Comprehensive Study of the Evolution of the GasâLiquid Partitioning

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Food and Beverage Chemistry/Biochemistry

Comprehensive study of the evolution of gas-liquid partitioning of acetaldehyde during wine alcoholic fermentation Evelyne Aguera, Yannick Sire, Jean-Roch Mouret, Jean-Marie Sablayrolles, and Vincent FARINES J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b01855 • Publication Date (Web): 22 May 2018 Downloaded from http://pubs.acs.org on May 22, 2018

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Journal of Agricultural and Food Chemistry

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Comprehensive study of the evolution of gas-liquid partitioning of acetaldehyde during

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wine alcoholic fermentation

3 4

Evelyne Agueraa, Yannick Sirea, Jean-Roch Mouretb, Jean-Marie Sablayrollesb, Vincent

5

Farinesb*

6

a

7

b

8

*

INRA, UE 999, F-11430 Gruissan, France SPO, Univ Montpellier, INRA, Montpellier SupAgro, Montpellier, France

: corresponding author, [email protected]

1 ACS Paragon Plus Environment


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Abstract:

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Determining the gas−liquid partitioning ( ) of acetaldehyde during alcoholic fermentation is

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an important step in the optimization of fermentation control with the aim of minimizing the

12

accumulation of this compound responsible for the undesired attributes of green apples and

13

fresh cut grass in wines. In this work, the effect of the main fermentation parameters on the

14

of acetaldehyde was assessed. values were found to be dependent on the temperature and

15

composition of the medium. A non-linear correlation between the evolution of the and

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fermentation progress was observed, attributed to the strong retention effect of ethanol at low

17

concentrations and it was demonstrated that the partitioning of this specific molecule was not

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influenced by the CO2 production rate. A model was developed to quantify the of

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acetaldehyde with a very accurate prediction as the difference between the observed and

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predicted values did not exceed 9%.

21 22

Keywords:

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Acetaldehyde, wine fermentation, online Gas Chromatography measurement, partition

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coefficient, gas−liquid transfer, modeling


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1. Introduction

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Acetaldehyde is an important metabolic intermediate and is quantitatively one of the key

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leaking carbonyl compounds synthetized by yeasts during alcoholic fermentation (AF), with

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final concentrations varying between 10 and 200 mg L-1 in red wines and 10 and 500 mg L-1

30

in white wines.1-5 Acetaldehyde can also be synthesized after AF through chemical oxidation

31

of ethanol when wine is exposed to oxygen.6 In wines, acetaldehyde positively contributes to

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the stabilization of the red wine color and to the levels of anthocyanin-tannin polymerization

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during fermentation and wine aging.7-9 Notably, the specific compound Vitisin B is a

34

combination between anthocyanin malvidin and acetaldehyde; it is a very stable molecule

35

responsible for the color stability especially in aged wines.9 However, in most finished wines,

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acetaldehyde imparts the undesired aromatic attributes of green apples, fresh cut grass and

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walnuts, with a sensory threshold from 100 to 125 mg L-1 for free-acetaldehyde.10,11 These

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sensory descriptors are commonly found in fortified wines that are intentionally oxidized (e.g.

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Sherry, Port, Vin Jaune)..12-14 Acetaldehyde is also a reactive low-molecular weight

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compound that can strongly bind ( = 2.06 · 10-6) the preservative sulfur dioxide (SO2).15

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SO2 binding reduces the sensory effect of acetaldehyde as well as the functional properties of

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SO2. Particularly, wines with important levels of acetaldehyde will require more SO2 to get

43

the concentrations of free or active SO2 required for preservation. Bound SO2 does not have

44

the same antimicrobial, anti-enzymatic or antioxidant properties as free SO2. 6,16,17 According

45

to some studies, SO2 induces acetaldehyde formation by yeasts and final concentration of

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acetaldehyde is higher in wines fermented with SO2 than in wines fermented without SO2. 18-22

47

SO2-induced production of acetaldehyde appears to be related to SO2 resistance in yeasts.20, 21

48

During AF, acetaldehyde production is linked to yeast fermentative metabolism of sugars via

49

the action of pyruvate decarboxylase and alcohol dehydrogenase.23, 24 Acetaldehyde



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production is a yeast strain but also a species-dependent trait that has been described in

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several studies.12, 18-20, 22, 25-38 Acetaldehyde is mainly accumulated in the early fermentation

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stages, i.e., during the lag and growth phases; thereafter, its concentration decreases because

53

of partial reutilization by yeast.2, 19, 24, 25, 27, 31, 37, 39-42 Parameters such as oxygen, temperature

54

and sulfur dioxide concentration affect the production/consumption rates of acetaldehyde by

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yeasts and, as a consequence, the accumulation/decrease of its concentration in the medium.43

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Low pH, absence of oxygen, and/or a high sugar content apparently promotes acetaldehyde

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production by yeasts.39 Some authors have reported that the fermentation temperature does

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not influence the final total acetaldehyde content in wine while others observe superior

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synthesis of acetaldehyde at 30 °C compared with that produced at 12 °C or 24 °C. 2,33

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Acetaldehyde is partially volatile, with a vapor pressure of 120 kPa and boiling point of 20.1

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°C.44 Henry’s law constant for acetaldehyde in water solution is 15 mol kg-1 bar-1.45 Despite

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its high volatility, the gas-liquid transfer of this key metabolite in oenology has not been

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studied in detail. Such a study is essential because it will enable to perform a complete mass

64

balance of acetaldehyde production, taking into account losses in the exhaust CO2 during

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fermentation and accumulation in the liquid phase.

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The objective of this study was to develop a model of the evolution of the partition coefficient

67

between the gas and liquid phases of acetaldehyde in winemaking fermentations. This work

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was divided into different steps. First, we assessed the extent to which fermentative

69

parameters impacted the gas-liquid partitioning ( ) of acetaldehyde during fermentation.

70

Second, based on this dataset, we developed a model to predict the partition coefficient for

71

this particular compound throughout the fermentation process. This model was then validated

72

in different winemaking situations. Finally, the model was used to estimate the rate of free-

73

acetaldehyde production, consumption and loss under several fermentation conditions.

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2. Materials and methods

76 77

2.1. Media

78 79

2.1.1. Model solutions simulating must and wine

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For the model solution simulating the must, 220 g L-1 glucose was added to a buffer solution

81

containing 6 g L-1 citric acid and 6 g L-1 malic acid, which was adjusted to pH 3.3 with

82

sodium hydroxide. For the model solution simulating wine, the buffer solution was

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supplemented with 13.1% (v/v) ethanol, without the addition of glucose.

84 85

2.1.2 Model solutions simulating musts at different stages of fermentation

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Changes in during fermentation were studied by supplementing the buffer solution

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described above with (1) 209 g L-1 glucose and 0.7% (v/v) ethanol, (2) 198 g L-1 glucose and

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1.3% (v/v) ethanol, (3) 187 g L-1 glucose and 2.0% (v/v) ethanol, (4) 176 g L-1 glucose and

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2.6% (v/v) ethanol, (5) 154 g L-1 glucose and 3.9% (v/v) ethanol, (6) 132 g L-1 glucose and

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5.2% (v/v) ethanol, and (7) 88 g L-1 glucose and 7.9% (v/v) ethanol. The resulting media

91

corresponded to a 5, 10, 15, 20, 30, 40 and 60% progression of fermentation.

92 93

2.1.3 Synthetic must for fermentation experiments

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We used a synthetic medium that mimicked grape musts.46 The base medium contained 90 g

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L-1 glucose, 90 g L-1 fructose, 6 g L-1 malic acid, 6 g L-1 citric acid, salts (0.75 g L-1 KH2PO4;

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0.50 g L-1 K2SO4; 0.25 g L-1 MgSO4; 0.155 g L-1 CaCl2; 0.20 g L-1 NaCl), vitamins (20 mg L-1

97

myo-inositol; 1.5 mg L-1 pantothenic acid; 0.25 mg L-1 thiamine; 2 mg L-1 nicotinic acid, 0.25

98

mg L-1 pyridoxine; 0.003 mg L-1 biotin), trace elements (4 mg L-1 MnSO4; 4 mg L-1 ZnSO4; 1

99

mg L-1 CuSO4; 1 mg L-1 KI; 0.4 mg L-1 CoCl2; 1 mg L-1 H3BO3; 1 mg L-1 (NH4)6Mo7O24) and



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anaerobic factors (0.05% v/v Tween 80; 15 mg L-1 ergosterol; 0.0005% v/v oleic acid). Even

101

if tartaric acid is the main organic acid occurring in grape juice and wine, it was replaced with

102

citric acid to prevent the formation of a tartrate precipitate during freezing at -20 °C. The

103

source of nitrogen was a mixture of ammonium (30%) and amino acids (70%). The initial

104

assimilable nitrogen concentration was set to 120 mg N L-1 for fermentations at a constant rate

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and at 200 mg N L-1 for standard fermentation. The amino-acid content of the medium was as

106

follows: 4.2 mg L-1 aspartic acid, 11.6 mg L-1 glutamic acid, 13.7 mg L-1 alanine, 35.2 mg L-1

107

arginine, 1.2 mg L-1 cysteine, 47.6 mg L-1 glutamine, 1.7 mg L-1 glycine, 3.1 mg L-1 histidine,

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3.1 mg L-1 isoleucine, 4.6 mg L-1 leucine, 1.6 mg L-1 lysine, 3.0 mg L-1 methionine, 3.6 mg L-1

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phenylalanine, 57.7 mg L-1 proline, 7.4 mg L-1 serine, 7.1 mg L-1 threonine, 16.9 mg L-1

110

tryptophan, 1.7 mg L-1 tyrosine and 4.2 mg L- 1 valine.

111 112

2.1.4. Yeast strains

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Fermentations were carried out with the commercial Saccharomyces cerevisiae strains Lalvin

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ICV oKay® for fermentations at a constant rate and EC1118 for standard fermentation

115

(Lallemand SA, Montreal, Canada). Fermentation tanks were inoculated with 200 mg L-1

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active dry yeast that had been previously rehydrated for 30 min at 35 °C in a 50 g L-1 glucose

117

solution (1 g of dry yeast diluted in 10 ml of solution).

118 119

2.2. Determination of the gas-liquid partition coefficients (static conditions)

120 121

2.2.1. Sample preparation

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The gas-liquid partition coefficients were measured in stainless steel tanks using the different

123

model solutions. The tanks contained 9 L of solution, and the headspace represented 30% of

124

the total volume. The temperature was kept constant at 18, 24 or 30°C. At t = 0, the solution


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was spiked with 10 mL of a 45 g L-1 acetaldehyde solution. The final concentration of

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acetaldehyde in the model solution was approximately 50 mg L-1. At t = 1 h, 1 h 15 min and

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1 h 30 min after acetaldehyde addition, the concentration in the liquid phase was precisely

128

measured using a commercial enzymatic test kit (Ref 984347, ThermoFischer scientific™),

129

whereas the acetaldehyde content in the gas headspace of the tank was measured by gas

130

chromatography. 47 These measurements were performed in triplicate to ensure that

131

equilibrium was reached.

132 133

2.2.2. Gas chromatography

134

The concentration of acetaldehyde in the gas phase was analyzed by using the online device

135

described by Morakul and Mouret.48, 49 The gas from the tank headspace was pumped at a

136

flow rate of 14 mL/min through a heated transfer line. It was then concentrated in a cold trap

137

(Tenax TM) for 6 min (desorption at 160 °C for 1 min) and injected onto a ZBWax (60 m ×

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0.32 mm × 0.5 µm, Phenomenex, Inc.) column. The injector was maintained at 200 °C.

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Helium was used as the carrier gas at a constant pressure of 120 kPa. The oven temperature

140

program was 38 °C for 3 min, followed by an increase of 3 °C/min to 65 °C; then 6 °C/min to

141

160 °C, at which it was maintained for 5 min; and an increase of 8 °C/min to 230 °C, a

142

temperature at which it was maintained for 5 min. A flame ionization detector was used at 260

143

°C. The online GC system was calibrated with a Sonimix 6000C1 instrument (LNI Schmidlin

144

SA). This equipment generates standard gases by dilution from standard gas bottles or

145

permeation tubes. A standard gas bottle (Messer) containing 2001 ppm of acetaldehyde (CAS

146

n° 75-07-0) in nitrogen was used for calibration of the online gas chromatography system in

147

this experiment.

148 149

2.2.3. Determination of the gas-liquid partition coefficient



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The distribution of a volatile compound (i) between the liquid and gas phases depends on the

151

vapor−liquid equilibrium (VLE), which is defined by the gas−liquid partition coefficient.

152

The gas−liquid partition coefficient, also called the dimensionless Henry’s law coefficient, is

153

defined as:

154

=

155

where is expressed as the ratio between the concentration of the compound in the gas phase

156

[ () in (mol or g) m−3] and that in the liquid phase [ () in (mol or g) m−3] at equilibrium.

157

Direct measurement of the acetaldehyde concentration in liquid (enzymatic method) and the

158

gas phases (gas chromatography) at equilibrium allows values in different media at

159

different temperatures to be determined.

()

(1)

()

160 161

2.3. Changes in the gas-liquid concentration ratio during fermentation

162 163

2.3.1. Fermentation conditions and control

164

Yeast fermentations were carried out in stainless steel tanks using synthetic must. The tanks

165

contained 9 L of must, and the headspace represented 30% of the total volume. The

166

temperature was kept constant at 20 °C. The CO2 released was automatically and

167

continuously measured with a gas mass flow meter. The high acquisition frequency and

168

precision of the flow meter allowed us to calculate the rate of CO2 production ( )

169

with a high level of precision. To determine the stripping effect, constant-rate fermentations

170

were performed. In these experiments, the rate of CO2 production was kept constant by a

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feedback control mechanism involving the addition of ammoniacal nitrogen via a peristaltic

172

pump (Ismatec Reglo).48, 50 We set up two fermentations in which the rates of CO2 production

173

were kept constant at 0.6 g L-1 h-1 (F_0.6) and 0.9 g L-1 h-1 (F_0.9) (Figure 1). F_0.6 was run

174

at 20 °C, whereas F_0.9 was performed at 24 °C. For F_0.6, the addition of diammonium 8 ACS Paragon Plus Environment

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phosphate (the solution contained 2.65 g of assimilable nitrogen per liter) was started at 12.5 g

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L-1 of produced CO2. For F_0.9, the addition of diammonium phosphate (the solution

177

contained 3.71 g of assimilable nitrogen per liter) was started at 17.5 g L-1 of produced CO2.

178

In the two cases, the rate of CO2 production was regulated between 10 and 85% progression

179

of the fermentation reaction.

180

Because the commercial Saccharomyces cerevisiae strain Lalvin ICV oKay® produced low

181

acetaldehyde levels, acetaldehyde (10 mL of 225 g L-1 solution) was added after

182

approximately every 10 g L-1 of CO2 produced during the fermentations. After each addition

183

of acetaldehyde, we waited for one hour before sampling to ensure that equilibrium of the

184

gas-liquid partition was reached.

185 186

2.3.2. Determination of the Gas-Liquid Partition Coefficients during Fermentation

187

The gas–liquid partition coefficients ( ) during fermentation were calculated by dividing the

188

volatile concentrations in the tank headspace (gas chromatography) by the concentrations in

189

the liquid (enzymatic method).

190 191

3. Results and discussion

192 193

3.1. Study of the gas-liquid partition coefficients in the model solutions

194 195

3.1.1. Effect of glucose and ethanol on the gas-liquid partition coefficients

196

The partition coefficients and standard deviations of the buffer, synthetic must and synthetic

197

wine are reported in Table 1. The results are consistent (within the same order of magnitude)

198

with the published results and values calculated from the chemical structure of acetaldehyde

199

based on the bond contribution or group contribution methods.45, 51, 52 Malic acid, citric acid



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and adjustment of the pH had a significant impact on the partition of acetaldehyde between

201

the gas and liquid phases, as shown by comparisons with published data obtained with pure

202

water at 25 °C (Table 1).45 This findings results from the “salting out” effect of the addition

203

of the two weak acids to the buffer, rather than from a modification of the pH. Acetaldehyde

204

is not present in a dissociated form, unlike volatile carboxylic acids.

205

The presence of glucose alone in the buffer solution increased the partition coefficient of

206

acetaldehyde (Figure 2A). Under our conditions of simulating a grape must, the mean relative

207

increase of was 25% from that of the buffer solution without glucose (Table 1). As for

208

other aroma compounds, we can expect that the release of acetaldehyde from the glucose

209

solution observed in this study resulted from the salting-out effect of glucose, which forms

210

hydrogen bonds with water molecules, thereby decreasing the activity of water, lowering the

211

free water content and decreasing the solubility of acetaldehyde.53

212

Unlike glucose, the addition of ethanol in the solution clearly increased retention of

213

acetaldehyde (Figure 2B). This effect was particularly notable for ethanol concentrations

214

lower than 20 g L-1. Beyond this concentration, the impact of ethanol on the partition

215

coefficient of acetaldehyde was less marked. The presence of 13% ethanol (i.e., the average

216

concentration in wines) decreased the values by up to 70% from that of the buffer solution

217

without ethanol. As previously described, ethanol increases the solubility of volatile

218

compounds in the matrix, thereby decreasing their headspace concentration. 54-60 Aznar et al.

219

also established a relationship between the headspace volatile compound concentration and

220

hydrophobicity (log Kow) by describing a decrease in headspace concentration upon

221

increasing the ethanol concentration of the solution from 4 to 42% (v/v). 56 A correlation

222

between the decrease in the headspace volatile compound concentration and log Kow values

223

was observed for log P values below 3, as for acetaldehyde in this study (log Kow = - 0.34).


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Finally, Figure 2C shows the evolution of the partition coefficient of acetaldehyde in the

225

model solutions simulating musts at different stages of fermentation, i.e., the solution

226

containing both glucose and ethanol. The partition coefficient of acetaldehyde strongly

227

decreased with the progress of fermentation until 40 % and then remained almost constant.

228

The behavior of the partition coefficient of acetaldehyde relative to the progress of

229

fermentation is very atypical compared to that observed for other aroma compounds, such as

230

higher alcohols (2-methylpropan-1-ol and 3-methyl butan-1-ol) and esters (ethyl acetate, 3-

231

methyl-1-butyl acetate, and 2-ethyl hexanoate).60 For these compounds, the decrease of

232

was linearly related to the fermentation progress in solutions identical to those used in this

233

study. However, for acetaldehyde, the impact of ethanol on the partition coefficient of this

234

molecule at the gas-liquid interface exceeds that of glucose.

235 236

3.1.2. Effect of temperature on the gas-liquid partition coefficients

237

The impact of temperature on the gas-liquid ratio of acetaldehyde over the range of enological

238

interest (i.e., roughly between 18 and 30 °C) was studied in buffer solutions containing either

239

glucose, ethanol or both compounds in a mixture. From previous observations, it can be

240

determined that an increase in temperature systematically induces an increase in the partition

241

coefficient of acetaldehyde. The Clausius-Clapeyron law was applied to the partition

242

coefficient ( ) change with temperature via the equation 2.61

243

− (⁄) =

244

where ∆!"#$ is the enthalpy of vaporization expressed in J mol-1, % is the ideal gas constant

245

(8.314 J mol-1 K-1), & is the temperature in Kelvin, and is the partition coefficient

246

expressed as () ⁄ () . The values of ln were plotted against the inverse of the

247

temperature (Figure 3; Van’t Hoff representation). The relationships were linear with

248

acceptable R2.60-65 The enthalpies of vaporization reflect the minimum energy required for a

∆

(2)



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switch from the liquid phase to the vapor phase and were calculated for this experiment from

250

these plots. The values obtained in the buffer solution (35.1 kJ mol-1) were between those

251

measured in the presence of glucose (40.8 kJ mol-1) or ethanol (29.4 kJ mol-1). Likely, the

252

physicochemical interactions between acetaldehyde and glucose were stronger than the

253

physicochemical interaction between acetaldehyde and ethanol. These values are consistent

254

with the acetaldehyde enthalpy of vaporization (at the boiling point), which is equal to 25.8 kJ

255

mol-1.66

256 257

3.2. Model development

258 259

The next step of this work was to develop a model predicting the evolution of the gas-liquid

260

ratio ( ) during fermentation.

261

An empirical equation was chosen to describe the complex evolution of based on the

262

composition of the liquid matrix (ethanol and glucose concentrations) and temperature. A

263

linear regression was used for the temperature effect on , whereas an exponential equation

264

was chosen for the ethanol and glucose effects (in regard to the interaction with temperature).

265

Finally, the general formula of the multiple nonlinear model adopted was:

266

= )* ∙ & + ) ∙ & ∙ exp(−) ∙ 01ℎ34567) + )8 ∙ & ∙ exp(−)9 ∙ 0:6;7) + ?

267

where )* is the slope for the temperature effect; )and )9 are the coefficients corresponding

268

to the effects of ethanol concentration ([Ethanol] in g L-1) and glucose concentration

269

([Glucose] in g L-1), respectively; ) and )8 are the coefficients corresponding to the effects

270

of the interaction between temperature and ethanol or glucose, respectively; and ε is an

271

independent N(0,σ^2) error term.

272

The model parameters were determined simultaneously using the R ver. 3.3.2 function nls

273

with the default Gauss-Newton algorithm via nonlinear weighted least squares analysis based

(3)


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on the values of obtained from all of the experiments, including those performed in buffer

275

alone. 67 Thus, all 75 measured values of were used to determine )* to )9 .68, 69 The number

276

of iteration steps needed to determine the parameters was 12. Table 2 shows the parameter

277

estimates with the corresponding estimated standard errors, t-test statistics (estimate/standard

278

error) and p values calculated using a t distribution with 70 degrees of freedom as a reference

279

distribution. The corresponding 95% t-based confidence intervals are in agreement with the

280

reported p values. The residual sum of squares was 8.3 ⋅ 10-6, and the residual standard error

281

was 3.444 ⋅ 10-4. Only parameter )8 was not significant at the threshold of 5%. The adjusted

282

R2 = 0.9810 indicated that the five variables combined predicted more than 98% of the

283

values. The bootstrap resampling technique combined with the R function nlsBoot was used

284

to estimate the standard errors together with the median and percentile confidence intervals

285

(2.5% and 97.5% percentiles of bootstrapped estimates). The data obtained are reported in

286

Table 2, indicating that the confidence intervals provided by the asymptotic method (t-based

287

confidence interval in Table 2) and bootstrap technique (Table 3) are comparable. Obtaining

288

similar results with the two different techniques highlights the robustness of the model chosen

289

to simulate the experimental data. In addition, the normality of the residual distributions and

290

homogeneity of the variance were analyzed using standard diagnostic graphs, and no violation

291

of the assumptions was detected. The mean relative error between the model prediction values

292

and experimentally measured values was calculated as follows:

293

?@ = ∑AMN

294

The average differences between the experimental and predicted values were less than 9%,

295

demonstrating that acetaldehyde gas-liquid partitioning was accurately predicted by the model

296

based on the effects of the modifications to the matrix and temperature (Figure 4).

A

IFJKLFJ

EFGHIFJ CD D

EFGHIFJ D

C

× 100%

(4)

297 298

3.3. Study of the gas-liquid partition coefficients during alcoholic fermentation 13 ACS Paragon Plus Environment


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299 300

One major parameter that must be taken into account when estimating the partition coefficient

301

of acetaldehyde during fermentation is that related to the release of CO2. Studying the

302

potential stripping effect is complex because in normal fermentation, both the rate of CO2

303

production and other factors (notably sugar and ethanol content) change during the

304

fermentation process. The issues associated with this complexity were overcome by

305

performing fermentations at a constant rate of CO2 release.50 It was therefore possible to

306

isolate the effect of CO2. By modifying the amount of assimilable nitrogen that was initially

307

present in the must, i.e., 120 mg L-1, it was possible to set up two fermentations in which the

308

rates of CO2 production were kept constant at 0.6 g CO2 L-1 h-1 at 20 °C and 0.9 g CO2 L-1 h-1

309

at 24 °C, with controlled perfusion of diammonium phosphate (2.65 g L-1 and 3.71 g L-1,

310

respectively). The rate of CO2 production was regulated between 10 and 85% of the

311

fermentation progress. Figure 1 compares the evolution of the CO2 production rate in these

312

two fermentations.

313

Table 4 provides fermentations data at constant CO2 rates, including the results of the

314

acetaldehyde measurements in the gas and liquid phases. From the mass flow meter

315

measurement of CO2 released, the residual glucose content and ethanol production during the

316

fermentation process were calculated. Then, from the model predicting the of acetaldehyde

317

(equation (3)) and the measurement of the acetaldehyde concentration in the gas phase by gas

318

chromatography (one hour after pulsing acetaldehyde in the course of fermentation), the free-

319

acetaldehyde content in the liquid phase was estimated. At the same time, sampling of the

320

liquid phase and performing measurements by an enzymatic method made it possible to

321

determine the total acetaldehyde content in the liquid phase. The total acetaldehyde content in

322

the liquid phase was calculated as the sum of the concentrations of free-acetaldehyde and

323

acetaldehyde that were mainly bound to SO2. The bisulfite combination with acetaldehyde is a


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324

stable combination in acidic media. This combination is considered to be irreversible, and the

325

very low value of the dissociation constant ( = 2.4 ⋅ 10-6 mol L-1) justifies this

326

assumption.15 Finally, the free-acetaldehyde ratio in the liquid phase was deduced by using

327

the equation (5).

328

ST>> 336>ℎU> T3V5 (%) = 0aZ# #YXZ# X[\X7 ]HJ (^ /`)

329

Because acetaldehyde combines with the SO2 produced by yeast during fermentation, the

330

partitioning coefficient cannot be directly calculated by equation (1), as was done for

331

experiments in the model solutions. Thus, an indirect validation of the model was performed.

332

Figure 5 compares the evolution of the free-acetaldehyde ratio for the two fermentations at

333

constant rates. Regardless of the fermentation rate, the free-acetaldehyde ratios are

334

comparable. This result demonstrates that CO2 stripping did not impact on the partitioning of

335

free-acetaldehyde during fermentation and that the liquid and gas phases always remained at

336

equilibrium throughout the process, in spite of the CO2 flux. Moreover, these observations

337

indicated that the model developed under ‘static’ conditions is also applicable under real

338

fermentation conditions.

339

One of the main reasons that predicting is valuable for winemaking fermentation is that it

340

allows the concentration of the free-acetaldehyde content in liquid to be calculated based on

341

measurements in the gas phase. It is therefore possible to perform full free-acetaldehyde

342

balancing during fermentation (Figure 6). The production of free-acetaldehyde at time ,

343

expressed as b(Z) in milligrams per liter of must, was calculated by adding the volatile

344

concentration in the liquid phase, expressed as (Z) in milligrams per liter of must, to the

345

amount of volatile compound lost in the gas phase, expressed as c(Z) in milligrams per liter of

346

must (equation (6)).

347

b(Z) = (Z) + c(Z)

0W@XX #YXZ# X[\X7

(^ /`)

]HJ

(5)

(6)



Page 16 of 40

348

The concentration in the liquid (Z) was determined from the concentration measured online

349

in the gas phase, expressed as (Z) in milligrams per liter of CO2, using the partition

350

coefficient ( ) value (equation (7)).

351

(Z) =

352

The losses in the gas were calculated according to equation (8).

353

c(Z) = d* (Z) × eZ ×

354

where eZ is the CO2 flow rate at time , expressed in liters of CO2 per liter of must and per

355

hour. The relative losses (%c), expressed as a percentage of production (b(Z) ), were

356

determined as follows (equation (9)).

357

%c = f

358

where XA is the final fermentation time in hours.

359

This percentage of loss is of primary importance from a technological point of view. For the

360

first time, the following parameters can be calculated: (1) the amount of free-acetaldehyde in

361

the headspace, (2) the amount of free-acetaldehyde dissolved in the liquid, (3) the percentage

362

of free-acetaldehyde lost by evaporation, (4) the total liquid content of acetaldehyde and (5)

363

the percentage of bound acetaldehyde (mainly to SO2) in the liquid phase. The ability to

364

calculate the total production of acetaldehyde and to differentiate between the amounts

365

remaining in liquid (free and bound acetaldehyde) and those lost as free-acetaldehyde in CO2

366

are of primary interest for improving our understanding of yeast metabolism and optimizing

367

fermentation control. From a microbiological point of view, the total amount produced needs

368

to be considered, whereas from a technological point of view, the concentration of free-

369

acetaldehyde remaining in wine is more important because it imparts undesired aromatic

370

attributes, while bound acetaldehyde does not. In even broader terms, this comprehensive

371

study opens horizons for future studies towards reaching a better understanding of the

(L)

(7)

Z

`(L) (L)

=

(8)

L

di FhJ (L) ×g(L) ×Z L

(LFhJ) jdi FhJ (L) ×g(L) ×Z

(9)


Page 17 of 40


372

mechanisms involved in the synthesis of free and bound acetaldehyde during alcoholic

373

fermentation.



374

Abbreviations used

375

concentration of a species in the liquid phase (mol m-3 or g m-3)

376

concentration of a species in the gas phase (mol m-3 or g m-3)

377

∆!"#$

enthalpy of vaporization ( J mol-1)

378

V

pure component

379

partition coefficient: gas concentration / liquid concentration (dimensionless)

380

c(Z)

losses in the gas phase at time (mg L-1 of must)

381

eZ

CO2 flow rate at time (L L-1 of must h-1)

382

b(Z)

production at time (mg L-1 of must)

383

%

gas constant (=8.314 J mol-1 K-1)

384

%c

relative losses (% of b(Z) )

385

&

temperature (°C or K)

Page 18 of 40


Page 19 of 40

386


Acknowledgments

387 388

The authors gratefully thank Isabelle Sanchez (from INRA, UMR 0729 Mistea, F-34060

389

Montpellier Cedex 2, France) for technical support in statistical analysis of the model.



390

References

391

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392 393 394 395

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396

determination of ethanol and acetaldehyde in wines by flow injection analysis and

397

immobilized enzymes. Anal. Chem. 1987, 59, 1859 – 1863.

398 399 400 401

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[9] Benito, Á., Calderón, F., & Benito, S. The combined use of schizosaccharomyces pombe

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[16] Hood, A. Inhibition of growth of wine lactic-acid bacteria by acetaldehyde-bound sulphur dioxide. Aust. Grapegrow Winemaker, 1983, 232, 34 – 43. [17] Main, G. L., Morris J.R. Color of riesling and vidal wines as affected by bentonite,

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World J. Microbiol. Biotechnol. 2003, 19, 311 – 315.

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potential of 26 enological Saccharomyces and non-Saccharomyces yeast strains in a

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resting cell model system. J. Ind. Microbiol. Biotechnol. 2011, 38, 1391 – 1398.

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[38] Liu, S. Q., Pilone, G. J. An overview of formation and roles of acetaldehyde in

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winemaking with emphasis on microbiological implications. Int. J. Food Sci. Technol.

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dioxide-bound acetaldehydeby malolactic lactic acid bacteria in white wine. J. Appl.

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Microbiol. 2006, 101, 474 – 479.

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[42] Jackowetz, J.N., Dierschke, S. Mira de Orduna, R. Multifactorail analysis of

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acetaldehyde kinetics during alcoholic fermentation by Saccharomyces cerevisae. Food

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Res. Int. 2011, 44, 310 – 316.

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[43] Ough, C.S., Amerine, M.A. Studies on aldehyde cultures of non-film-forming species of Saccharomyces. Appl. Microbiol. 1958, 9, 316 – 319. [44] Lide, D.R., Frederikse, H.P.R. CRC Handbook of Chemistry and Physics, 76th Edition;

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Lide, D.R., Frederikse H. P. R., Eds.; CRC Press, Inc. Publisher: Boca Raton, Florida,

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USA, 1995.

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[45] Buttery, R.G., Ling, L.C., Guadagni, D.G. Volatilities aldehydes, ketones, and esters in dilute water solution. J. Agric. Food. Chem. 1969, 17, 385 – 389. [46] Bely, M., Sablayrolles, J.M., Barre, P. Automatic detection of assimilable nitrogen

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deficiencies during alcoholic fermentation in oenological condition. J. Ferment. Bioeng.

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1990, 70, 246 – 252.

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[47] Beulter, H.O. Acetaldehyde in methods of enzymatic analysis. Bergmeyer, H.U., Ed.; VCH Publishers: Cambridge, UK, 1988, 6. [48] Morakul, S., Mouret, J.R., Nicolle, P., Trelea, I.C., Sablayrolles, J.M., Athes, V.

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Modelling of the gas–liquid partitioning of aroma compounds during wine alcoholic

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fermentation and prediction of aroma losses. Process Biochem. 2011, 46, 1125 – 1131.


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[49] Mouret, J.R., Morakul S., Nicolle P., Athes V., Sablayrolles J. M. Gas-liquid transfer of

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aroma compounds during winemaking fermentations. J. Food Sci. Technol. 2012, 49, 238

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– 244.

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[50] Manginot, C., Sablayrolles, J. M., Roustan, J. L., Barre, P. Use of constant rate alcoholic

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fermentations to compare the effectiveness of different nitrogen sources added during the

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stationary phase. Enzyme Microb. Technol. 1997, 20, 373 – 380.

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[51] Hine, J., Mookerjee, P. K. Structural effects on rates and equilibriums. XIX. Intrinsic

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hydrophilic character of organic compounds. Correlations in terms of structural

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contributions. J. Org. Chem. 1975, 40(3), 292 – 298.

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[52] Meylan, W. M., Howard, P. H. Bond contribution method for estimating Henry’s law constants. Environ. Toxicol. Chem. 1991, 10, 1283 – 1293. [53] Nahon, D. F., Koren, P. A. N. Y., Roozen, J. P., Posthumus, M. A. Flavor release from

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mixtures of sodium cyclamate, sucrose, and an orange aroma. J. Agric. Food. Chem.

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1998, 46, 4963 – 4968.

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[54] Athes, V., Paricaud, P., Ellaite, M., Souchon, I., Fürst, W. Vapour-liquid equilibria of

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aroma compounds in hydroalcoholic solutions: Measurements with a recirculation

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method and modelling with the NRTL and COSMO-SAC approaches. Fluid Phase

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Equilib. 2008, 265, 139 – 154.

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[55] Conner, J. M., Birkmyre, L., Paterson, A., Piggott, J. R. Headspace concentrations of ethyl esters at different alcoholic strengths. J. Sci. Food Agric. 1998, 77, 121 – 126. [56] Aznar, M., Tsachaki, M., Linforth, R. S. T., Ferreira, V., Taylor, A.J. Headspace analysis

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of volatile organic compounds from ethanolic systems by direct APCI-MS. Int. J. Mass

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Spectrom. 2004, 239, 17 – 25.

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[57] Conner, J. M., Paterson, A., Piggott, J. R. Interactions between ethyl esters and aroma

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compounds in model spirit solutions. J. Agric. Food Chem. 1994, 42, 2231 – 2234.



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[58] Escalona, H., Piggott, J. R., Conner, J. M., Paterson, A. Effect of ethanol strength on the volatility of higher alcohols and aldehydes. Ital. J. Food Sci. 1999, 11, 241 – 248. [59] Tsachaki, M., Linforth, R. S. T., Taylor, A. J. «Dynamic headspace analysis of the

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release of volatile organic compounds from ethanolic systems by directAPCI-MS. J.

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Agric. Food Chem. 2005, 53, 8328 – 8333.

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[60] Morakul, S., Athes, V., Mouret, J.R., Sablayrolles, J.M. Comprehensive study of the

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evolution of gas-liquid partitioning of aroma compounds during wine alcoholic

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fermentation. J. Agric. Food Chem. 2010, 58, 10219 – 10225.

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[61] Meynier, A., Garillon, A., Lethuaut, L., Genot, C. Partition of five aroma compounds

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between air and skim milk, anhydrous milk fat or full-fat cream. Lait. 2003, 83, 223 –

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235.

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[62] Tsachaki, M., Gady, A.L., Kalopesas, M., Linforth, R. S. T., Athes, V., Marin, M.,

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Taylor, A. J. Effect of ethanol, temperature, and gas flow rate on volatile release from

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aqueous solutions under dynamic headspace dilution conditions. J. Agric. Food Chem.

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2008, 56, 5308-5315.

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[63] Seuvre, A. M., Turci, C., Voilley, A. Effect of the temperature on the release of aroma

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compounds and on the rheological behaviour of model dairy custard. Food Chem. 2008,

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108, 1176 – 1182.

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[64] Savary, G., Guichard, E., Doublier, J.L., Cayot, N. Mixture of aroma compounds:

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2006, 39, 372 – 379.

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[65] Jouquand, C., Ducruet, V., Giampaoli, P. Partition coefficients of aroma compounds in

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polysaccharide solutions by the phase ratio variation method. Food Chem. 2004, 85, 46 –

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474.


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561 562 563 564 565 566 567


[66] Dean, J.A., Lange, N.A. Lange's Handbook of Chemistry, 15th Edition; Dean, J.A., Ed.; McGraw-Hill, Inc. Publisher: New-York, USA, 1999. [67] R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, https://www.R-project.org/, 2016. [68] Bates, D.M., Watts, D.G. Nonlinear regression analysis and its applications; Bates, D.M., Watts, D.G., Eds.; Wiley Publisher: New York, Chichester, USA, 1988. [69] Bates, D.M., Chambers, J.M. Nonlinear models. Chapter 10 of Statistical Models in S;

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Chambers, J.M., Hastie, T.J., Eds.; Wadsworth & Brooks/Cole Publisher: Mionterey,

569

California, USA, 1992.



570

Figure captions

571

Figure 1. Constant rate fermentation at 0.6 (

572

CO2 production rate throughout fermentation.

) and 0.9 (

Page 28 of 40

) g L-1 h-1 : evolution of the

573 574

Figure 2. Evolution of the partition coefficient of for acetaldehyde in model solutions of

575

buffer supplemented with glucose (A) and ethanol (B) and in model solutions simulating

576

musts at different stages of fermentation (C) for three temperatures (18 , 24 and 30

577

°C).

578 579

Figure 3. The ln of the partition coefficient ( ) as a function of 1000/T (K-1) in different

580

media: 22% (w/v) glucose , 13% (v/v) ethanol and buffer solutions.

581 582

Figure 4. Comparison of the predicted and measured values for acetaldehyde in model

583

solutions of buffer supplemented with glucose (A) and ethanol (B) and in model solutions

584

simulating musts at different stages of fermentation (C) for three temperatures (18 , 24

585

and 30 °C).

586 587

Figure 5. Evolution of the free-acetaldehyde ratio throughout fermentation for the two

588

fermentations at constant rates, 0.6 g L-1 h-1 at 20 °C and 0.9 g L-1 h-1 at 24 °C.

589


Page 29 of 40


and calculated liquid phases, total

590

Figure 6. Evolution of free-acetaldehyde (gas*

591

production and losses ) and total acetaldehyde*

592

throughout fermentation. *data resulting from measurements

(free and bound in the liquid phase)



Page 30 of 40

Table 1. Gas-liquid partition coefficients (×103; concentration ratio) of acetaldehyde in buffer, synthetic must and synthetic wine solutions at 18, 24 and 30 °C.

Model solution

18 °C

24 °C

30 °C

Buffer

3.20 ± 0.04

4.68 ± 0.19

5.67 ± 0.07

Glucose 22 %, ethanol 0 %

3.94 ± 0.06

5.36 ± 0.05

7.67 ± 0.29

Glucose 0 %, ethanol 13 %

0.97 ± 0.03

1.32 ± 0.06

1.88 ± 0.07

These data are consistent (within the same order of magnitude) with the literature values and estimated data. At 25°C in water, the of acetaldehyde is equal to (×103): 2.77 (values calculated from the bond contribution, EPIWEB 4.0 software developed by the U.S. EPA Office of Pollution Prevention Toxics and Syracuse Research Corporation), 2.45 (value calculated from a group contribution method, EPIWEB 4.0 software) and 2.69.45


Page 31 of 40


Table 2. Estimated coefficients of the model parameters identiﬁed from equation (3) by a nonlinear method using a Gauss-Newton algorithm and significance testing

t-based confidence interval

Coefficients

Standard error

t value

Pr(>||t||)

kl

6.186 · 10-5

3.490 · 10-6

17.728

km

1.214 · 10-4

4.785 · 10-6

kn

1.079 · 10-1

ko kp

2.5%

97.5%

< 2 · 10-16

5.490 · 10-5

6.882 · 10-5

25.373

< 2 · 10-16

1.119 · 10-4

1.310 · 10-4

1.168 · 10-2

9.241

9.57 · 1014

8.462 · 10-2

1.312 · 10-1

3.813 · 10-9

8.691 · 10-9

0.439

0.66218

-1.352 · 10-8

2.115 · 10-8

-4.306 · 10-2

1.048 · 10-2

-4.111

1.06 · 10-4

-6.396 · 10-2

-2.217 · 10-2



Page 32 of 40

Table 3. Estimated coefficients, standard errors, medians and percentile confidence intervals (2.5% and 97.5% percentiles of bootstrapped estimates)

kl km kn ko kp

Coefficients

Standard error

Median

2.5%

97.5%

6.169 · 10-5

3.372 · 10-6

6.167 · 10-5

5.500 · 10-5

6.816 · 10-5

1.215 · 10-4

4.714 · 10-6

1.215 · 10-4

1.127 · 10-4

1.309 · 10-4

1.085 · 10-1

1.157 · 10-2

1.076 · 10-1

8.739 · 10-2

1.331 · 10-1

1.859 · 10-8

6.508 · 10-8

3.735 · 10-9

2.619 · 10-11

1.122 · 10-7

-4.424 · 10-2

1.013 · 10-2

-4.303 · 10-2

-6.574 · 10-2

-2.714 · 10-2


Page 33 of 40


Table 4. Data for acetaldehyde (A.) in fermentations at constant rates Produced CO2 (g L-1)

Residual sugars (g L-1)

Ethanol (g L-1)

[A.]gas (µg L-1)

[Free A.]liquid* (mg L-1)

[Total A.]liquid (mg L-1)

Free A. ratio (%)

Fermentation at constant rate 0.6 gCO2 L-1 h-1 – 20 °C 9.9

158.5

10.1

123

58

156

37

20

136.6

20.4

148

97

183

53

27.3

120.8

27.8

134

98

168

58

31.9

110.8

32.5

121

92

169

54

42.7

87.3

43.5

120

95

185

51

45.7

80.8

46.6

136

108

226

48

62.9

43.5

64.2

89

72

166

43

-1

-1

Fermentation at constant rate 0.9 gCO2 L h – 24 °C 9.3

159.8

9.5

146

56

147

38

23.2

129.7

23.7

164

94

172

55

25.1

125.5

25.6

75

44

81

55

39.2

94.9

40.0

140

92

173

53

48.4

75.0

49.4

129

86

165

52

58.0

54.1

59.2

118

79

165

48

71.8

24.2

73.2

108

73

162

45

*: estimated from equation (3)



Page 34 of 40

Figure 1.


Page 35 of 40


Figure 2.

A

B

C



Page 36 of 40

Figure 3.


Page 37 of 40


Figure 4.

A

B

C



Page 38 of 40

Figure 5.


Page 39 of 40


Figure 6.



Page 40 of 40

Graphic for table of contents.


Comprehensive Study of the Evolution of the GasâLiquid Partitioning

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