# Mapping gaseous ethanol in the headspace of wine glasses with an interband cascade laser sensor

Florian Lecasse, Raphaël Vallon, Clément Jacquemin, Vincent Alfonso, Clara

Cilindre, Bertrand Parvitte, Virginie Zéninari, Gérard Liger-Belair

► To cite this version:

Florian Lecasse, Raphaël Vallon, Clément Jacquemin, Vincent Alfonso, Clara Cilindre, et al.. Mapping gaseous ethanol in the headspace of wine glasses with an interband cascade laser sensor. Sensors and Actuators B: Chemical, 2025, 443, pp.138216. 10.1016/j.snb.2025.138216. hal-05223733

HAL Id: hal-05223733

https://univ-reims.hal.science/hal-05223733v1

Submitted on 26 Aug 2025

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# Mapping gaseous ethanol in the headspace of wine glasses with an interband cascade laser sensor

Florian Lecasse<sup>a, ID,*</sup>, Raphaël Vallon<sup>a ID</sup>, Clément Jacquemin<sup>b, a ID</sup>, Vincent Alfonso<sup>a ID</sup>,
Clara Cilindre<sup>a ID</sup>, Bertrand Parvitte<sup>a ID</sup>, Virginie Zéninari<sup>a ID</sup>, Gérard Liger-Belair<sup>a ID,*</sup>

## ARTICLE INFO

Keywords:

Gaseous ethanol

Champagne

Interband cascade laser

Spectroscopy

## ABSTRACT

Under standard wine tasting conditions, volatile organic compounds responsible for the wine's bouquet progressively invade the chemical space perceived by the consumer in the glass headspace. Thanks to the recent interband cascade laser technology, a sensor dedicated to monitor gaseous ethanol in the headspace of wine glasses was built and validated. Accordingly, the mapping of the mixing ratio of gaseous ethanol was performed, in space and time, in the headspace of a champagne glass. From the start of serving champagne in the glass, and during the several minutes following, a kind of temperature-dependent ethanol footprint was revealed in the glass headspace, which was discussed based on the vapor-liquid equilibrium combined with a purely diffusive 3D-model.

## 1. Introduction

The French microbiologist Louis Pasteur (1822–1895), a key figure in wine science, contributed to the understanding of alcoholic fermentation, and proposed a process (known as pasteurization) which helped winemakers and brewers to considerably improve the quality of their yeast-fermented beverages [1]. Ethanol (EtOH) and carbon dioxide ($CO_2$) are the main organic byproducts of alcoholic fermentation, the chemical reaction initiated by yeasts that break down fermentable sugars in grape musts to progressively transform them in wines. Wines are therefore complex mixtures of water, and ethanol (the principal volatile organic compounds), with hundreds of others volatile organic compounds (VOCs) showing more or less volatility [2]. In addition, champagne and other traditional method sparkling wines contain $10–12 g L^{-1}$ of dissolved $CO_2$ following a second in-bottle alcoholic fermentation in a sealed bottle [3].

Under standard tasting conditions, with usually 7%–15% ethanol by volume in wines, gaseous ethanol is undoubtedly the most abundant VOC in the headspace of a wine glass, after gaseous $CO_2$ which has already been shown to be the predominant species present in the headspace of a glass throughout the tasting of a sparkling wine [4–9]. The VOCs are the main determinants of wine aroma performance, which relate to particular attributes that characterize the overall sensory profile [10]. Among them, ethanol has long been shown to be crucial in the stability, aging and sensory properties of wines [11].

Indeed, ethanol gas plays a multimodal role in the perception of the wine bouquet via the orthonasal route. Firstly, ethanol gas alters the perception of certain aromatic molecules, particularly those responsible for fruity aromas [12–14]. Moreover, as a function of its abundance in the headspace of a wine glass, inhaling high concentrations of gaseous ethanol can induce an unpleasant tingling/burning sensation after triggering the human trigeminal system [12,15]. The fine monitoring of gaseous ethanol in the headspace of wine glasses is therefore crucial to better understand the neuro-physicochemical mechanisms responsible for aroma release and flavor perception throughout wine tasting.

The concentration of gaseous ethanol was monitored for the first time in the headspace of champagne glasses by micro-gas chromatography (µGC) coupled with a thermal conductivity detector, but with a very low time-resolution (about 1 min, corresponding to 0.02 Hz), and at a single point in the headspace of the glass [4,5]. As described by Wollan et al. [16] more recently, the evaporation rate of ethanol from standard commercial still wines was found to be strongly influenced by glass shape and by the volume of the glass headspace in particular. A decade ago, a sniffer-camera for visualizing ethanol vapors above a wine glass has been developed by using chemiluminescence from an enzyme-immobilized mesh [17]. Ethanol vapors have also been monitored through infrared thermal imaging with a specific bandpass filter for the $1000–1100 cm^{-1}$ wavenumber range, in order to discriminate fresh grapes from decayed grapes [18]. This novel, rapid and

<footnote><sup>a</sup> Université de Reims Champagne-Ardenne, CNRS, GSMA, Reims, France</footnote>

<footnote><sup>b</sup> mirSense, 1 Rue Jean Rostand, Campus Eiffel, 91400, Orsay, France</footnote>

<footnote>* Corresponding authors.
E-mail addresses: florian.lecasse@univ-reims.fr (F. Lecasse), gerard.liger-belair@univ-reims.fr (G. Liger-Belair).</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Overlap of the transmission through a standard soda-lime-silica glass (with a wall thickness of 1 mm) and experimental cross-section of ethanol (at 20 °C and 1 atm) measured by Sharpe et al. [26] (a); Zoom of the ethanol transitions around 3000 cm⁻¹ with a highlight of the wavenumber range scan by the ICL (b).</figcaption></figure>

nonintrusive technique nevertheless only allows the semi-quantitative analysis for vapors from ethanol solutions ranging from 10 to 70%.

In a broader sense, metal oxide semiconductors can be used for ethanol vapor detection. However, depending on the nature of the materials, this often requires operation at high temperatures to achieve optimal sensitivity [19–23]. Moreover, the long-term stability of these materials is not guaranteed, necessitating frequent calibration. The response time of these sensors ranges from a few seconds to several minutes [21–24]. These timescales are still too slow to track the dynamic evolution of the gas cloud (including gaseous ethanol) in the headspace of a glass subjected to orbital agitation [8]. Given the need to observe rapid concentration variations, ethanol detection by molecularly imprinted polymers is also unsuitable [25].

Optical gas sensing is one of the fastest developing research areas of laser spectroscopy, particularly for the non-intrusive and non-invasive detection of greenhouse gases at the heart of environmental issues [27–29]. In particular, Tunable Diode Laser Absorption Spectroscopy (TDLAS) is a high acquisition frequency method, with a high reproducibility and, above all, requiring no calibration [30]. For all these reasons, a CO₂-Diode Laser Sensor (called the CO₂-DLS) for high-frequency gaseous CO₂ measurements in the headspace of champagne glasses was developed around fifteen years ago, based on the Tunable Diode Laser Absorption Spectroscopy (TDLAS) [31]. Since its conception, this spectrometer has undergone several successive developments, including the possibility of mapping gaseous CO₂, in the headspace of champagne glasses [32] and more recently to study the influence of wine swirling on the dynamics of gaseous CO₂ above champagne glasses [8].

The present article addresses the very last major upgrade of the CO₂-DLS. By integrating an Interband Cascade Laser (ICL), a new sensor dedicated to the precise monitoring of gaseous ethanol was built and rigorously validated. Accordingly, by allowing the measurement of low mixing ratios of gaseous ethanol (<1%) compatible with those inhaled during wine tasting [4,5], this sensor proves to be a valuable tool for better understanding the sensory analysis of wines (whether still or sparkling). Here, this newly developed sensor allowed to perform, in space and time, the mapping of the mixing ratios of gaseous ethanol in the headspace of a champagne glass. From the start of the pouring stage and during the several minutes following, a kind of temperature-dependent ethanol footprint was revealed in the glass headspace, which was discussed based on the vapor-liquid equilibrium combined with a purely diffusive 3D-model.

## 2. Materials and methods

### 2.1. An Interband Cascade Laser (ICL) sensor to monitor gaseous ethanol

The ethanol molecule has strong ro-vibrational transitions in the mid-infrared region (roughly 2–10 µm). However, the standard soda-lime silica glasses are known to absorb a large amount of infrared radiations. In Fig. 1a the overlapping of the glass material transmission is presented, with the ethanol transitions along infrared wavenumbers. Below 2200 cm⁻¹ infrared radiations are entirely absorbed by the glass. Therefore, to monitor ethanol concentration through the glass headspace by TDLAS, the laser source has to be above this threshold of 2200 cm⁻¹. Based on the remaining ethanol absorption, an interband cascade laser (ICL) is the most suitable source for this application [30]. Thus, a continuous wave ICL from nanoplus GmbH (Germany) was used to scan the ethanol absorption centered at 2989.5 cm⁻¹ as shown in Fig. 1b.

The ICL was included on the optical breadboard of the CO₂-DLS, a diode laser sensor developed to monitor gas-phase CO₂ in the headspace of champagne glasses [8,32], as presented in the Fig. 2. A total of three lasers are mounted on this optical breadboard for only one photodiode. In order to set apart each light signal, a galvanometric mirror (Thorlabs GVS001) is used to direct one laser beam at the time through the glass headspace, as already described by Lecasse et al. [8]. The galvanometric mirror quasi-instantly provides a common path for the three lasers. The common path follows a classic TDLAS setup with a pellicle beamsplitter (45/55) splitting the laser beam in two distinct paths: the reference path operated with a one-inch uncoated germanium Fabry-Pérot (FP) etalon (Laser Component L5940) and a gas-sensing path where the laser beam is conducted by an APC (Angle Physical Contact)/FC (Ferrule Connector) flurozirconium single mode optical fiber. The optical fiber guides the laser beams to an optical setup displayed in Fig. 2c. A pair of galvanometric mirrors (6210H Cambridge Technology, USA), located at the focus point of an off-axis parabolic mirror, is used to adjust the optical path of the laser beam through the glass headspace along (x, y) coordinates [7,32]. Symmetrically, the same optical elements are used to balance the laser beam translation in order to reach a single element cryogenic InAs photodiode (for more information on the scanning setup, see the article by Moriaux et al. [32]).
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Overview of the experimental spectrometer dedicated to monitor gaseous ethanol in the headspace of wine glasses; Optical breadboard with the laser source part (a), Top view of the spectrometer with the corresponding legends: ① Laser @ 2.004 µm; ② Laser @ 2.682 µm; ③ Interband Cascade Laser @ 3.345 µm; ④ Galvanometric mirror; ⑤ Pellicle beam splitter; ⑥ Germanium Fabry-Pérot; ⑦ InAs photodiode; and ⑧ Fiber coupler (b). The three laser (denoted ①, ② and ③) beams follow a common path, represented by the red beam, to an optical fiber used to guide the beams in the headspace of the glass for multipoint network monitoring (c) with the corresponding legends: ① First pair of galvanometric mirror; ② First off-axis parabolic mirror; ③ Second off-axis parabolic mirror; ④ Second pair of galvanometric mirror; ⑤ Cryogenic InAs photodiode.</figcaption></figure>

## 2.2. Instrument control and data processing

A Thorlabs PRO8000 chassis equipped with TED8040 and LDC8010 control boards was used for the ICL temperature control and current control, respectively. A National Instrument Ni-PXI-7852R FPGA (Field-Programmable Gate Array) was used to apply an analogic sawtooth-shape current modulation, up to 400 Hz with three lasers, to the LDC8010 card to tune the wavenumber emission of the laser. The 400 Hz modulation frequency was estimated based on the full bandwidth of the galvanometric mirror operating with a square-wave signal, and considering the mechanical angle between the three laser beams. In order to scan the fraction of the ethanol absorption band, as illustrated in Fig. 1b, the ICL was set to a temperature of 45 °C and the current modulation goes from 20 mA (under the laser emission threshold ≈32 mA) to 90 mA. The raw data recorded from the Peltier-cooled InAs photodiode of the reference path and the cryogenic InAs photodiode of the gas-sensing path are presented in Fig. 3. Part A of the modulation occurs below the laser emission threshold; therefore, this part was used to remove the photodiode signal offset. Part B takes place shortly after laser emission begins, but the galvanometric mirror was not yet stable, so this part was not used. Part C corresponds to the signal used to determine the light intensity passing through the glass headspace. Finally, part D represents the data recorded while the galvanometric mirror was switching to another position, and this part was also not used. The laboratory LabVIEW software, developed to interface the two lasers of the CO₂-DLS, was adapted to drive the Thorlabs PRO8000 chassis, and thus the laser for ethanol sensing.

Because the ethanol spectrometer was part of the CO₂-DLS, acquisition of data and the procedure to process the data from the ethanol laser are exactly the same as the ones from the CO₂ lasers (see the article by Lecasse et al. [8] for more information). The recorded signal from the laser beam passing through the FP etalon was used to obtain the relative emission wavenumber of the laser during time-current modulation [8]. The signal recorded from the sampling way, through

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Example of raw data recorded by the ICL in one modulation time period. The orange line represents the Airy function of the tuned ICL passing through the one-inch germanium Fabry-Pérot etalon; The red and blue lines correspond to the baseline and ethanol absorption, respectively. The ethanol absorption on the blue line is the result of 0.95% of gaseous ethanol along a 5.6 cm optical path.</figcaption></figure>

an empty glass, was used as a baseline ($I_0$). Depending on the glass shape, the laser beam refraction slightly varies with the spatial location of the spot. Therefore, a baseline was recorded for each pixel of the 2D matrix. While champagne was poured into the glass, the signal becomes the transmitted light intensity ($I$). Ethanol is a molecule with low fundamental vibrational modes of vibration. The results is a dense absorption spectra with overlapping bands. Therefore, there is no specific line profile for ethanol, unlike the carbon dioxide molecule. Its absorption is defined by experimental cross-section data obtained
<figure><img src="image_7.png" /><figcaption>Fig. 4. Fit (blue line) of an experimental transmission spectrum (red dots) as recorded by the ICL sensor; The black dots are the difference between the experimental data and the fit.</figcaption></figure>

by Sharpe et al. [26], as seen in Fig. 1a. Fortunately, the experimental conditions of 760 Torr and 298.15 K are similar for the monitoring of ethanol above alcoholic beverages. The transmission spectra (I/I₀) follows the well-known Beer–Lambert law, as defined hereafter:

$$ \frac{I}{I_0}(\sigma, T, P) = A \exp(-C_{EIOH} L N(T, P) \sigma_{eff}(\sigma, T, P)) \quad (1) $$

with σ being the wavenumber (in cm⁻¹), T being the temperature (in K), P being the pressure (in Pa), A being the shift coefficient, Cₐₐᵢₐ₄ being the ethanol mixing ratio, L being the optical path length (in cm), N(T, P) being the molecule density (in molecule cm⁻³) retrieved with the ideal gas law and σₐₐᵢₐ₄(σ, T, P) being the ethanol cross section (in cm² molecule⁻¹).

To retrieve the gaseous ethanol concentration from a recorded transmission spectrum, a Python routine was developed to fit the experimental transmission spectrum with the latter theoretical Beer–Lambert law defined in Eq. (1). However, the recorded data are oversampled along the wavenumber scale, compared to the cross-section data. Therefore, the cross-section data were linearly interpolated to become a continuous function and match the wavenumber resolution of the recorded spectra. Thus, a least-squared method can be applied between Beer–Lambert equation and experimental data. The spectral range scanned by the laser is located around a local absorption maximum for ethanol, as seen in Fig. 1b. Consequently, all the light transmitted by this laser through a sample containing gaseous ethanol is partially absorbed. Baseline determination must then be meticulously recorded, or an amplitude shift on the transmission may occur. However, commercial glasses are inhomogeneous, and small perturbations, which may occur during the pouring process, can cause signal variations from the previously recorded baseline. To prevent these signal variations from being interpreted as concentration changes, a linear correction factor, denoted A, was introduced in the Beer–Lambert equation. The Python routine adjusts three parameters, the ethanol mixing ratio, the maximum of absorption on a relative scale (to obtain an absolute wavenumber scale) and the A factor to clear the transmission shift. The fit of an experimental transmission spectra is presented in Fig. 4. The Python routine also enables the averaging of multiple spectra to improve the signal-to-noise ratio, thereby making data fitting easier.

## 2.3. Validation and detection limit

Two standard gas bottles certified by Air Liquide (Paris, France) were used in order to validate the ethanol spectrometer. The ethanol

<figure><img src="image_8.png" /><figcaption>Fig. 5. Mean value of the mixing ratio of gaseous ethanol (in ppm) measured with the ICL sensor in the two standard gas bottles provided by Air Liquide and their dilution with dry air.</figcaption></figure>

concentrations in the bottles are 1000, and 2000 ppm, respectively, with nitrogen as diluent. The high concentration value corresponds to the maximum mixing ratio that Air Liquide can supply in compliance with the ISO 9001 quality standard. To achieve sufficient light absorption, a gas cell with an optical path length of 50 cm was used. This optical path length perfectly matches the light absorption of 1% ethanol in a 10 cm diameter glass. This concentration was chosen as a reference because it was observed by Cilindre et al. in the headspace of a flute using micro gas chromatography [4]. In order to increase the number of sample, the standard gas bottle were diluted with dry air by using two thermal mass flow controller (Brooks Instrument SLA 5850). Fitting measured concentrations to standard concentrations gives an affine function with a slope close to unity and an excellent coefficient of determination (see Fig. 5). The deviation of 2% observed on the slope is less than the precision error of the standard bottles, and the diluted samples, which are 5% and 8% respectively. The ethanol spectrometer and data processing method are therefore validated for retrieving gaseous ethanol concentrations with a great accuracy (see Fig. 5).

The detection limit of the ethanol spectrometer (i.e., a concentration value that is 95% reliable in relation to the background noise) evolves as a function of the signal-to-noise ratio [33,34]. One method of improving the signal quality is to average several spectra to reduce the white noise introduced throughout the detection chain. Since a spectrum is obtained from a modulation of current over time, the number of spectra averaged is related to an integration time. However, the signal acquired by the spectrometer is not only composed of white noise, 1/fᵃ components, such as pink noise or brown noise, are also present [34]. Consequently, beyond an optimal integration time, the spectrometer's detection limit is no longer improved and may even deteriorate. To determine the detection limit of the ethanol spectrometer, the same glass as the one used in the present study (with 5.6 cm of optical path length) was filled with a gas standard containing 2000 ppm ethanol. The spectra were then processed with different integration times. The Allan deviation was then calculated on the different concentration values obtained (see Fig. 6) [33,34].

The results obtained in Fig. 6 show that beyond an integration time of about 1 s, the detection limit no longer decreases significantly. There is a break with the theoretical decay of white noise, represented by the plain blue line in Fig. 6. Thus, above an integration time of about 1 s, the monitoring of gaseous ethanol in the glass headspace has a poor time resolution and crucial information or phenomena could be missed.
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Allan's standard deviation as a function of integration time; The straight line with slope $t^{-1/2}$ represents the behavior of a signal dominated by white noise.</figcaption></figure>

From these data, the minimum measurable ethanol concentration is determined as a function of both the optical path length and integration time (up to 1 s). This minimum mixing ratio $\chi_{EiOH}^{min}$ is given by the following relationship:

$$ \chi_{EiOH}^{min} = \frac{\sigma'}{L\sqrt{\tau_i}} \qquad (2) $$

with L being the optical path length (cm), $\tau_i$ being the integration time (s) and $\sigma'$ being the limit detection coefficient (1165 ppm cm $s^{1/2}$). The value of the last parameters is given by the product of the slope of the plain blue line in Fig. 6, with a statistical coefficient (3.3 for a confidence interval of 95% according to the International vocabulary of metrology [35]) and the length of the gas cell (5.6 cm).

## 2.4. Champagne samples and glass shape

A batch of standard commercial Champagne wine (Henri de Verlaine, Marne, France), brut-labeled, with 12.5% ethanol by volume, and elaborated in standard 75 cL bottles, was used for this set of experiments. Bottles were classically elaborated with $24 \text{ g L}^{-1}$ of saccharose to launch the second in-bottle alcoholic fermentation. Based on the procedure described by Caputi et al. [36], the method officially recommended by the International Organisation of Vine and Wine (labeled OIV-MA-AS314-01) was used to precisely measure the concentration of dissolved CO$_2$ in two bottles. A concentration of $10.9 \pm 0.2 \text{ g L}^{-1}$ of dissolved CO$_2$ was found. At least 24 h before the experiments with the spectrometer, a first batch of bottles was stored in a thermoregulated wine cellar (at $12 \pm 1 \text{ °C}$), while another batch was vertically immersed (under the cork) in a water bath regulated at $20 \pm 1 \text{ °C}$.

The standard and so-called INAO tasting glass (Lehmann Glass, Marne, France) with a total volume capacity of 21 cL was used for this set of experiments. This glass, presented in Fig. 7, is commonly used worldwide by wine tasters and sommeliers, and has been certified for wine sensory analysis by the Institut National des Appellations d'Origines (INAO)/ISO 3591:1977. To trigger a standardized effervescence identical from one glass to another, all glasses were laser-etched on their bottom with a ring-shaped structure done with 20 laser beam points of impact, as described in detail by Liger-Belair et al. [37] Such laser-etched glasses are usually easily recognizable, with a central bubbly flow ascending along their axis of symmetry. Before each set of experiments, the glasses were thoroughly washed with an acetic acid solution (10% vol/vol), rinsed using distilled water, and then dried in a drying oven at 60 °C. Such a protocol was necessary to remove surface impurities (such as cellulose fibers or tartaric salt crystals) that could

<table><caption>**Table 1**
Summary of the optical path and subsequent detection limit for each pixel of the 2D multipoint network in the glass headspace.</caption><thead><tr><th colspan="6">INAO (21 cL)</th></tr><tr><th>Pixel</th><th>Pathlength (cm)</th><th>C<sup>min</sup><sub>EiOH</sub> (ppm)</th><th>Pixel</th><th>Pathlength (cm)</th><th>C<sup>min</sup><sub>EiOH</sub> (ppm)</th></tr></thead><tbody><tr><td>0 & 8</td><td>3.6</td><td>1483</td><td>4</td><td>5.9</td><td>905</td></tr><tr><td>1 & 9</td><td>4.2</td><td>1271</td><td>5</td><td>5.6</td><td>953</td></tr><tr><td>2 & 10</td><td>4.7</td><td>1136</td><td>6</td><td>5.2</td><td>1027</td></tr><tr><td>3 & 11</td><td>5.1</td><td>1047</td><td>7</td><td>4.7</td><td>1136</td></tr></tbody></table>

trigger heterogeneous nucleation of CO$_2$ bubbles elsewhere than from the laser beam impact points [37].

## 2.5. The multipoint mapping network in the headspace of glasses

Real-time mapping of gas-phase ethanol was achieved along a well-defined two-dimensional (2D) multipoint network in the headspace of the glass, as presented in the sketch displayed in Fig. 7. The 2D multipoint network structures the headspace of the INAO glass (filled with 10 cL of champagne) into three columns and into four rows. The central column corresponding to the axis of symmetry of the glass is structured with four vertically arranged dots separated from 1 cm from one another. The other two columns of 4 vertically aligned points are located symmetrically 1.5 cm from either side of the central column. Table 1 summarizes the different optical path-lengths for each dot of the multipoint network.

## 2.6. Experimental procedure

Measurements were performed in a thermo-regulated room ($20 \pm 1 \text{ °C}$). Before starting the acquisition data recordings, each glass (previously level-marked with 100 mL of distilled water) was placed on the support provided for this purpose between the two parabolic mirrors of the mapping set-up, as shown in Fig. 2b. The laser current modulation was set to 84 Hz, and the equivalent of four transmission spectra were recorded at each pixel before switching to the next one. These four spectra were then averaged to improve the detection limit of the spectrometer (integration time = 1/21 s). The modulation frequency of 84 Hz was chosen to avoid harmonics of the French main frequency (50 Hz). This value represents an optimal compromise: it offers high temporal resolution without oversampling, and allows the full 2D multipoint scan of the glass headspace to be completed in under 600 ms. The spectrometer detection limit of gas-phase ethanol is both a function of integration time and optical path, Table 1 summarizes the lowest measurable ethanol concentration for each pixel of the 2D matrix. A volume of 10 cL of champagne was gently poured in the glass 20 s after the acquisition started in order to record baseline ($I_0$) through the empty glass for each pixel of the multipoint network. Real-time monitoring of gas-phase ethanol was recorded within 250 seconds just after the end of the pouring process. A total of three successive pourings were performed, for each temperature, to provide one single average gas-phase ethanol concentration for each point of the 2D multipoint network in the glass headspace.

# 3. Results

## 3.1. How gaseous ethanol evolves, in space and time, in the glass headspace

From the start of serving champagne in the glass, and during the next five minutes following, the time dependence of the mixing ratios of gaseous ethanol along a 2D network of twelve points found in the headspace of the glass is displayed in Fig. 8 (for 100 mL of champagne dispensed at 20 °C). Firstly, and whatever the level at which the monitoring is done in the glass headspace, a rapid increase
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Sketch of the standard soda-lime-silica INAO glass filled with 10 cL of champagne, and with the multipoint network chosen to monitor gaseous ethanol in the glass headspace; The progression of the numbers, from 0 to 11, follows the progression of the laser beam through the glass headspace (a); 3D model of the glass used in Comsol Multiphysics, with the mesh of glass headspace (b); Side view of the mesh of the glass headspace (c).</figcaption></figure>

in the mixing ratios of gaseous ethanol was observed during the several seconds following the start of serving, until a maximal mixing ratio close to 0.9% vol/vol was reached. Secondly, after reaching this maximum concentration, and whatever the point of measurement of the 2D network in the glass headspace, an overall decrease in gaseous ethanol following an exponential decay-type was observed over a timescale in the order of 100 s. Then, in a final step, the ethanol mixing ratio stabilizes at a plateau value, which strongly depends on the distance from the measuring point to the surface of the liquid. Indeed, the closer the measurement point to the liquid surface, the higher the value of the plateau (from about 0.75% vol/vol, 1.4 cm above the champagne surface, to about 0.25% vol/vol, 4.4 cm above the champagne surface). Therefore and unambiguously, about a hundred seconds after the start of the service, a steady state regime with a strong vertical gradient of gaseous ethanol was clearly evidenced in the headspace above the champagne surface. Nevertheless, and interestingly, along a horizontal cross section in the glass headspace, the concentration of gaseous ethanol appeared spatially homogeneous. It is worth noting that a vertical gradient of gas-phase CO₂ had already been observed in the headspace of champagne glasses, under various tasting conditions, as described in the articles by Moriaux et al. [7,32], and Alfonso et al. [9]. Moreover, and notably, in various glass types (including the same INAO glass used in the present study), no horizontal gradient of gas-phase CO₂ had been detected to date [7,9,32].

## 3.2. The influence of champagne temperature

The influence of the champagne temperature on the spatial and time dependent gaseous ethanol footprint left in the glass headspace was also examined through the ICL sensor. The monitoring of gaseous ethanol was conducted along the axis of symmetry of the glass, 0.5 cm below the glass edge (i.e., at the pixel 7), and as close as possible to the champagne surface (i.e., at the pixel 4), for two champagne temperatures (namely, 12 °C and 20 °C). For 100 mL of champagne dispensed in the glass, the resulting spatial and time dependence of the gaseous ethanol footprint is displayed in Fig. 9 (in the glass headspace, along the axis of symmetry, within the next five minutes following the beginning of the pouring process, and for the two champagne temperatures). Whatever the champagne temperature, the same qualitative overall behavior regarding gaseous ethanol was found. After the rapid increase in ethanol corresponding to the few seconds of the serving step, the vertical stratification of the gaseous ethanol was progressively revealed, but with both a maximum mixing ratio of ethanol reached

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Monitoring of gas-phase ethanol along a 2D network in the headspace of the glass (see inset) filled with 10 cL of champagne at 20 °C; From the start of serving champagne (light gray dashed line), and during the 5 next minutes following the end of the serving (dark gray dashed line), the mixing ratios of gaseous ethanol resulting from three successive pourings were averaged, with their respective standard deviations displayed every 20 measurement points.</figcaption></figure>

after the serving step and a quasi steady-state plateau value strongly temperature-dependent. The peak of gaseous ethanol reached in the glass headspace was indeed about 0.6% and 0.9% (at 12 °C and 20 °C, respectively); whereas, the plateau concentrations of gaseous ethanol were about 0.5% and 0.75%, 1.4 cm above the champagne surface, and about 0.15% and 0.2%, 0.5 cm below the glass edge (at 12 °C and 20 °C, respectively). Through µGC, a similar plateau of gaseous ethanol had already been observed by Liger-Belair et al. [5] in the headspace of various champagne glasses, but with very poor spatial and temporal resolutions which did not make it possible to highlight every details of the ethanol footprint in the glass headspace.

# 4. Discussion

## 4.1. The vapor-liquid equilibrium in the glass headspace

In ambient air, the partial pressure of ethanol is obviously zero. However, from the start of serving champagne in the glass, the ethanol
<figure><img src="image_12.png" /><figcaption>Fig. 9. Monitoring of gas-phase ethanol close to the champagne surface and close to the glass edge (see inset) in the headspace of the glass filled with 10 cL of champagne at 12 and 20 °C, respectively; From the start of serving champagne, and during the 5 next minutes following, the mixing ratios of gaseous ethanol resulting from three successive pourings were averaged, with their respective standard deviations displayed every 20 measurement points.</figcaption></figure>

present in the wine inevitably evaporates in the glass headspace ruled by the vapor–liquid equilibrium. As a first approximation, champagne can be viewed as a water–ethanol mixture (with 12.5 ± 0.5% ethanol by volume). Because water–ethanol mixtures are non-ideal binary solutions, the vapor pressure of ethanol in a water–ethanol solution (denoted $P_{EIOH}$) is ruled by the following modified Raoult's law [38]:

$$P_{EIOH} = x_{EIOH} \gamma_{EIOH}(T) \phi_{EIOH}(T) P_{EIOH}^{Sat}(T) \quad (3)$$

with $x_{EIOH}$ being the molar fraction of ethanol in the liquid phase, $\phi_{EIOH}(T)$ being the temperature-dependent fugacity coefficient of ethanol (close to unity for ethanol at 12 and 20 °C [39]), $\gamma_{EIOH}(T)$ being the temperature-dependent activity coefficient of ethanol in the liquid phase (estimated with the modified UNIFAC model by taking into account the Van Der Waals interaction between functional groups composing the molecules inside a complex liquid mixture) [40], and $P_{EIOH}^{Sat}(T)$ being the vapor-pressure of ethanol in a pure ethanol solution (given by the empirical equation proposed by Dillon et al. [41] in the temperature range between -23.15 and 376.85 °C).

Therefore, based on the latter modified Raoult's law, the vapor pressures of ethanol in a water–ethanol mixture with 12.5 ± 0.5% ethanol by volume, are 553 ± 67 Pa, and 942 ± 110 Pa (at 12 ± 1 °C and 20 ± 1 °C, respectively). Finally, the ambient pressure being close to 1 atm (i.e., about 10⁵ Pa), the corresponding saturated mixing ratios of ethanol at the vapor–liquid equilibrium (in %) are 0.55 ± 0.07%, and 0.94 ± 0.11% (at 12 ± 1 °C and 20 ± 1 °C, respectively). It turns out that these saturated mixing ratios determined at the vapor–liquid equilibrium are totally consistent with the peak of gaseous ethanol mixing ratios reached rather homogeneously in the glass headspace a few seconds after the start of pouring for a champagne served at 12 and 20 °C, respectively (see Figs. 8 and 9).

It is worth noting that the saturated vapor pressure of ethanol is reached quickly and rather homogeneously in the whole glass headspace certainly because of effervescence. Indeed, in case of champagne and other sparkling wines, the bursting of CO₂ bubbles at the liquid surface triggers the formation of high-speed liquid jets projected upwards, which quickly break-up into a myriad of tiny droplets of wine several cm above the liquid surface [42,43]. A numerical modeling proposed by Ghabache et al. [44] revealed that, in the context of sparkling wine tasting, with a standard bubbling/bursting rate close to 400 bubbles per second, the aerosol of droplets produced by the

bursting of bubbles allowed evaporation of the wine approximately 10 times more efficiently than the evaporation from the surface of the wine only (as for a still wine, for example).

## 4.2. Mapping gaseous ethanol in the glass headspace by a 3D diffusion COMSOL model

As seen in Fig. 8, after reaching the peak of gaseous ethanol corresponding to the vapor–liquid equilibrium mixing ratio in the glass headspace quickly after the start of service, a steady state regime with a strong vertical gradient of gaseous ethanol appears above the champagne surface (whereas the mixing ratio of gaseous ethanol remained horizontally homogeneous). The establishment of this steady state regime was examined under the angle of the combined effects of the vapor–liquid equilibrium and molecular diffusion of ethanol in an open system initially devoid of gas-phase ethanol.

Diffusion is a consequence of the constant stochastic thermal motion of molecules, as first described by Adolf Fick in the 19th century [45]. If there is a concentration gradient between at least two adjacent media, diffusion occurs until equilibrium is reached [46]. The so-called second Fick's law established that the rate of change of concentration at a point in space is proportional to the second derivative of concentration with space. Fick's second law of diffusion is a linear partial differential equation (PDE) with the dependent variable being the concentration of the chemical species under consideration (i.e., gaseous ethanol in the present case). The rate of change of the gaseous ethanol concentration at a point in the glass headspace is therefore ruled by the second Fick's law as follows:

$$\frac{\partial C_{EIOH}}{\partial t} = D_{EIOH} \nabla^2 C_{EIOH} \quad (4)$$

with $C_{EIOH}$ being the concentration of gaseous ethanol, and $D_{EIOH}$ being the diffusion coefficient of ethanol in air ($m^2 s^{-1}$); the temperature-dependent diffusion coefficient of ethanol in air was determined according to the empirical equation developed in the article by Lapuerta et al. [47].

In addition, to achieve the spatial and temporal modeling of gaseous ethanol in the glass headspace, initial and boundary conditions must be prescribed. As seen in Fig. 8, immediately after serving champagne (i.e. at $t = 0$), gaseous ethanol appeared homogeneously distributed throughout the glass headspace, with a mixing ratio very close to the corresponding vapor–liquid equilibrium mixing ratio $C^{sat}$. Moreover, at the champagne surface (i.e., $y = 0$), the mixing ratio of gaseous ethanol is continuously governed by the vapor–liquid equilibrium and is therefore also equal to $C^{sat}$.

Nevertheless, at the top of the headspace, the glass is open to the ambient air. The ethanol mixing ratio at the edge of the glass is therefore undoubtedly influenced by the dynamics of the ambient air flow. Therefore, the boundary condition near the edge of the glass is a little more complex and deserves to be discussed below. In the case of an open atmosphere (obviously devoid of gaseous ethanol) stirred by natural convection capable of continuously removing ethanol vapors that reach the top of the headspace from the glass, the ethanol gas mixing ratio can be considered zero near the edge of the glass. Conversely, under perfectly static ambient air conditions, the mixing ratio of ethanol can be considered zero at an infinite distance from the edge of the glass, and therefore inevitably non-zero at the edge of the glass. However, gaseous ethanol was not monitored above the glass headspace. The gaseous ethanol mixing ratio was therefore arbitrarily assumed to be constant (and denoted $C_{edge}$) at the edge of the glass, thus becoming an adjustable parameter to best fit the experimental data. This choice was reinforced by previous observations (done by infrared imaging) of a cloud of gaseous CO₂ stagnating several millimeters above a flute for several minutes after the champagne was served [5], as shown in the time sequence displayed in Fig. 10. Finally,
<figure><img src="image_13.png" /><figcaption>Fig. 10. False color infrared time-sequence showing gas-phase CO₂ escaping from the champagne surface and stagnating above a glass for several minutes after champagne was poured (readapted from Liger-Belair et al. [5]); Because the density, and the diffusion coefficient in air, of gaseous ethanol are close to that of gaseous CO₂, it is therefore highly likely that the mixing ratio of ethanol is not zero at the edge of the glass.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 11. Two different computations of the 3D model with a null mixing ratio at the glass edge (black dotted line) and at an infinite distance (black dashed line) for 12 and 20 °C (a); During the steady-state regime, beyond 200 s after the end of serving, plateau values of gas-phase ethanol mixing ratios measured with the ICL sensor along the axis of symmetry of the glass headspace, as compared with those determined from the purely diffusive 3D-model (respectively 1.4 cm, 2.4 cm, 3.4 cm, and 4.4 cm above the liquid surface, and at 12 and 20 °C) (b); Color maps showing the spatial distribution of the mixing ratios of gaseous ethanol in the plane of symmetry of the glass headspace, as modeled by pure diffusion during the steady-state regime, according to the temperature of a water-ethanol mixture (with 12.5% ethanol by volume) (c).</figcaption></figure>

the following initial and boundary conditions were applied throughout the glass headspace:

$$ \left\{ \begin{array}{l} C(t = 0; \forall \Omega) = C^{sat} \\ C(\forall t; \forall(x, z) \in \Omega; y = y_{edge}) = C_{edge} \\ C(\forall t^*; \forall(x, z) \in \Omega; y = 0) = C^{sat} \end{array} \right. \qquad (5) $$

with Ω being the entire volume of the glass headspace, and y edge being the position of the glass edge.

The diffusion PDE completed with these initial and boundary conditions was numerically solved using the Comsol Multiphysics software with the “transport of diluted species” interface [48,49]. The glass headspace was modeled as a 3D domain, and a typical mesh of the domain is presented in Fig. 7. The mixing ratio range of gaseous ethanol at the top of the headspace was computed by resolving the 3D model for a glass filled with 10 C L of a water-ethanol mixture with a boundary

conditions equal to zero at the edge of the glass, and another one located at an infinite distance of the glass. The results, displayed Fig. 11a, presage a maximum ethanol mixing ratio of 1430 and 2420 ppm for 12 and 20 °C, respectively. In Fig. 11b, the plateau values of gas-phase ethanol corresponding to the steady-state regime as measured with the ICL sensor (beyond 200 s after the end of serving) are compared with those determined with the purely diffusive 3D-model, for both champagne temperatures. In the model, the ethanol mixing ratio at the glass edge (i.e., C edge ) was chosen to best fit our experimental data. A 1000 ppm ethanol mixing ratio (for both temperature) gives the best concordance between the model and experimental data. The color maps displayed in Fig. 11c show the steady state regime of the spatial distribution of the mixing ratios of gaseous ethanol in the plane of symmetry of the glass headspace, as modeled by pure diffusion
<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Spatial- and time-dependent distribution of gaseous ethanol as modeled by pure diffusion along the axis of symmetry of the headspace of the glass filled with a 12.5% water-ethanol mixture at 12 and 20 °C; For both temperatures, the purely diffusive model was compared with our experimental data recorded through the ICL sensor in the headspace of the champagne glass.</figcaption></figure>

according to the temperature of a water-ethanol mixture (with 12.5% ethanol by volume).

The space and time evolution of the numerically computed mixing ratio of gaseous ethanol in the glass headspace, along its axis of symmetry, is displayed in Fig. 12, still with a 1000 ppm ethanol mixing ratio constraint at the glass edge. In Fig. 12 the time-dependent footprint approached by this purely diffusive 3D-model was also compared with the temperature-dependent ethanol footprint revealed through the ICL sensor in case of the 12 and 20 °C champagne samples. Firstly, on a timescale of several tens of seconds after the end of service, the 3D-model accounts for the establishment of a steady state regime, with a vertical gradient of gaseous ethanol in very good agreement with those observed experimentally for both champagne temperatures. Nevertheless, in the steady-state regime at 12 °C (shown in Fig. 11b), the experimental data close to the liquid surface (1.4 and 2.4 cm) slightly deviate from the diffusive model and might be due to thermal convection between the liquid phase (at 12 °C) and the gaseous headspace above champagne (near 20 °C). However, it is noteworthy to mention that the spatial- and time-dependent distribution of gaseous ethanol recorded through the ICL sensor in the headspace of a champagne glass is in very good agreement with a purely diffusive model.

## 5. Conclusions and prospects

Based on the recent ICL technology, a sensor dedicated to monitor gaseous ethanol in the headspace of wine glasses was built and validated. With the integration of a new laser dedicated to ethanol monitoring into the CO₂-DLS setup, the instrument has accordingly been renamed ŒNOLAS (ŒNOlogic Laser Absorption Spectrometer). Unlike gas chromatography and metal oxide semiconductors method, the developed ICL sensor is calibration free, nonintrusive and has a much higher acquisition rate, which coupling to galvanometric mirrors led to a mapping of the mixing ratio of gaseous ethanol, in space and time, in the headspace of a champagne glass. From the start of serving champagne in the glass, and during the several minutes following, a temperature-dependent ethanol footprint was revealed in the glass headspace, which was discussed based on the vapor-liquid equilibrium combined with a purely diffusive 3D-model. On a timescale of several tens of seconds after the end of service, the 3D-model and our experimental measurements achieved with the ICL sensor account for the establishment of a very stable steady state regime showing a vertical gradient of gaseous ethanol in the glass headspace. The very strong quantitative agreement between experimental data and a purely diffusive 3D-model highlights the predominance of molecular diffusion in the spatial and temporal distribution of gaseous species present in the headspace of a wine glass.

Most interestingly, from the taster's point of view, the observed temporal and spatial distribution of ethanol vapor might affect the olfactory perception of champagne and sparkling wines. Indeed, the sensory descriptors of red and white model wines, as well as the orthonasal alcohol attribute perception induced by both the olfactory and trigeminal properties of ethanol, are strongly affected by the change in ethanol content. We thus suppose that a lower mixing ethanol ratio close to the edge of the glass might increase the perception of fruity aromas of wines or change the sensory descriptors of the wine.

To further explore the behavior of gaseous ethanol, and probably most of the VOCs in the glass headspace, it would be interesting to monitor the mixing ratio of gaseous species above the glass headspace, as well as natural convection in ambient air (which should have a key role on the mixing ratios of gaseous species close to the glass edge). Moreover, additional work might be carried out to study if consumers would be able to discriminate varying levels of ethanol mixing ratio close to the edge of the glass, under various tasting conditions. Also in the near future, monitoring of gaseous ethanol will be achieved using the ICL ethanol sensor, in a collection of glasses of various shapes and volumes, under standard tasting conditions of still wines, sparkling wines and spirits.

## CRediT authorship contribution statement

**Florian Lecasse:** Writing – original draft, Software, Methodology, Investigation, Formal analysis, Conceptualization. **Raphaël Valion:** Software, Methodology, Conceptualization. **Clément Jacquemin:** Software, Formal analysis, Conceptualization. **Vincent Alfonso:** Software, Methodology. **Clara Cilindre:** Methodology, Conceptualization. **Bertrand Parvitte:** Methodology, Conceptualization. **Virginie Zéninari:** Methodology, Conceptualization. **Gérard Liger-Belair:** Writing – original draft, Methodology, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

F. Lecasse acknowledges la Région Grand-Est and the Université de Reims Champagne-Ardenne, France for his PhD funding. The authors thank Champagne Castelnau (Reims, Marne) for regularly supplying the laboratory with various champagne samples.
# Data availability

Data will be made available on request.

# References

[1] J.-M. Cavaillon, S. Legout, Louis pasteur: Between myth and reality, Biomolecules 12 (4) (2022) http://dx.doi.org/10.3390/biom12040596.

[2] L. Zhang, Q. Liu, Y. Li, S. Liu, Q. Tu, C. Yuan, Characterization of wine volatile compounds from different regions and varieties by HS-SPME/GC-MS coupled with chemometrics, Curr. Res. Food Sci. 6 (August 2022) (2023) 100418, http://dx.doi.org/10.1016/j.crsb.2022.100418.

[3] G. Liger-Belair, C. Cilindre, Recent progress in the analytical chemistry of champagne and sparkling wines, Annu. Rev. Anal. Chem. 14 (1) (2021) 21–46, http://dx.doi.org/10.1146/annurev-anchem-061318-115018.

[4] C. Cilindre, A. Conreux, G. Liger-Belair, Simultaneous monitoring of gaseous CO₂ and ethanol above champagne glasses via micro-gas chromatography (µGC), J. Agricult. Food Chem. 59 (13) (2011) 7317–7323, http://dx.doi.org/10.1021/jf200748t.

[5] G. Liger-Belair, M. Bourget, H. Pron, G. Polidori, C. Cilindre, Monitoring gaseous CO₂ and ethanol above champagne glasses: Flute versus coupe, and the role of temperature, in: J. Langowski (Ed.), PLoS One 7 (2) (2012) 1–8, http://dx.doi.org/10.1371/journal.pone.0030628.

[6] A.L. Moriaux, R. Vallon, C. Cilindre, B. Parvitte, G. Liger-Belair, V. Zeninari, Development and validation of a diode laser sensor for gas-phase CO₂ monitoring above champagne and sparkling wines, Sensors Actuators, B: Chem. 257 (2018) 745–752, http://dx.doi.org/10.1016/j.snb.2017.10.165.

[7] A.-L. Moriaux, R. Vallon, F. Lecasse, N. Chauvin, B. Parvitte, V. Zéninari, G. Liger-Belair, C. Cilindre, How does gas-phase CO₂ evolve in the headspace of champagne glasses? J. Agricult. Food Chem. 69 (7) (2021) 2262–2270, http://dx.doi.org/10.1021/acs.jafc.0c02958.

[8] F. Lecasse, R. Vallon, F. Polak, C. Cilindre, B. Parvitte, G. Liger-Belair, V. Zéninari, An infrared laser sensor for monitoring Gas-Phase CO₂ in the headspace of champagne glasses under wine swirling conditions, Sensors 22 (15) (2022) 5764, http://dx.doi.org/10.3390/s22155764.

[9] V. Alfonso, F. Lecasse, R. Vallon, C. Cilindre, B. Parvitte, V. Zéninari, G. Liger-Belair, Mapping gas–phase CO₂ in the headspace of two champagne glasses through infrared laser absorption spectroscopy: oxenoptert glass versus INAO glass, OENO One 58 (2) (2024) http://dx.doi.org/10.20870/oeno-one.2024.58.2.7886.

[10] L. Chen, P. Darriet, Strategies for the identification and sensory evaluation of volatile constituents in wine, Compr. Rev. Food Sci. Food Saf. 20 (5) (2021) 4549–4583, http://dx.doi.org/10.1111/1541-4337.12810.

[11] A. De-la Fuente-Blanco, I. Arias-Pérez, A. Escudero, M.-P. Sáenz-Navajas, V. Ferreira, The relevant and complex role of ethanol in the sensory properties of model wines, OENO One 58 (3) (2024) http://dx.doi.org/10.20870/oeno-one.2024.58.3.7864.

[12] C.M. Ickes, K.R. Cadwallader, Effects of ethanol on flavor perception in alcoholic beverages, Chemosens. Percept. 10 (4) (2017) 119–134, http://dx.doi.org/10.1007/s12078-017-9238-2.

[13] P. Jones, R. Gawel, I. Francis, E. Waters, The influence of interactions between major white wine components on the aroma, flavour and texture of model white wine, Food Qual. Pref. 19 (6) (2008) 596–607, http://dx.doi.org/10.1016/j.foodqual.2008.03.005.

[14] A. Escudero, E. Campo, L. Fariña, J. Cacho, V. Ferreira, Analytical characterization of the aroma of five premium red wines. Insights into the role of Odor Families and the concept of fruitiness of wines, J. Agricult. Food Chem. 55 (11) (2007) 4501–4510, http://dx.doi.org/10.1021/jf0636418.

[15] A.A. Nolden, J.E. Hayes, Perceptual qualities of ethanol depend on concentration, and variation in these percepts associates with drinking frequency, Chemosens. Percept. 8 (3) (2015) 149–157, http://dx.doi.org/10.1007/s12078-015-9196-5.

[16] D. Wollan, D.-T. Pham, K.L. Wilkinson, Changes in wine ethanol content due to evaporation from wine glasses and implications for sensory analysis, J. Agricult. Food Chem. 64 (40) (2016) 7569–7575, http://dx.doi.org/10.1021/acs.jafc.6b02691.

[17] T. Arakawa, K. Iitani, X. Wang, T. Kajiro, K. Toma, K. Yano, K. Mitsubayashi, A sniffer-camera for imaging of ethanol vaporization from wine: The effect of wine glass shape, Analyst 140 (8) (2015) 2881–2886, http://dx.doi.org/10.1039/C4AN02390K.

[18] L. Ding, D. Dong, L. Jiao, W. Zheng, Potential using of infrared thermal imaging to detect volatile compounds released from decayed grapes, PLoS One 12 (6) (2017) 1–11, http://dx.doi.org/10.1371/journal.pone.0180649.

[19] N. Yamazoe, G. Sakai, K. Shimanoe, Oxide semiconductor gas sensors, Catal. Surv. Asia 7 (2003) 63–75, http://dx.doi.org/10.1023/A:1023436725457.

[20] D. Chen, M. Liu, L. Yin, T. Li, Z. Yang, X. Li, B. Fan, H. Wang, R. Zhang, Z. Li, H. Xu, H. Lu, D. Yang, J. Sun, L. Gao, single-crystalline MoO₃ nanoplates: topological synthesis and enhanced ethanol-sensing performance, J. Mater. Chem. 21 (25) (2011) 9332–9342, http://dx.doi.org/10.1039/C1JM11447F.

[21] C.A. Zito, T.M. Perfecto, D.P. Volanti, Impact of reduced graphene oxide on the ethanol sensing performance of hollow SnO₂ nanoparticles under humid atmosphere, Sensors Actuators B: Chem. 244 (2017) 466–474, http://dx.doi.org/10.1016/j.snb.2017.01.015.

[22] Y. Liu, X. Li, Y. Wang, X. Li, P. Cheng, Y. Zhao, F. Dang, Y. Zhang, Hydrothermal synthesis of AuSnO₂ hierarchical hollow microspheres for ethanol detection, Sensors Actuators B: Chem. 319 (2020) 128299, http://dx.doi.org/10.1016/j.snb.2020.128299.

[23] Y.-H. Liu, S.-J. Young, L.-W. Ji, Y.-L. Chu, S.-J. Chang, High-sensitive ethanol gas sensor using Ag modified ZnO nanosheets, Talanta Open 10 (2024) 100386, http://dx.doi.org/10.1016/j.talo.2024.100386.

[24] T. Alizadeh, F. Rezaloo, A new chemiresistor sensor based on a blend of carbon nanotube, nano-sized molecularly imprinted polymer and poly methyl methacrylate for the selective and sensitive determination of ethanol vapor, Sensors Actuators B: Chem. 176 (2013) 28–37, http://dx.doi.org/10.1016/j.snb.2012.08.049.

[25] T. Cowen, S. Grammatikos, M. Cheffena, Highly selective ethanol vapour sensing materials for a new generation of gas sensors based on molecularly imprinted polymers, Talanta 281 (2025) 126902, http://dx.doi.org/10.1016/j.talanta.2024.126902.

[26] S.W. Sharpe, T.J. Johnson, R.L. Sams, P.M. Chu, G.C. Rhoderick, P.A. Johnson, Gas-Phase databases for quantitative infrared spectroscopy, Appl. Spectrosc. 58 (12) (2004) 1452–1461, http://dx.doi.org/10.1366/0003702042641281.

[27] H. Kang, H.-T. Jung, Gas sensors for climate change, ACS Sensors 9 (3) (2024) 1031–1032, http://dx.doi.org/10.1021/acsensors.4c00429.

[28] N. Liu, L. Xu, S. Zhou, L. Zhang, J. Li, Simultaneous detection of multiple atmospheric components using an NIR and MIR laser hybrid gas sensing system, ACS Sens. 5 (11) (2020) 3607–3616, http://dx.doi.org/10.1021/acssensors.0c01910.

[29] G. Rodriguez Gutierrez, A. Ortiz Perez, S. Palzer, Integrated, selective, simultaneous multigas sensing based on nondispersive infrared spectroscopy-Type photoacoustic spectroscopy, ACS Sens. 9 (1) (2024) 23–28, http://dx.doi.org/10.1021/acssensors.3c01285.

[30] J. Hodgkinson, R.P. Tatam, Optical gas sensing: a review, Meas. Sci. Technol. 24 (1) (2013) 012004, http://dx.doi.org/10.1088/0957-0233/24/1/012004.

[31] M. Mulier, V. Zeninari, L. Joly, T. Decarpenterie, B. Parvitte, P. Jeandet, G. Liger-Belair, Development of a compact CO₂ sensor based on near-infrared laser technology for enological applications, Appl. Phys. B: Lasers Opt. 94 (4) (2009) 725–733, http://dx.doi.org/10.1007/s00340-009-3389-z.

[32] A.-L. Moriaux, R. Vallon, C. Cilindre, F. Polak, B. Parvitte, G. Liger-Belair, V. Zeninari, A first step towards the mapping of gas-phase CO₂ in the headspace of champagne glasses, Infrared Phys. Technol. 109 (April) (2020) 103437, http://dx.doi.org/10.1016/j.infraredd.2020.103437.

[33] J. Skrinsky, M. Skrinska, Z. Zelinger, Allan Variance - Stability of diode-laser spectrometer for monitoring of gas concentrations, in: 2014 International Conference on Mathematics and Computers in Sciences and in Industry, IEEE, 2014, pp. 159–164, http://dx.doi.org/10.1109/MCSI.2014.53.

[34] P. Werle, R. Mücke, F. Slemr, The limits of signal averaging in atmospheric trace-gas monitoring by tunable diode-laser absorption spectroscopy (TDLAS), Appl. Phys. B Photophys. Laser Chem. 57 (2) (1993) 131–139, http://dx.doi.org/10.1007/BF00425997.

[35] ISO99, Vocabulaire international de métrologie — Concepts fondamentaux et généraux et termes associés, 2007.

[36] A. Caputi, M. Ueda, P. Walter, T. Brown, Titrimetric determination of carbon dioxide in wine, Am. J. Enol. Vitic. 21 (1970) 140–144.

[37] G. Liger-Belair, Modeling the losses of dissolved CO₂ from Laser-Etched champagne glasses, J. Phys. Chem. B 120 (15) (2016) 3724–3734, http://dx.doi.org/10.1021/acs.jpcb.6b01421.

[38] K. O'Hare, P. Spedding, Evaporation of a binary liquid mixture, Chem. Eng. J. 48 (1) (1992) 1–9, http://dx.doi.org/10.1016/0300-9467(92)85001-P.

[39] B. Mariorella, H. Blanch, C. Wilke, Ethanol/Water, Physical and Chemical Properties for Separation Process Design, Lawrence Berkeley National Laboratory, 1982, p. 22.

[40] J. Gmehling, J. Li, M. Schiller, A modified UNIFAC model. 2. Present parameter matrix and results for different thermodynamic properties, Ind. Eng. Chem. Res. 32 (1) (1993) 178–193, http://dx.doi.org/10.1021/ie00013a024.

[41] H.E. Dillon, S.G. Penoncello, A fundamental equation for calculation of the thermodynamic properties of ethanol, Int. J. Thermophys. 25 (2) (2004) 321–335, http://dx.doi.org/10.1023/B:IJOT.0000028470.49774.14.

[42] G. Liger-Belair, C. Cilindre, R.D. Gougeon, M. Lucio, I. Gebefügi, P. Jeandet, P. Schmitt-Kopplin, Unraveling different chemical fingerprints between a champagne wine and its aerosols, Proc. Natl. Acad. Sci. 106 (39) (2009) 16545–16549, http://dx.doi.org/10.1073/pnas.0906483106.

[43] T. Séon, G. Liger-Belair, Effervescence in champagne and sparkling wines: From bubble bursting to droplet evaporation, Eur. Phys. J. Spec. Top. 226 (1) (2017) 117–156, http://dx.doi.org/10.1140/epjst/e2017-02679-6.

[44] E. Ghabache, G. Liger-Belair, A. Antkowiak, T. Séon, Evaporation of droplets in a Champagne wine aerosol, Sci. Rep. 6 (2016) 1–10, http://dx.doi.org/10.1038/srep25148.

[45] A. Fick, V. On liquid diffusion, Lond. Edinb. Dublin Philos. Mag. J. Sci. 10 (63) (1855) 30–39, http://dx.doi.org/10.1080/14786445508641925.
[46] J. Crank, *The Mathematics of Diffusion*, Second ed., 1975, pp. 310–316.

[47] M. Lapuerta, J.P. Hernández, J.R. Agudelo, An equation for the estimation of alcohol-air diffusion coefficients for modelling evaporation losses in fuel systems, Appl. Therm. Eng. 73 (1) (2014) 539–548, http://dx.doi.org/10.1016/j.applthermaleng.2014.08.009.

[48] J.L. Plawsky, Transport Phenomena Fundamentals, second ed., in: Chemical industries, CRC Press, 2009.

[49] A. Datta, V. Rakesh, *An Introduction to Modeling of Transport Processes: Applications to Biomedical Systems*, in: Cambridge Texts in Biomedical Engineering, Cambridge University Press, 2009.

**Florian Lecasse** received his M.Sc. in Applied Physics and Physical Engineering, with a specialization in Spectroscopy and Instrumental Engineering, from the University of Reims (France) in 2019, followed by a Ph.D. in laser spectroscopy applied to the study of sparkling wine in 2023. After working for nine months as a research engineer, he was appointed as an Associate Professor at the University of Reims in 2024. His current research focuses on the development of optical sensors to further investigate volatile organic compounds in the headspace of both sparkling and still wines.

**Raphaël Vallon** received his M.Sc. in Matter Sciences from the French Ecole Normale Supérieure of Lyon in 2004 and his Ph.D. in Atomic and Molecular Physics in 2007 from University Claude Bernard in Lyon. After Post-Doc studies at Orsay University and at ONERA, he received an associate professor position at the University of Reims in 2010. His current research interests are in the development of new mid-infrared laser sources and their use for the development of high-performance optical gas sensors.

**Clément Jacquemin** received his M.Sc. in Applied Physics and Physical Engineering, with a specialization in Spectroscopy and Instrumental Engineering, from the University of Reims (France) in 2019. He obtained his Ph.D. in Applied Physics from the University of Reims in 2023, conducted in partnership with the company mirSense (Orsay, France). He is currently working as an R&D Engineer at mirSense and serves as an associate researcher at the GSMA, University of Reims.

**Vincent Alfonso** received his M.Sc. in Applied Physics and Physical Engineering, with a specialization in Spectroscopy and Instrumental Engineering, from the University of Reims (France) in 2022. He is currently pursuing a Ph.D. in Applied Physics at the University of Reims.

**Clara Cilindre** is an associate professor at University of Reims, since 2009. During her Ph.D. in wine biochemistry (University of Reims), she studied Champagne wine proteins using a proteomic approach (2D electrophoresis and mass spectrometry). She has also worked on wine metabolomics using non-targeted approaches (NMR and FT-ICR-MS) to study the influence of various parameters such as grape variety, infection by Botrytis cinerea and wine aging. Now, her research is focused on the physical chemistry of foam and effervescence in champagne, sparkling wines, and carbonated beverages in general.

**Bertrand Parvitte** received the M.S. physics, the Ph.D. and the Habilitation degrees from the University of Reims, France, in 1993, 1997, and 2004, respectively. He received an associate professor position at the University of Reims in 1998, and a full professor position in 2006, in the same University. He has authored or coauthored more than 110 peer-reviewed journal articles and conference proceeding papers. His current research interests are in the improvements of the performances of optical gas sensors such as direct-absorption sensors, heterodyne radiometers or photoacoustic cells particularly via the modeling of these sensors and their numerical optimization.

**Virginie Zéninari** received the M.S. physics, the Ph.D. and the Habilitation degrees from the University of Reims, France in 1994, 1998, and 2004, respectively. She received an associate professor position at the University of Reims in 2000, and a full professor position in 2008 in the same University. She supervised 14 Ph.D. students and is currently deputy director of the GSMA lab in Reims. She has authored or coauthored more than 150 peer-reviewed journal articles, patents and conference proceeding papers. Almost 300 oral or poster presentations in national and international congresses were presented including more than 15 invited talks. Her current research interests are the development of optical gas sensors by direct-absorption spectroscopy and by photoacoustic technique using cutting-edge laser sources.

**Gérard Liger-Belair** received his Ph.D. in physical sciences in 2001 from the University of Reims, in France. He received an associate professor position at the University of Reims in 2002, and a full professor position, in 2007, in the same University. He has been researching the physics and chemistry behind the bubbling properties of champagne and sparkling wines for several years. His current interests include the science of bubbles, foams and thin films, and their broad interdisciplinary applications. He is the author of several academic and popular science books. His first book "Uncorked: The Science of Champagne", published by Princeton University Press, won the 2004 award for the Best Professional/Scholarly Book in Physics from the Association of American Publishers.