335 336

References (1) Laurie, V. F.; Clark, A. C. Wine oxidation. In Oxidation in foods and beverages and antioxidant

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(2) Ugliano, M. Oxygen contribution to wine aroma evolution during bottle aging. Journal of Agricultural and Food Chemistry 2013, 61, 6125-6136. 15 ACS Paragon Plus Environment


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pentafluorobenzyl derivatives of aldehydes by gas chromatography with electron-capture or mass

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spectrometric detection: Validation of an optimized strategy for the determination of oxygen-related

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odor-active aldehydes in wine. Journal of Chromatography A 2006, 1122, 255-265.

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Quantitative analysis by GC-MS/MS of 18 aroma compounds related to oxidative off-flavor in wines.

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(32) Jackowetz, J. N.; Mira De Orduña, R. Improved sample preparation and rapid UHPLC analysis

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(33) Waterhouse, A. L.; Sacks, G.; Jeffery, D. W. Sulfur Dioxide. In Understanding Wine Chemistry. John Wiley & Sons Ltd: Chichester, UK, 2016; pp 164-172. (34) Paixão, N.; Perestrelo, R.; Marques, J. C.; Câmara, J. S. Relationship between antioxidant capacity and total phenolic content of red, rosé and white wines. Food Chemistry 2007, 105, 204-214. (35) Clark, A. C.; Deed, R. C. The chemical reaction of glutathione and trans-2-hexenal in grape juice

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media to form wine aroma precursors: the impact of pH, temperature, and sulfur dioxide. Journal of

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437 438

Figure Captions

439

Figure 1. LC-QQQ-MS chromatogram of a spiked model wine showing the peaks of all of the analytes

440

and ISs. Peaks: 1, furfural; 2, 2-methylpropanal; 3, methional; 4, 5-methylfurfural; 5, benzaldehyde-d6;

441

6, benzaldehyde; 7, 3-methylbutanal; 8, 3-(methylthio)butanal; 9, 4,5-dimethylfurfural; 10, (E)-2-

442

hexenal; 11, 2-phenylacetaldehyde; 12, hexanal-d12; 13, hexanal; 14, hydrocinnamaldehyde; 15, (E)-2-

443

heptenal; 16, (E)-2-octenal; 17, (E)-2-nonenal; 18, nonanal.

444 445

Figure 2. Influence of pH on LC-QQQ-MS response in model wine (n=3). The result is shown as the

446

MS response intensity (peak area) for each analyte. Numbers on the top of the bar charts are relative

447

standard deviation (RSD) results of the response intensity of each analyte over the whole pH range.

448



Page 22 of 28

13

110 100

4

25 1

15

3

0 0.5

1.0

1.5

2.0

2.5

14

12

5

5

30

6

10 2

intensity

20

retention time (min)

11

18

20 7

17

15

10

9

16

10 8

intensity

90 40

0 0

5

10

15

retention time (min)

ACS Paragon Plus Environment

20

25

al

an

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hi on et al hy lfu rfu be ra nz l al de 3hy m et de hy lb ut an (E )-2 al ph -h en ex yl en ac al et al de hy de he xa (E na )-2 l -h ep te (E na )-2 l -o ct (E en )-2 al -n on en al no na na l IS _b en IS _m et hi o IS _d im fu r IS _h ex IS _h yd ro m

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response intensity

Page 23 of 28 Journal of Agricultural and Food Chemistry

80000 7.1

70000

60000 3.0 3.2 3.5 3.8 4.0

10000

3.1 7.5

4.9

50000 3.4

15000 2.1

3.1 4.1

2.0 10.6

2.6 6.6


3.0 1.5 2.2

2.9 2.2 4.5

0


Page 24 of 28

Table 1: Mass spectral transitions and collision energies selected for analysis of aldehyde compoundsb. retention time (min)

precursor ion to product iona (m/z)

fragmentor

collision energy (eV)

Analytes furfural 0.827 177 – 81 50 8 2-methylpropanal 1.248 153 – 81 60 9 methional 1.638 185 – 81 60 6 5-methylfurfural 1.712 191 – 81 55 10 benzaldehyde 2.059 187 – 81 55 7 3-methylbutanal 3.218 167 – 81 60 8 (E)-2-hexenal 6.655 179 – 81 60 4 2-phenylacetaldehyde 7.090 201 – 81 60 7 hexanal 12.308 181 – 81 60 7 (E)-2-heptenal 14.502 193 – 81 60 8 (E)-2-octenal 18.725 207 – 81 60 7 (E)-2-nonenal 21.352 221 – 81 60 7 nonanal 22.464 223 – 81 60 5 Internal standards (abbr.) furfural-d4 (fur) 0.823 180 – 81 55 10 benzaldehyde-d6 (ben) 1.975 193 – 81 50 8 3-methylbutanal-d2 (3mb) 3.347 169 – 81 60 10 3-(methylthio)butanal (methio) 3.802 199 – 81 60 8 4,5-dimethylfurfural (dimfur) 5.901 205 – 81 60 8 hexanal-d12 (hex) 11.930 193 – 81 60 8 hydrocinnamaldehyde (hydro) 13.854 215 – 81 60 10 3,5,5-trimethylhexanal (trimhex) 20.730 223 – 81 60 5 a The precursor ions of all of the analytes are the hydrogen sulfite bound forms (i.e. α-hydroxyalkylsulfonates), and the product ion is the hydrogen sulfite ion. b

The cell accelerator voltage is 1 V and the polarity is negative for all analytes.


Page 25 of 28


Table 2. Calibration data in different matrices. calibration range LOD (µg/L) (µg/L) furfuralc,e 8.6 – 1726.4 3.45 2-methylpropanalc 1.0 – 206.5 0.10 methionalc,e 2.4 – 116.4 0.49 5-methylfurfuralb,c 4.6 – 218.6 0.91 benzaldehydec 2.3 – 108.2 0.90 3-methylbutanalc,f 2.5 – 251.7 2.52 (E)-2-hexenale 0.59 – 14.0 0.49 2-phenylacetaldehyde 6.4 – 222.9 0.46 hexanal 2.3 – 108.2 0.23 (E)-2-heptenal 0.47 – 11.4 2.13 (E)-2-octenale 0.52 – 12.5 0.36 (E)-2-nonenale 0.45 – 10.8 0.18 nonanalf 0.96 – 23.0 0.12 a Abbreviations for IS can be found in Table 1.

LOQ (µg/L) 11.5 0.34 1.62 3.04 3.01 8.39 1.63 1.55 0.77 7.10 1.21 0.60 0.39

ISa dimfur ben hydro n/a methio ben hydro dimfur hydro hex dimfur dimfur dimfur

model wine sloped (2.11 ± 0.03) × 10-3 (1.49 ± 0.03) × 10-2 (0.69 ± 0.03) × 10-3 n/a (3.40 ± 0.06) × 10-3 (0.94 ± 0.02) × 10-2 (2.84 ± 0.08) × 10-3 (1.11 ± 0.03) × 10-2 (1.77 ± 0.04) × 10-2 (1.66 ± 0.01) × 10-3 (2.48 ± 0.02) × 10-2 (2.29 ± 0.01) × 10-2 (3.05 ± 0.08) × 10-2

R2 0.996 0.994 0.966 n/a 0.996 0.991 0.991 0.986 0.990 0.999 0.999 0.999 0.992

white wine sloped, g (1.17 ± 0.07) × 10-3 (1.35 ± 0.09) × 10-2 (1.00 ± 0.12) × 10-3 n/a (3.32 ± 0.48) × 10-3 (1.16 ± 0.06) × 10-2 (2.37 ± 0.15) × 10-3 (1.00 ± 0.10) × 10-2 (1.82 ± 0.16) × 10-2 (1.52 ± 0.06) × 10-3 (1.74 ± 0.11) × 10-2 (1.85 ± 0.10) × 10-2 (2.54 ± 0.21) × 10-2

R2 0.991 0.992 0.990 n/a 0.994 0.989 0.998 0.994 0.995 0.995 0.995 0.997 0.996

red wine sloped, g (1.45 ± 0.15) × 10-3 (1.51 ± 0.15) × 10-2 (1.27 ± 0.18) × 10-3 n/a (3.17 ± 0.34) × 10-3 (0.94 ± 0.12) × 10-2 (2.26 ± 0.19) × 10-3 (1.04 ± 0.09) × 10-2 (1.68 ± 0.23) × 10-2 (1.62 ± 0.16) × 10-3 (1.67 ± 0.14) × 10-2 (1.84 ± 0.16) × 10-2 (2.56 ± 0.23) × 10-2

R2 0.992 0.994 0.995 n/a 0.993 0.981 0.997 0.994 0.997 0.990 0.998 0.998 0.993

b

No suitable IS identified, as there were significant different slopes between red and white wine matrices (Supplementary Table 1).

c

No baseline noise found and LOD and LOQ were determined by injecting samples containing decreasing concentration levels of analytes.

d

Quoted slope and uncertainty are the average and standard deviations generated in model wine (n=3), white wine (n=4) and red wine (n=4) respectively.

e f

Showed significantly different (p ≤ 0.05) linearity slope between model and real wine matrices.

Showed significantly different (p ≤ 0.05) linearity slope between model wine and white wine matrices.

No significant difference (p ≤ 0.05) was observed in the slopes for the white and red wines, and combined data for the slopes and uncertainties of all white and red wines can be found in Supplementary Table 2.

g



Page 26 of 28

Table 3. Method validation data.

furfural 2-methylpropanal methional 5-methylfurfural benzaldehyde 3-methylbutanal (E)-2-hexenal 2-phenylacetaldehyde hexanal (E)-2-heptenal (E)-2-octenal (E)-2-nonenal nonanal a

calibration data for quantitationa 0.00110 0.01471 0.00115 n/a 0.00396 0.01227 0.00221 0.01116 0.02058 0.00158 0.01584 0.01701 0.02450

recovery test concentrations (µg/L) 10; 204 0.67; 13 2.6; 51 34; 164 2.7; 55 5.3; 106 0.56; 11 2.6; 52 2.5; 50 0.52; 10 1.2; 24 0.64; 129 5.1; 1024

recoveryb (%) 111 ± 3 111 ± 14 113 ± 16 n/a 102 ± 6 97 ± 9 97 ± 4 111 ± 2 99 ± 3 99 ± 3 97 ± 8 104 ± 2 114 ± 6

repeatability, white RSD (%) 1.2 1.1 1.9 n/a 3.8 2.1 2.3 2.3 1.2 2.9 2.1 0.9 1.8

repeatability, red RSD (%) 1.9 3.7 2.6 n/a 0.3 2.3 2.3 2.6 2.9 8.0 1.7 3.1 3.8

reproducibility, white RSD (%) 8.4 1.9 1.2 n/a 1.7 2.9 0.3 0.4 6.1 0.4 0.5 0.6 6.0

reproducibility, red RSD (%) 1.2 1.1 1.9 n/a 2.3 2.1 2.3 2.3 1.2 2.9 2.1 0.9 1.8

Slopes of the calibration curves used for accuracy calculation, constructed in Chardonnay wine.

b

Quoted recovery and uncertainty are the average and standard deviations of the recovery results calculated from both triplicated white (n=1) and red wine (n=1) samples at two different concentration levels.


Page 27 of 28


Table 4. Concentrations (µg/L) of volatile aldehyde compounds in the 20 commercial wine samples.

vintage age when analysed

red 1 2017

Rapid Quantitation of 12 Volatile Aldehyde ... - ACS Publications

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