Adrien Douady

Cristian Puentes

Pierre Awad

Martine Esteban- Decloux

Batch distillation of spirits: experimental study and simulation of the behaviour of volatile aroma compounds

Keywords: batch distillation, copper still, spirits, volatile aroma compounds, simulation

This paper focuses on the behaviour of volatile compounds during batch distillation of wine or low wine, in traditional Charentais copper stills, heated with a direct open flame laboratory (600 L) and industrial (2,500 L) scale. Sixty-nine volatile compounds plus ethanol were analysed during the low wine distillation in the 600 L alembic still. Fortyfour were quantified and classified according to their concentration profile in the distillate over time and compared to previous studies. Based on the online recording of volume flow, density, and temperature of the distillate with a Coriolis flowmeter, distillation was simulated with ProSim® BatchColumn software. Twenty-six volatile compounds were taken into account, using the coefficients of the 'Non-Random Two Liquids' model. The concentration profiles of 18 compounds were accurately represented, with slight differences in the maximum concentration for seven species together with a single compound that was poorly represented. The distribution of the volatile compounds in the four distillate fractions (heads, heart, seconds and tails) was well estimated by simulation. Finally, data from wine and low wine distillations in the large-scale alembic still (2,500 L) were correctly simulated, suggesting that it was possible to adjust the simulation parameters with the Coriolis flowmeter recording and represent the concentration profiles of most of the quantifiable volatile compounds.

Introduction

The diversity of distilled spirits consumed around the world is very large. In Europe, the definition, description, labelling, and protection of geographical indications of spirit drinks are specified in regulation 110/2008 (1). The standard unit for ethanol concentration is the alcoholic strength by volume (annex I point 11), or ABV (Alcohol by Volume) in % v/v. The distillation process concentrates the ethanol and volatile aroma compounds in the fermented must, as well as those formed in situ through chemical reactions [START_REF] Nykänen | Aroma of beer, wine and distilled alcoholic beverages, handbook of aroma research[END_REF]. The quality and specific characteristics of a spirit beverage are highly dependent on the nature and concentration of the volatile compounds and, to a lesser extent, its ethanol concentration. The volatile compounds responsible for the overall aroma perception of spirit beverages belong to many chemical families, such as alcohols, carboxylic acids, esters, and aldehydes [START_REF] Nykänen | Aroma of beer, wine and distilled alcoholic beverages, handbook of aroma research[END_REF]. The precise relationship between volatile compounds and aroma perception is still difficult to assess, due to the variable nature of volatile compounds, concentration relative to sensory threshold and possible synergies. The diversity of volatile compounds and differences in concentration are mainly due to the raw material, fermentation method and the distillation process, which includes both the apparatus and the method [START_REF] Decloux | Distillation of AOC French spirits: cognac, armagnac, calvados and martinique agricultural rum, in The Alcohol Textbook[END_REF][START_REF] Ledauphin | Differences in the volatile compositions of french labeled brandies (armagnac, calvados, cognac, and mirabelle) using GC-MS and PLS-DA[END_REF].

Three main methods are commonly used with a typical distillation apparatus: (i) continuous distillation in a multi-stage distillation column (e.g. rum, vodka, Armagnac, Calvados, neutral alcohol), (ii) batch (simple discontinuous) distillation involving recycling (e.g. Cognac, Armagnac, Auge Calvados, rum), and (iii) batch distillation in a column involving recycling (fruit brandies). The preferred method depends on the organoleptic qualities desired. For instance, full-bodied, single malt whiskies are produced with a batch method in traditional pot stills, while lighter grain whiskies are produced in multi-stage distillation columns [START_REF] Gaiser | Computer simulation of a continuous whisky still[END_REF]. According to Ferrari et al. [START_REF] Ferrari | Determination of key odorant compounds in freshly distilled cognac using GC-O, GC-MS, and sensory evaluation[END_REF] the behaviour of volatile compounds is different during distillation in pot stills and rectification columns. Indeed, in contrast to esters, it was observed that the larger quantities of higher alcohols were recovered in the distillate in continuous distillation than in the simple batch process.

This study focused on the behaviour of volatile compounds during simple batch distillation. This method is generally conducted in a traditional copper still, known as an "alambic charentais" (Fig. 1) [START_REF] Decloux | Distillation of AOC French spirits: cognac, armagnac, calvados and martinique agricultural rum, in The Alcohol Textbook[END_REF][START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF][START_REF] Cantagrel | From vine to cognac, in Fermented Beverage Production[END_REF][START_REF] Awad | Evolution of volatile compounds during the distillation of cognac spirit[END_REF]. Distillation is carried out in two successive cycles. During the first cycle, known as 'wine distillation', the wine is introduced into the boiler. An initial small fraction of distillate ('heads') is collected and separated. Distillation then continues until the ABV of the distillate reaches about 2% v/v. This second fraction constitutes the low wine, with ethanol concentration between 27 and 30 % v/v. The second cycle is the low wine distillation. In analogy with wine distillation, the first fraction of distillate ('heads') with the highest ethanol concentration is removed. The 'heart' is collected and separated once the ethanol concentration of the distillate reaches about 60 % v/v. The average ethanol concentration of the heart must be lower than 72.4 %v/v for Cognac [START_REF] Bo Agri | Eau-de-Vie Des Charentes » Homologué Par Le Décret N° 2015-10 Du 7 Janvier 2015 Relatif à L'appellation D'origine Contrôlée « Cognac » Ou « Eau-de-Vie de Cognac » Ou « Eau-de-Vie Des Charentes[END_REF]. The last two fractions are the 'seconds' and 'tails'. The heart constitutes the spirit (or eau-de-vie) and is placed in an oak barrel for ageing. All the other distillate fractions are recycled back into the process. Several methods exist but heads and tails are usually recycled in the wine and seconds in the low wine. After the tails, when the gas burner is turned off, a small fraction of distillate, known as "petites-eaux", is sent directly to the distillation residue storage.

Despite its long history, batch distillation is still poorly understood. In order to acquire knowledge about the behaviour of volatile compounds, the National Interprofessional Committee of Cognac (BNIC) conducted experimental investigations in 1989. Both distillations (wine and low wine) were sampled in an operational plant. Fifty-seven volatile compounds were analysed and thirty-nine concentration profiles in the distillate were drawn as a function of decreasing ABV and classified in different types [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF], as shown in Fig. 2 andTable 1. Types 1, 2, 3, and 6 were present in both wine and low wine distillations, while types 4 and 5 were specific to the wine distillation and types 7 and 8 to the low wine distillation. Types 1, 7, and 8 were representative of highly volatile compounds that concentrate in the firsts running of distillate. Types 3 and 4 were present in all fractions of distillate. Types 2, 5 and 6 corresponded to low volatile compounds, which increased in concentration with decreasing alcohol strength. Unfortunately, neither the composition of the liquids introduced into the boiler (ABV, volatile compound concentrations), the distillation parameters (heating power profile, volumes, cut criteria), or the concentrations of the volatile compounds in the distillates were reported in this study. More recently, Lukić et al. [START_REF] Lukić | Behaviour of volatile compounds during traditional alembic distillation of fermented muscat blanc and muškat ruža porečki grape marcs[END_REF] investigated the distribution of 155 volatile compounds among the various cuts (heads, heart 1 to heart 3, and tails) during the distillation of fermented Muscat wines, in a traditional 120 L alembic still, heated over a direct open flame. They highlighted the importance of distillation cut criteria on the heart composition and the recycling of the tails fraction in the next distillation, in order to enrich the liquid to be distilled in the monoterpenes and C13 norisoprenoids responsible for varietal aromas. Spaho et al. [START_REF] Spaho | Effects of distillation cut on the distribution of higher alcohols and esters in brandy produced from three plum varieties[END_REF] investigated the impact of the ethanol concentration of the cut between the heart and tails on the distribution of some higher alcohols and esters, as well as the organoleptic characteristics of the heart fraction of fruit brandy made from three plum varieties, using a traditional 10 L copper still heated over a direct flame. Silva et al. [START_REF] Silva | Influence of the number of distillations on the composition of organic sugarcane spirit[END_REF] studied the impact of repeated batch distillations of mono-distilled organic sugarcane spirit, using a 12 L copper still, to improve product quality to meet national and international standards. Balcerek et al. [START_REF] Balcerek | The effect of distillation conditions and alcohol content in 'heart' fractions on the concentration of aroma volatiles and undesirable compounds in plum brandies[END_REF] studied the influence on plum brandy of the batch distillation method (initial distillation in a 35 L copper still heated with a water steam jacket, followed by a second distillation in a column or a single distillation in a column) and the ethanol concentration of the heart on its volatile composition, organoleptic characteristics, and concentrations of volatile compounds harmful to human health (eg methanol, hydrocyanic acid, and ethyl carbamate). Awad et al. [START_REF] Awad | Evolution of volatile compounds during the distillation of cognac spirit[END_REF] characterised the volatile compounds produced by chemical reactions during the batch distillation of Cognac in an industrial-scale alembic (2,500 L), using real wines and low wine. Sampling and analysis of the initial wine and low wine and the distillate fractions and residues, as well as volume measurements, provided data for mass balances. They identified several types of volatile compounds that increased significantly in quantity during distillation. It was found that the wine distillation was key to the formation of volatile compounds.

In parallel to this experimental approach, many researchers have used simulation to explore the behaviour of the volatile compounds and the influence of the operating parameters on distillation. Many studies have been published on continuous distillation in multistage columns [START_REF] Gaiser | Computer simulation of a continuous whisky still[END_REF][START_REF] Batista | Computer simulation applied to studying continuous spirit distillation and product quality control[END_REF][START_REF] Batista | Computational simulation applied to the investigation of industrial plants for bioethanol distillation[END_REF][START_REF] Valderrama | Advances on modeling and simulation of alcoholic distillation. Part 1: Thermodynamic modeling[END_REF][START_REF] Valderrama | Advances on modeling and simulation of alcoholic distillation. Part 2: Process simulation[END_REF][START_REF] Esteban-Decloux | Two industrial examples of coupling experiments and simulations for increasing quality and yield of distilled beverages, Food Bioprod[END_REF], batch distillation in columns [START_REF] Osorio | Rigorous dynamic modeling and simulation of wine distillations[END_REF][START_REF] Osorio | Wine distillates: practical operating recipe formulation for stills[END_REF] or alembic [START_REF] Scanavini | Cachaça production in a lab-scale alembic: modelling and computational simulation[END_REF][START_REF] Sacher | Dynamic modeling and simulation of an alembic pear wine distillation, Food Bioprod[END_REF]. For batch distillation in alembic stills, Scanavini et al. [START_REF] Scanavini | Cachaça production in a lab-scale alembic: modelling and computational simulation[END_REF] and Sacher et al. [START_REF] Sacher | Dynamic modeling and simulation of an alembic pear wine distillation, Food Bioprod[END_REF] developed models which need computational modification to be adapted to other distillation units. Scanavini et al. [START_REF] Scanavini | Cachaça production in a lab-scale alembic: modelling and computational simulation[END_REF] compared simulated data to two experimental distillations with a synthetic solution of seven volatile compounds, conducted in a laboratory scale (8 L) still, heated over a direct open flame. Good agreement between experimental and simulated data was observed for the ethanol concentration profile, temperature in the boiler, and volatile compounds in the heart. The model developed by Sacher et al. [START_REF] Sacher | Dynamic modeling and simulation of an alembic pear wine distillation, Food Bioprod[END_REF], was calibrated using a laboratory scale (2 L) copper heated by an electric resistor and with 17.9 % v/v pear distillate. They compared their results with the quantification of 15 volatile compounds. Good correlation was observed between experimental and simulated data for each fraction of distillate (heads, heart, seconds, tails). The concentration profiles over time for most volatile compounds were in agreement with those published by Cantagrel [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]. The only exception was 2phenylethanol, as the model used to estimate the vapour-liquid equilibrium data (UNIFAC Dortmund 1993, a predictive method developed by [START_REF] Gmehling | Modified UNIFAC model. 2. Present parameter matrix and results for different thermodynamic properties[END_REF] for the estimation of thermodynamic properties), was not adapted to this type of compound in hydroalcoholic solution. In fact, when data predicted by this UNIFAC model were compared with experimental findings published by Athès et al. [START_REF] Athès | Vapourliquid equilibria of aroma compounds in hydroalcoholic solutions: Measurements with a recirculation method and modelling with the NRTL and COSMO-SAC approaches[END_REF], the predicted volatility was overestimated and, consequently, the predicted concentration in the heart.

A good knowledge of liquid/vapour equilibria is indispensable to simulate a distillation process [START_REF] Valderrama | Advances on modeling and simulation of alcoholic distillation. Part 2: Process simulation[END_REF][START_REF] Puentes | Review and thermodynamic modeling with NRTL model of vapor-liquid equilibria (VLE) of aroma compounds highly diluted in ethanol-water mixtures at 101.3 kPa[END_REF]. Puentes et al. [START_REF] Puentes | Review and thermodynamic modeling with NRTL model of vapor-liquid equilibria (VLE) of aroma compounds highly diluted in ethanol-water mixtures at 101.3 kPa[END_REF] carried out a bibliographic review, gathering all available data on liquid-vapour equilibria for volatile compounds in hydro-alcoholic solutions, covering forty-four volatile compounds belonging to several chemical families (12 alcohols, 12 esters, nine carboxylic acids, one acetal, seven carbonyl compounds, one furan, and two terpenes). The 'Non Random Two Liquids' (NRTL) model was selected to represent the non-ideality of the solution [START_REF] Renon | Local compositions in thermodynamic excess functions for liquid mixtures[END_REF], as advised by [START_REF] Batista | Computer simulation applied to studying continuous spirit distillation and product quality control[END_REF][START_REF] Valderrama | Advances on modeling and simulation of alcoholic distillation. Part 1: Thermodynamic modeling[END_REF]. For modelling purposes, the interactions between the volatile compounds and the solvent (ethanol-water) were taken into account, whereas those between volatile compounds were ignored, due to their low concentrations. These data were used to classify the volatile compounds in three main groups, according to their relative volatilities with respect to ethanol and water, over the entire range of ethanol concentrations: (i) light (more volatile than ethanol), (ii) intermediate (less volatile than ethanol, but more volatile than water), and (iii) heavy (less volatile than water).

In this context, the present work focused on the behaviour of volatile compounds during a batch distillation in alembic stills with real wine or low wine in traditional copper Charentais stills, heated over a direct open flame, on laboratory (600 L) and industrial (2,500 L) scales. BatchColumn software (ProSim®) configured with the set of interaction parameters determined by Puentes et al. [START_REF] Puentes | Review and thermodynamic modeling with NRTL model of vapor-liquid equilibria (VLE) of aroma compounds highly diluted in ethanol-water mixtures at 101.3 kPa[END_REF] was used to assess the potential of commercial software to represent experimental distillation data.

Material and methods

Distillation

One experiment was conducted with a 600 L copper still, heated over a direct open flame, composed of a boiler, a wine heater and a cooler (Figure 1). The installation included several temperature probes (PT 100 sensors), a Coriolis flowmeter (Emerson, micro motion® F-Serie 050) and a data recording using InTouch 2014 R2 software, developed by Wonderware®. The Coriolis flowmeter, between the alcoholmeter receiver and the storage tanks, provided continuous measurement of distillate density, volume flow, and temperature. Online data were recorded every minute, except for volume flow, where the values recorded were the average over the previous 60 seconds. The low wine was introduced into the boiler. Heating power was fixed by gas pressure: 815 mbar to reach boiling point, 115 mbar during production of the heads, and then 195 mbar for the heart, seconds, and tails. Distillation cuts were determined by the ethanol concentration, indicated by the alcoholmeter and recorded. Four fractions of distillate were collected: heads, heart, seconds, and tails. The heads did not pass through the Coriolis flowmeter, to avoid clogging so no data was recorded. The volume and temperature of each fraction were measured. The volume of the residue was deduced from the material balance, as detailed below. At the end of the distillation, when the gas was turned off, the petites-eaux fraction passed directly into the main tank and was not collected.

Experimental data obtained in a previous study by Awad et al. (9) were used to assess the potential of the simulation software to represent the experimental data obtained using a standard size copper still (2,500 L), the maximum capacity authorised for Cognac distillation [START_REF] Bo Agri | Eau-de-Vie Des Charentes » Homologué Par Le Décret N° 2015-10 Du 7 Janvier 2015 Relatif à L'appellation D'origine Contrôlée « Cognac » Ou « Eau-de-Vie de Cognac » Ou « Eau-de-Vie Des Charentes[END_REF]. Supplementary data measured (but not published) by these authors (gas pressure, distillate strength and flow rate) were also used here for comparison purposes. The density, temperature, and volume flow of the distillate were measured by a Coriolis flowmeter (Endress Hauser® LPG). These values together with gas pressure, were recorded every 10 seconds.

Sampling

In the first experiment -distillation in a 600 L copper still -the low wine was introduced into the boiler and each fraction (heads, heart, seconds and tails) together with the residue were sampled to quantify the volatile compounds. Moreover, to obtain information about the behaviour of the volatile compounds, distillate samples (1.4 L) were taken from the heart (five samples) and seconds (four samples) after 30 minutes and then every hour. In total, 15 samples were collected.

Determining ethanol concentration and ethanol/water mass balances

Samples from low wine distillation in the 600 L copper still were analysed by the Union Nationale du Groupement des Distilleries Agricoles (UNGDA), laboratory (Malakoff, France). The ethanol concentration (ABV) of the samples (low wine, distillates and residue) were determined from density measurement at 20 °C, using an Anton Paar® DMA 500 electronic densitometer. These solutions were considered as a binary hydro-alcoholic system, being composed of water, ethanol and highly dilute volatile compounds. The error generated by this assumption on the ABV estimation varied between 0.5 % and 1 % [START_REF] Puentes | Simulation of spirits distillation for a better understanding of volatile aroma compounds behavior: Application to Armagnac production[END_REF]. Ethanol mass concentrations (Xm_eth, g of ethanol/g of solution) were derived from these data.

The volume, temperature, and ethanol concentration of each fraction of distillate (heads, heart, seconds, and tails) were measured in the tanks. To estimate the mass of each fraction, it was necessary to compute the density of the solution at the measured temperature. An Excel macro edited with Visual Basic was developed, using the equation from the International Alcoholometric Tables [START_REF]International Alcoholometric Tables, OIML R 22[END_REF].

One of the goals of this study was to assess the potential of the simulation tool to represent experimental data. To do this, it was first necessary to check the consistency of the experimental data, both offline (from the sample and tank measurements) and online (from the Coriolis flowmeter recordings). The offline data was evaluated by an ethanol/water mass balance and then compared with the online mass balances. As the petites-eaux fraction was neither collected nor taken into account in the residue sample, its ethanol concentration was deduced from the last droplets of tails and the residue.

For each fraction (j), the total mass of distillate (mj, kg solution) and the mass of ethanol (meth_j, kg ethanol) were determined using the volume (V, L solution), density (ρ, kg/L solution), and ethanol mass concentration (Xm_eth, g ethanol/g solution) in the various tanks, plus the samples analysed at the UNGDA, according to equations 1 and 2.

𝒎 𝒋 = 𝑉 𝑡𝑎𝑛𝑘_𝑗 × 𝜌 𝑡𝑎𝑛𝑘_𝑗 + ∑ 𝑉 𝑘 𝑛𝑏 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 𝑘=1 × 𝜌 𝑘 [1] 𝒎 𝒆𝒕𝒉_𝒋 = 𝑉 𝑡𝑎𝑛𝑘_𝑗 × 𝜌 𝑡𝑎𝑛𝑘_𝑗 × 𝑋 𝑚_𝑒𝑡ℎ_𝑡𝑎𝑛𝑘_𝑗 + ∑ 𝑉 𝑘 𝑛𝑏 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 𝑘=1 × 𝜌 𝑘 × 𝑋 𝑚_𝑒𝑡ℎ_𝑘 [2]
Then for each fraction (heart, seconds and tails), according to the below equations 3 and 4, ethanol/water mass balances were determined from online recorded data [volume flow (FT, L/h solution), density (ρ, kg/L solution), temperature (T, °C) and time] and calculated ethanol concentration (Xm_eth, g ethanol/g solution) from density and temperature, as explained previously.

𝒎 𝒋 = ∑ 𝐹𝑇 𝑘 𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑡𝑖𝑚𝑒 (min) 𝑘=1 /60 × ρ 𝑘 [3] 𝒎 𝒆𝒕𝒉_𝒋 = ∑ 𝐹𝑇 𝑘 𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑡𝑖𝑚𝑒 (min) 𝑘=1 /60 × ρ 𝑘 × 𝑋 𝑚_𝑒𝑡ℎ𝑎𝑛𝑜𝑙_𝑘 [4]

Volatile compound analysis and mass balances

Volatile compounds were analysed by the UNGDA using the methods reported in Puentes et al. [START_REF] Puentes | Simulation of spirits distillation for a better understanding of volatile aroma compounds behavior: Application to Armagnac production[END_REF]. Sixty-nine volatile compounds were quantified (16 alcohols, 20 esters, 19 carboxylic six terpenes, two lactones, two furans, two acetals, one carbonyl compound, and one norisoprenoid). Due to the large range of ethanol concentrations among the samples, they were classified in two groups: (i) in samples of low wine, heads, heart and seconds, the ABV was adjusted to 40 % v/v and (ii) in samples from the tails and residue, the ABV was adjusted to 12 % v/v. This standardisation of the ethanol concentration with de-ionised water or anhydrous alcohol reduced the matrix effects on the quantification of volatile compounds. Considering the variety of volatile compounds to be analysed (chemical family and concentration) three different methods were implemented. For volatile compounds present at high concentrations, direct injection into the gas chromatograph was effective. However, for volatile compounds at low concentrations (0.1-0.9 mg/L), liquid-liquid extraction using organic solvent was necessary before injection. Finally, for compounds with very low volatility, such as carboxylic acids, for which gas chromatography is not an appropriate technique, a pre-treatment step of derivatisation was required to convert them into benzylic esters.

In the same way, the offline data was assessed by way of a mass balance for each volatile compound analysed. For each fraction j, the mass fraction of each volatile compound i (Xm_i/j) was calculated with equation 5, using the concentration (mg/L) determined by the UNGDA laboratory and the calculated density of the solution (kg/L).

𝑋 𝑚_𝑖/𝑗 ( 𝑔 𝑔 ) = 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 × 10 -6 [5]
Then, the mass of volatile compounds i in fraction j was deduced from mass fraction (Xm_i/j) and the total mass of the fraction (mj). As previously, for fractions where several samples were collected during distillation (heart and seconds), the mass of volatile compounds i in fraction j was calculated with equation 6.

𝑚 𝑖/𝑗 = 𝑋 𝑚_𝑖/𝑡𝑎𝑛𝑘_𝑗 × 𝑚 𝑡𝑎𝑛𝑘_𝑗 + ∑ 𝑋 𝑚_𝑖/𝑘 𝑛𝑏 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 𝑘=1 × 𝑚 𝑘 [6]
Creation of the simulation tool with ProSim BatchColumn software BatchColumn software (ProSim®) was used to simulate the experimental distillation unit. This simulation tool includes a rigorous dynamic model for the design and analysis of batch distillation processes. Among the volatile compounds quantified, 36 were already present in the ProSim Simulis database. Regarding the thermodynamic modelling, the vapour phase was represented with the ideal gas law. In the case of carboxylic acids, capable of forming dimers due to strong hydrogen bonds, this was combined with an association model [START_REF] Detcheberry | Thermodynamic modeling of the condensable fraction of a gaseous effluent from lignocellulosic biomass torrefaction[END_REF]. The non-ideality of the liquid phase was represented with the NRTL model, using the interaction parameters estimated by Puentes et al. [START_REF] Puentes | Review and thermodynamic modeling with NRTL model of vapor-liquid equilibria (VLE) of aroma compounds highly diluted in ethanol-water mixtures at 101.3 kPa[END_REF]. The interaction parameters for four supplementary alcohols (1octanol, 1-decanol, 1-dodecanol, and 1-tetradecanol) were derived from the predictions of the UNIFAC Dortmund model (1993).

The simulation module (Fig. 3) was designed with five trays (cooler, three intermediate trays, and the boiler) to take into account the internal reflux due to heat losses through the still-head. Nevertheless, to minimise the exchange through the three intermediate trays, very small loads of liquid were considered (0.1 kg each). An efficiency of 0.9 was established for the boiler and 0.63 for the intermediate trays. These values were chosen a priori to represent the mass flow and ABV concentration of the distillate over time. The pressure of the installation was set at the standard atmosphere without pressure loss. Four tanks were connected to the cooler output to collect the distillate fractions (heads, heart, seconds and tails).

The initial mass, composition and temperature of the load in the boiler were taken from experimental data. The simulation was developed in five steps: heating the low wine to boiling point, followed by the separation of the distillate into four fractions (heads, heart, seconds and tails). The duration of each step was equivalent to the corresponding experimental value. During the distillation process, two adjustable parameters were the heating power (Qb) and internal reflux level (R). For the first step, heating power was estimated using the mass of liquid introduced into the boiler, its specific heat (dependent on ethanol concentration), the temperature difference between the initial value and boiling point (also dependent on ethanol concentration) and the duration of this step. Then heating power and reflux ratio were adjusted to obtain good agreement with the experimental mass flow and ethanol concentrations over time.

Results and discussion

For the experiment with the 600 L alembic still, a global ethanol/water mass balance was calculated using the experimental offline data, and its consistency was evaluated. Then, for each distillate fraction (except the heads), the mass and ethanol concentration in the online data were compared to offline data to verify their coherence. The mass balance was calculated for each volatile compound i and its concentration profile during distillation was compared with data reported by Cantagrel [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]. Simulated data (mass flow, ABV, concentration profiles and distributions of volatile compounds) were compared to experimental data. Finally, the experimental distribution of volatile compounds obtained during the distillation of wine and low wine in the 2,500 L alembic still were compared to the simulated results.

Global and ethanol mass balances

Ethanol concentration, as well as the volume and temperature of each distillate fraction, were used to calculate the total and ethanol/water mass balances (Table 2). The ethanol concentration of the petites-eaux fraction, which was not collected, was calculated from the last ethanol concentration measured in the tails (5.9 % v/v), the average ethanol concentration of the residue (0.07 % v/v) and the volatility of ethanol at this concentration. The ethanol concentration was 3.33 % v/v, corresponding to a 0.026 (g/g) ethanol mass fraction. To obtain a coherent balance, the volume of low wine had to be increased from 600 L to 609 L, a variation within the precision range of the volume introduced into the boiler.

The ethanol concentration measured in samples collected during distillation (heart and seconds) were compared to average values from online data. The small differences obtained (Table 3) suggest that the ethanol concentration estimated from the Coriolis flowmeter density and temperature measurement was reliable. Indeed, for the first eight comparisons, the maximum difference did not exceed 1 %. However, it should be emphasised that the Coriolis flowmeter may be less accurate at low ethanol concentrations, as evidenced by the difference of 5.12 % in the last comparison.

The ethanol concentration, total mass and ethanol mass were calculated from both offline and online data for each fraction of distillate, except the heads and petites-eaux, which did not pass through the Coriolis flowmeter. Differences are presented in Table 4. For the heart fraction, distillate and ethanol masses were lower online than offline, while the ethanol mass fraction difference was the lowest (0.46 %). Mass differences may be caused by an underestimated flow rate value. For the seconds, masses were also lower online than offline, but to a lesser extent. In contrast, online and offline masses of the tails were similar, while the difference in ethanol concentration was the highest (6.85 %). Nonetheless, the similarity of these results suggests that the data recorded by the Coriolis flowmeter were globally coherent and could be used later to adjust the simulation.

Volatile compound mass balances

Among the 69 volatile compounds analysed in the low wine, only 45 (9 alcohols, one carbonyl compound, one furan, one acetal, 18 esters, three terpenes and 12 carboxylic acids) were above their quantification limits. The total mass balance of each compound was established by comparing the mass contained in the boiler (In) and the sum of masses in the distillate and residue fractions (Out). These ratios (Out/In) are presented in Table 5 with volatile compounds previously analysed by Cantagrel [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF] indicated with a star (6 alcohols, one carbonyl compound, one furan, one acetal, 16 esters, and three carboxylic acids).

As observed previously by several authors [START_REF] Awad | Evolution of volatile compounds during the distillation of cognac spirit[END_REF][START_REF] Sacher | Dynamic modeling and simulation of an alembic pear wine distillation, Food Bioprod[END_REF], the mass balance ratio for alcohols at high concentrations are close to 1. However, for many volatile compounds present at low concentrations, outputs were often lower than inputs (Out/In < 1). This may be due to analytical issues, volatilisation of the compound at the condensing coil output or chemical reactions. On the contrary, a ratio above 1 (Out/In > 1) indicates either the formation of volatile compounds during distillation or analytical issues. Despite these differences, the 45 volatile compounds were considered in the rest of this study.

The experimental concentration profiles of volatile compounds during distillation

The concentrations of the 45 volatile compounds were plotted as a function of decreasing ABV of the distillate and classified according to figure 2. In Table 6, this classification is compared with that proposed by Cantagrel [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]. Among the 45 volatile compounds, only 28 were quantified in both studies and 22 had a similar classification to the one proposed by Cantagrel [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]. In Cantagrel, ethyl caprylate and ethyl caprate were classified as type 8, whereas, in this study, they were considered as type 1 (Table 6; Fig. 4a,4b). However, type 1 and type 8 have similar profiles and this modification may be explained by a small change in the distillation process. In the past, yeast residues containing ethyl caprylate and ethyl caprate were introduced into the boiler just after the end of the heart distillation, while this residue is no longer distilled with the low wine but blended into the wine. Ethyl stearate, ethyl oleate and ethyl linoleate were classified in type 1 instead of type 7 (Fig. 4c,4d,4e), and 2-phenylethanol was classified in type 6 instead of type 2 (Fig. 4f). However, in each case, it should be emphasised that the shape of both concentration profiles is comparable. Among the other compounds not considered by Cantagrel [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF], linalool, myristic acid, palmitic acid, and 2-phenylethyl caprylate are not considered as their concentration profiles were not correctly represented which may reflect analytical issues.

These results are similar to the classification of volatile compounds into three groups (light, intermediate and heavy) proposed by Puentes et al. [START_REF] Puentes | Simulation of spirits distillation for a better understanding of volatile aroma compounds behavior: Application to Armagnac production[END_REF], in the low ethanol concentrations in the liquid phase (<30 % v/v). As shown in Table 7, the classifications of the 24 volatile compounds considered in both studies were consistent with each other: all the type 1 and 7 species were classified as light compounds, type 3 and 6 as intermediate compounds, and type 2 as heavy compounds.

Comparison of experimental and simulated data

In order to evaluate the capacity of the simulation tool to represent experimental online data collected by the Coriolis flowmeter accurately, experimental and simulated ethanol concentrations and mass flow data were plotted (Fig. 5). The alignment of both curves indicated that the simulated data were consistent with the experimental data. Therefore, it was concluded that the design of the batch distillation unit, together with the heating power and internal reflux levels, were properly configured in the software.

Subsequently, the simulation tool was used to present the concentration profiles of the volatile compounds during distillation, as well as their distribution in the distillate fractions. Among the 41 volatile compounds clearly identified by type (Table 6), 26 were both available in the ProSim database and their NRTL coefficients were known. The consistency of simulated profiles with experimental data is presented in Table 8.

The simulated curves for 18 volatile compounds were overlaid on experimental data. An example (isoamyl acetate) is shown in Figure 6a. For seven of these compounds, the shape of the curve was the same but the maximum concentration was different (e.g. ethyl lactate in Figure 6b) or the maximum was obtained at a different ABV value (e.g. 1-hexanol in Figure 7d). Finally, only one volatile compound (formic acid) showed inconsistencies between the simulated and experimental data.

The simulation data were the most consistent for alcohols present in the distillate at high concentration (2-methyl-1-propanol, 3-methyl-1-butanol) (Fig. 7a). Nevertheless, for 2-methyl-1-butanol, also present at high concentration, a shift in the maximum concentration value was observed between the simulated and experimental data (Fig. 7b). The simulated 2-phenylethanol concentration profile was the same shape as the experimental one (Fig. 7c), but the experimental concentration in the tails fraction was lower than the simulated result, while all the other values were higher. This underestimated value may be related to the experimental mass balance ratio of 0.6 (Table 5). Finally, 1-hexanol presented a similar shape but a maximum concentration at a different ABV value (Fig. 7d).

For compounds with maximum concentrations at different ABV in the experimental and simulated data, it would be interesting to measure new equilibrium data, particularly at low ABV (<30 % v/v) and estimate new NRTL coefficients. With compounds with different maximum concentrations but at the same ABV, this may reflect analytical errors. It must be emphasised that most methods produce accurate results for the heart fraction but are not always adapted to analysing solutions with low ethanol concentrations.

Carboxylic acids were also simulated without taking into account dimerization using the ideal gas law. According to the concentration profiles reported in Figure 8, the introduction of an association model improved the simulation, especially for acetic acid (Fig 8a), a small molecule whose dimerised fraction in the vapour phase is high. For longer molecules, such as isovaleric acid, dimerisation becomes less important, which explains why the simulation with both models is similar (Fig 8b).

Values of experimental distribution of the volatile compounds in the various distillate fractions were compared to simulated data. In some cases, as the experimental mass balances did not give a ratio (Out/In) close to 1, the distribution was calculated using the total output mass. As shown in Table 9, the distribution data calculated by simulation was close to the experimental results for most volatile compounds. Moreover, experimental and simulated results for the heart and seconds fractions together (Ht+S) were closer. However, some differences were noted. The simulation predicted a higher concentration of 2-phenylethanol in the residue (47.1 %) compared to the experimental data (33.7 %). This overestimated concentration in the residue is in agreement with the simulated concentration profile during distillation (Fig. 7c). Similarly, the simulation predicted 6.5 % furfural in the tails and 11.2 % in the residue, whereas this compound was not detected in these experimental fractions. This may be due to the underestimation of the simulated values (Fig. 9), because the experimental mass balance was in agreement, with an (Out/In) quotient very close to 1 (0.96) (Table 5). Only two compounds, isobutanoic and isovaleric acids, presented inconsistent distribution between experimental and simulated data. Their corresponding equilibrium data and chemical analyses require further study.

Assessment of the simulation tool on other experimental data

In order to assess the potential of the simulation tool to represent wine and low wine distillations in a standard size copper still (2,500 L), experimental data from Awad et al. [START_REF] Awad | Evolution of volatile compounds during the distillation of cognac spirit[END_REF] were used as reference. The simulation module was configured as previously (three intermediate trays with 0.1 kg retention and 0.63 efficiency, a cooler with 0.1 kg retention, and a boiler with 0.9 efficiency). The heating power, reflux ratio, and duration of each step were chosen according to the control parameters. Experimental and simulated ethanol concentrations and distillate mass flows during wine distillation are illustrated in Figure 10. Figure 11 shows the same data for the low wine distillation.

As in the previous comparison, simulated data were in good agreement with experimental results for both ABV and mass flow of the distillate. Thus, simulation proved to be a suitable tool for studying the behaviour of volatile compounds during traditional distillation.

The experimental distributions of 23 volatile compounds in the distillate fractions were compared to simulated data for wine (Table 10) and low wine distillation (Table 11).

Unlike the previous study in the 600 L copper still, carboxylic acids were not quantified. Most volatile compounds were well represented by simulation for both distillations. It should be noted that the simulated distribution of 2-phenylethanol in wine distillation was close to the experimental results (Table 10), but the proportion of the 2phenylethanol in the residue (60.4 %) from the low wine distillation predicted by simulation (Table 11) was higher than the experimental value (36.8 %). A similar difference had already been observed for the low wine distillation in the 600 L alembic still, where the mass balance (Out/In) was 0.6. Despite the fact that acetaldehyde and 1,1-diethoxyethane were classified as light volatile compounds with type 1 concentration profiles, it is interesting to note that they were quantified in the residue of both wine and low wine distillations (Tables 10,11). These experimental results were probably due to analytical errors, due to the very low ethanol content of the residue (e.g. incorrect assignment of overlapping chromatographic peaks, slight delays in retention times due to matrix effects, etc.). This argument is supported by the study using the 600 L boiler, where the concentration of these compounds in the residue was zero, as predicted by the simulation. The same explanation of analytical errors may apply to the presence of propanal and butanal in the residue. For the wine distillation, even if ethyl caproate, ethyl caprylate, and ethyl caprate were classified as type 1 (Table 6), their high concentration in the head fraction of the wine distillation (Table 10) was unexpected and may also be due to analytical errors.

Conclusion

This study was conducted in 600 L (laboratory scale) and 2,500 L (industrial-scale) copper stills, both equipped with a Coriolis flowmeter on the distillate line after the alcoholmeter. Among the 69 volatile compounds initially analysed, 45 were quantified in the samples of the laboratory experiment. The concentration profiles as a function of decreasing distillate ABV were analysed to confirm and complete the classification proposed by Cantagrel [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]. Simulations were performed with ProSim® BatchColumn software. Twenty-six species were included in the simulation, considering the interaction parameters of the NRTL model, estimated from equilibrium data at high dilution [START_REF] Puentes | Review and thermodynamic modeling with NRTL model of vapor-liquid equilibria (VLE) of aroma compounds highly diluted in ethanol-water mixtures at 101.3 kPa[END_REF]. Good correlation was observed between experimental and simulated data in terms of ABV, mass flow, concentration profiles, concentrations of the volatile compounds and distribution in the various distillate fractions. This consistency also validated the NRTL coefficients determined by Puentes et al. [START_REF] Puentes | Review and thermodynamic modeling with NRTL model of vapor-liquid equilibria (VLE) of aroma compounds highly diluted in ethanol-water mixtures at 101.3 kPa[END_REF]. However, for some volatile compounds, it would be interesting to verify their equilibrium data at low ethanol concentrations to improve the consistency of simulated data with experimental findings. From an experimental point of view, the recorded data from a Coriolis flowmeter placed in the distillate flow (i.e. volume flow, temperature and density of the distillate over time) were sufficient to simulate the distillation process. Indeed, from the Coriolis flowmeter data, it was possible to calculate the mass flow and ethanol concentration over time and, consequently, to adjust the simulation parameters, such as heating power and internal reflux level.

In the future, it would be interesting to take into account the recycling of distillate fractions, as most distilleries blend the heads and tails into the wine and the seconds into the low wine. An experimental sampling and measurement programme, following successive distillations, could be designed with the aim to determine the heart composition and compare it to the base wine composition. Elucidating the behaviour of volatile compounds throughout the whole Charentais distillation process would highlight the main differences between batch distillations and continuous distillation in multi-stage columns, used for Armagnac and Calvados production. This knowledge would also be useful, in the longer term for developing optimisation strategies to control distillate composition, which is intimately linked to quality, as well as to reduce energy consumption. Figure 2. Concentration profiles of volatile compounds during distillation. Adapted from [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF][START_REF] Cantagrel | From vine to cognac, in Fermented Beverage Production[END_REF]. Figure 3. Diagram of the batch distillation unit simulated in the BatchColumn software. Figure 4. Volatile compounds with concentration profiles different of Cantagrel [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]. Figure 5. Experimental and simulated profiles during low wine distillation (600 L). Figure 6. Experimental and simulated concentration profiles for two esters. Figure 7. Experimental and simulated concentration profiles for four alcohols. Figure 8. Experimental and simulated concentration profiles for two acids with and without dimerisation. Figure 9. Experimental and simulated concentration profiles for furfural. Figure 10. Experimental and simulated profiles during wine distillation in 2,500 L still Figure 11. Experimental and simulated profiles during low wine distillation in 2,500 L still.

List of Figures

List of the tables

Table 1. Classification volatile compounds during batch distillation by Cantagrel (7) Table 2. Offline mass balance ethanol/water Table 3. Comparison between offline and online data ethanol measurements Table 4. Mass balances -comparison between offline and online data Table 5. Volatile compound mass balances (Out/In). Table 6. Classification of experimental data and comparison with Cantagrel (7) Table 7. Comparison with the classification by Puentes et al. [START_REF] Puentes | Simulation of spirits distillation for a better understanding of volatile aroma compounds behavior: Application to Armagnac production[END_REF] Table 8. Comparison between experimental and simulated concentration profiles Table 9. Distribution of volatile compounds during low wine distillation in a 600 L copper still Table 10. Distribution of volatile compounds during the wine distillation in a 2,500 L copper still Table 11. Distribution of volatile compounds during the low wine distillation in a 2,500 L copper still 4. Volatile compounds with concentration profiles different of Cantagrel [START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]. Figure 5. Experimental and simulated profiles during low wine distillation (600 L). Figure 6. Experimental and simulated concentration profiles for two esters. Figure 7. Experimental and simulated concentration profiles for four alcohols. Figure 8. Experimental and simulated concentration profiles for two acids with and without dimerisation. Figure 9. Experimental and simulated concentration profiles for furfural. Figure 10. Experimental and simulated profiles during wine distillation in 2,500 L still Figure 11. Experimental and simulated profiles during low wine distillation in 2,500 L still.

Figure 1 .

 1 Figure 1. Diagram of a traditional Charentais copper still.Figure2. Concentration profiles of volatile compounds during distillation. Adapted from[START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF][START_REF] Cantagrel | From vine to cognac, in Fermented Beverage Production[END_REF]. Figure3. Diagram of the batch distillation unit simulated in the BatchColumn software. Figure4. Volatile compounds with concentration profiles different of Cantagrel[START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]. Figure5. Experimental and simulated profiles during low wine distillation (600 L). Figure6. Experimental and simulated concentration profiles for two esters. Figure7. Experimental and simulated concentration profiles for four alcohols. Figure8. Experimental and simulated concentration profiles for two acids with and without dimerisation. Figure9. Experimental and simulated concentration profiles for furfural. Figure10. Experimental and simulated profiles during wine distillation in 2,500 L still Figure11. Experimental and simulated profiles during low wine distillation in 2,500 L still.

Figure 1 .Figure 2 .

 12 Figure 1. Diagram of a traditional Charentais copper still.

Figure 3 .Figure 4 .

 34 Figure 3. Diagram of the batch distillation unit simulated in the BatchColumn software.

Figure 5 .

 5 Figure 5. Experimental and simulated profiles during low wine distillation (600 L).

Figure 6 .

 6 Figure 6. Experimental and simulated concentration profiles for two esters.

Figure 7 .

 7 Figure 7. Experimental and simulated concentration profiles for four alcohols.

Figure 8 .Figure 9 .

 89 Figure 8. Experimental and simulated concentration profiles for two acids with and without dimerisation.

Figure 10 .Figure 11 .

 1011 Figure 10. Experimental and simulated profiles during wine distillation in 2,500 L still

Table 1 .

 1 Classification volatile compounds during batch distillation by Cantagrel[START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]

	Type Classification of compounds during wine distillation
	acetaldehyde, 1,1-diethoxy-ethane, 1,1-diethoxy-methyl-2-propane, ethyl
	acetate, ethyl propanoate, ethyl butanoate, ethyl caproate, ethyl caprylate,
	ethyl caprate, ethyl laurate, ethyl myristate, ethyl palmitate, ethyl stearate,
	ethyl oleate, ethyl linoleate, ethyl linolenate, isoamyl acetate, isoamyl
	caprate.
	Furfural
	Methanol
	1-propanol, 2-methyl-1-propanol, 1-butanol, 2-methyl-1-butanol, 3-methyl-1-
	butanol, 2-phenylethyl acetate.
	2-phenylethanol
	ethyl lactate, diethyl succinate
	Classification of compounds during low wine distillation
	acetaldehyde, 1,1-diethoxy-ethane, 1,1-diethoxy-methyl-2-propane, ethyl
	acetate, ethyl propanoate, ethyl butanoate, hexyl acetate, ethyl caproate,
	ethyl laurate, ethyl myristate, ethyl palmitate, isobutyl caprate, isoamyl
	acetate, isoamyl caprylate, isoamyl caprate, isoamyle myristate.
	2-phenylethanol
	methanol
	furfural, 2-phenylethyl acetate, ethyl lactate, diethyl succinate, caprylic acid,
	capric acid, lauric acid.
	1-propanol, 2-methyl-1-propanol, 1-butanol, 2-methyl-1-butanol, 3-methyl-1-
	butanol, ethyl stearate, ethyl oleate, ethyl linoleate, ethyl linolenate
	ethyl caprylate, ethyl caprate.

Table 2 .

 2 Offline mass balance ethanol/water

		Products	xm_eth M (kg)	Meth (kg)
	In	Low wine (LW)	0.257 584.75 150.53
		Heads (H)	0.702 3.48	2.45
		Heart (Ht)	0.648 181.07 117.36
		Seconds (S)	0.314 92.40	29.04
	Out	Tails (T) Petites-eaux (PE)	0.068 20.73 0.026 4.47	1.41 0.11
		D = ∑ distillate	0.498 302.15 150.37
		Residue (R)	0.000	282.60 0.15
		D+R	5	584.75 150.52
		Mass balance : LW -(D + R)		0.0	0.01

Table 3 .

 3 Comparison between offline and online data ethanol measurements

	Initial sampling time (min)	Final sampling time (min)	xm_offline sample measurement	xm_online average of three values	Difference (%)
	154	157	0.7086	0.7092	0.08
	214	217	0.6915	0.6923	0.12
	274	277	0.6652	0.6675	0.34
	334	337	0.6339	0.6341	0.04
	394	397	0.5863	0.5868	0.09
	490	493	0.4709	0.4737	0.59
	550	554	0.3708	0.3720	0.32
	610	614	0.2642	0.2616	0.99
	670	674	0.1525	0.1603	5.12

Table 4 .

 4 Mass balances -comparison between offline and online data

		xm (gethanol/gsolution)	M (kg)	Methanol (kg)
	Product	offline online	Difference (%)	offline online	Difference (%)	offline online	Difference (%)
	heart (Ht)	0.648 0.645	0.46	181.07 173.26	4.51	117.36 112.24	4.56
	seconds (S) 0.314 0.320	1.87	92.40 90.37	2.25	29.04 28.91	0.45
	tails (T)	0.068 0.073	6.85	20.73 20.35	1.87	1.41	1.49	5.40

Table 5 .

 5 Volatile compound mass balances (Out/In).

		Out/In	Out/In		Out/In
	Methanol*	0.98 Ethyl acetate*	1.07	Linalool	0.69
	1-propanol*	0.97 Ethyl lactate *	0.97	α-terpineol	0.87
	2-methyl-1-	0.99 Ethyl caproate *	1.09	(E)-nerolidol	1.53
	propanol* 2-methyl-1-	1.02 Ethyl caprylate*	1.14
	butanol* 3-methyl-1-	1.02 Ethyl caprate *	1.30	Formic acid	1.30
	butanol* 1-hexanol	1.07 Ethyl laurate *	1.50	Acetic acid	0.74
	(Z)-3-hexenol	1.24 Isoamyl acetate*	1.09	Isobutanoic acid 0.78
	2-phenylethanol* 0.60 Ethyl butanoate*	1.19	2-methylbutanoic	0.84
	1-tetradecanol	1.20 Hexyl acetate*	1.12	acid Isovaleric acid	0.88
		Isoamyl caprylate* 1.37	Lactic acid	3.36
	Acetaldehyde *	0.80 2-phenylethyl	0.70	Caproic acid	0.80
		caprylate Diethyl succinate* 0.87	Caprylic acid*	0.81
	Furfural*	0.96 Isoamyl laurate	2.45	Capric acid*	0.83
		Ethyl myristate*	1.86	Lauric acid*	0.98
	1,1-diethoxy-	0.78 Ethyl palmitate*	2.19	Myristic acid	0.92
	ethane *	Ethyl stearate*	2.25	Palmitic acid	0.87
		Ethyl oleate*	1.91
		Ethyl linoleate*	2.27
	* Volatile compounds analysed by Cantagrel (7)

Table 6 .

 6 Classification of experimental data and comparison with Cantagrel[START_REF] Cantagrel | A scientific approach to quality control for cognac spirits, in Distilled Beverage Flavour[END_REF]

	Type 1	Type 2	Type 7
	Acetaldehyde*	Formic acid°	1-propanol*
	1,1-diethoxy-ethane*	Acetic acid°	2-methyl-1-propanol*
	Ethyl acetate*	Lactic acid°	2-methyl-1-butanol*
	Ethyl butanoate*	Type 3	3-methyl-1-butanol*
	Ethyl caproate*	Methanol*	1-hexanol°
	Ethyl laurate*		(Z)-3-hexenol°
	Ethyl myristate*	Type 6	1-tetradecanol°
	Ethyl palmitate*	Caprylic acid*	(E)-nerolidol°
	Isoamyl acetate*	Capric acid*	α-terpineol°
	Isoamyl caprylate*	Lauric acid*
	Hexyl acetate*	Ethyl lactate*	Not well represented
	Ethyl caprylate xx	Furfural*	Linalool°
	Ethyl caprate xx	Diethyl succinate*	Myristic acid°
	Ethyl stearate xx	2-phenylethanol xx	Palmitic acid°
	Ethyl oleate xx	Isobutanoic acid°	2-phenylethyl caprylate°
	Ethyl linoleate xx	2-methylbutanoic acid°
	Isoamyl laurate°	Isovaleric acid°
		Caproic acid°
	[* Similar, xx different, and ° not analysed by Cantagrel (7)]

Table 7 .

 7 Comparison with the classification by Puentes et al.[START_REF] Puentes | Simulation of spirits distillation for a better understanding of volatile aroma compounds behavior: Application to Armagnac production[END_REF]

	LIGHT		INTERMEDIATE	HEAVY
	Type 1	Type 7	Type 3	Type 6	Type 2
	Acetaldehyde 1-propanol	Methanol	Caprylic acid	Formic
	Ethyl acetate 2-methyl-1-		Ethyl lactate	acid Acetic acid
	Ethyl	propanol 2-methyl-1-		2-phenylethanol
	butanoate Ethyl	butanol 3-methyl-1-		Isobutanoic acid
	caproate Isoamyl	butanol 1-hexanol		Isovaleric acid
	acetate Hexyl acetate (Z)-3-hexenol		Caproic acid
	Ethyl	1-tetradecanol
	caprylate Ethyl caprate

Table 8 .

 8 Comparison between experimental and simulated concentration profiles

	Overlaid curves (18 species)	Curves with the same shape but differences in maximum concentration (7)	Inconsistent curves (1)
	Methanol	Ethyl caprylate	2-methyl-1-butanol	Formic acid
	1-propanol	Ethyl caprate	1-hexanol
	2-methyl-1-	Isoamyl acetate Furfural
	propanol 3-methyl-1-	Ethyl butanoate Ethyl lactate
	butanol (Z)-3-hexenol	Hexyl acetate	Isobutanoic acid
	2-phenylethanol Acetaldehyde	Caproic acid
	1-tetradecanol	1,1-diethoxy-	Caprylic acid
	Ethyl acetate	ethane Acetic acid
	Ethyl caproate	Isovaleric acid

Table 9 .

 9 Distribution of volatile compounds during low wine distillation in a 600 L copper still

			Heads (%)	Heart (%)	Seconds (%) Tails (%) Residue (%)	Ht+S (%)
	Compound	CAS	exp sim exp	sim	exp	sim	exp sim exp	sim	exp	sim
	Ethanol	64-17-5	1.62 1.64 77.97 77.92 19.29 19.15 0.94 0.86 0.17 0.44 97.27 97.07
	Methanol	67-561	1.6	1.3 66.0 65.1	24.9 26.8 2.5 3.1	5.0	3.7 90.9 92.0
	1-propanol	71-23-8	1.7	1.7 80.4 83.0	13.8 15.0 0.3 0.3	3.8	0.1 94.2 98.0
	2-methyl-1-propanol 78-83-1	2.3	2.3 92.1 92.2	4.2	5.5 0.1 0.0	1.3	0.0 96.4 97.7
	2-methyl-1-butanol	137-32-6	1.9	1.4 92.6 87.2	3.9 11.3 0.1 0.0	1.5	0.0 96.5 98.6
	3-methyl-1-butanol	123-51-3	1.6	1.7 92.1 90.1	5.9	8.2 0.1 0.0	0.4	0.0 98.0 98.3
	1-hexanol	111-27-3	1.2	1.0 92.8 82.5	5.0 16.5 0.1 0.0	0.9	0.0 97.8 99.0
	(Z)-3-hexenol	928-96-1	0.9	1.1 73.0 81.1	19.4 17.6 0.5 0.1	6.3	0.0 92.4 98.8
	2-phenylethanol	60-12-8	0.2	0.0 18.2	6.6	37.6 34.0 10.4 12.2 33.7 47.1 55.7 40.6
	1-tetradecanol	112-72-1	5.7	0.9 84.3 98.3	6.6	0.8 1.4 0.0	1.9	0.0 90.9 99.1
	Ethyl acetate	141-78-6	11.2 10.5 82.7 89.6	1.2	0.0 0.3 0.0	4.6	0.0 83.9 89.6
	Ethyl lactate	97-64-3	0.2	0.2 22.7 24.8	34.2 40.4 7.2 9.7 35.7 24.9 56.9 65.2
	Ethyl caproate	123-66-0	11.3	6.4 85.7 93.6	1.1	0.0 0.2 0.0	1.6	0.0 86.8 93.7
	Ethyl caprylate	106-32-1	9.0	7.5 90.4 92.5	0.6	0.0 0.0 0.0	0.0	0.0 91.0 92.5
	Ethyl caprate	110-38-3	6.7	5.7 91.7 94.3	1.4	0.0 0.2 0.0	0.1	0.0 93.1 94.3
	Isoamyl acetate	123-92-2	11.8 10.7 86.6 89.3	0.4	0.0 0.2 0.0	1.0	0.0 87.0 89.3
	Ethyl butanoate	105-54-4	11.4 13.2 80.7 86.9	1.9	0.0 0.4 0.0	5.6	0.0 82.6 86.9
	Hexyl acetate	142-92-7	9.8	6.2 90.2 93.8	0.0	0.0 0.0 0.0	0.0	0.0 90.2 93.8
	Acetaldehyde	75-07-0	5.1 10.2 94.9 90.0	0.0	0.0 0.0 0.0	0.0	0.0 94.9 90.0
	Furfural	98-01-1	0.9	0.5 44.6 43.4	54.5 38.3 0.0 6.5	0.0 11.2 99.1 81.7
	1,1-diethoxy-ethane 105-57-7	4.2	8.4 95.8 91.7	0.0	0.1 0.0 0.0	0.0	0.0 95.8 91.8
	Acetic acid	64-19-7	0.3	0.1 10.8	6.3	8.8	9.7 4.0 3.4 76.2 80.5 19.6 16.0
	Isobutanoic acid	79-31-2	0.0	0.2 24.7 16.1	39.6 26.5 10.3 8.0 25.4 49.2 64.3 42.6
	isovaleric acid	503-74-2	0.0	0.3	0.0 25.1	38.9 39.4 14.0 9.3 47.2 25.8 38.9 64.5
	Caproic acid	142-62-1	0.1	0.0 22.6 11.6	49.0 58.1 11.8 13.4 16.5 16.8 71.6 69.7
	Caprylic acid	124-07-2	0.3	0.2 30.2 36.0	54.7 58.3 9.8 3.9	5.1	1.6 84.9 94.2

Table 10 .

 10 Distribution of volatile compounds during the wine distillation in a 2,500 L copper still

			Heads (%) Low wine (%) Residue (%)
		CAS	exp sim exp	sim	exp	sim
	Ethanol	64-17-5	1.3 1.4 98.2 96.6		0.5 2.1
	Methanol	67-56-1	0.9 0.6 96.3 84.8		2.8 14.6
	1-propanol	71-23-8	1.7 1.3 98.0 98.0		0.3 0.7
	1-butanol	71-36-3	2.4 1.7 97.6 98.2		0.0 0.1
	2-methyl-1-propanol 78-83-1	3.5 2.2 96.5 97.7		0.1 0.0
	2-methyl-1-butanol	137-32-6	3.4 2.7 96.5 97.3		0.1 0.0
	3-methyl-1-butanol	123-51-3	2.7 2.2 97.2 97.8		0.1 0.0
	1-hexanol	111-27-3	2.5 1.8 97.5 98.2		0.0 0.0
	(Z)-3-Hexenol	928-96-1	1.0 1.5 99.0 98.3		0.0 0.1
	2-phenylethanol	60-12-8	0.1 0.1 40.9 42.4	59.0 57.6
	Ethyl acetate	141-78-6	15.2 9.8 84.7 90.2		0.1 0.0
	Ethyl lactate	97-64-3	0.1 0.2 68.0 55.3	32.0 44.5
	Ethyl caproate	123-66-0	47.4 6.1 52.6 93.9		0.0 0.0
	Ethyl caprylate	106-32-1	51.9 17.1 48.1 82.9		0.0 0.0
	Ethyl caprate	110-38-3	52.9 29.9 47.1 70.1		0.0 0.0
	Acetaldehyde	75-07-0	3.6 10.6 89.0 89.4		7.4 0.0
	2-methylpropanal	78-84-2	0.4 15.9 99.6 84.1		0.0 0.0
	Propanal	123-38-6	1.6 5.3 86.3 94.7	12.1 0.0
	Butanal	123-72-8	3.1 10.7 82.0 89.3	14.9 0.0
	3-methylbutanal	590-86-3	5.4 15.6 93.6 84.4		0.9 0.0
	Pentanal	110-62-3	4.1 9.9 95.9 90.1		0.0 0.0
	Furfural	98-01-1	0.0 0.4 99.1 74.1		0.9 25.5
	1.1-diethoxyethane	105-57-7	3.3 4.2 81.2 95.8	15.5 0.0
	Linalool	78-70-6	2.3 4.8 96.7 95.2		1.0 0.0

Table 11 .

 11 Distribution of volatile compounds during the low wine distillation in a 2,500 L copper still Figure 1. Diagram of a traditional Charentais copper still. Figure 2. Concentration profiles of volatile compounds during distillation. Adapted from (7,8). Figure 3. Diagram of the batch distillation unit simulated in the BatchColumn software. Figure

		Heads (%) Heart (%)	Seconds (%) Tails (%)	Residue (%) Ht+S
		exp sim exp sim	exp sim	exp sim exp	(%) sim exp sim
	Ethanol	1.4	1.4	73.0 72.8	24.7 24.4 0.7	0.6	0.1	0.8	97.7 97.2
	Methanol	1.2	1.0	62.6 57.67 31.5 32.08 2.3	2.31 2.5	6.9	94.1 89.7
	1-propanol	1.4	1.5	80.9 78.7	17.3 19.6 0.1	0.2	0.2	0.1	98.3 98.2
	1-butanol	1.2	1.6	83.9 83.4	14.9 15.0 0.0	0.0	0.0	0.0	98.8 98.4
	2-methyl-1-propanol	2.1	2.1	91.5 89.8	6.4 8.1	0.0	0.0	0.0	0.0	97.9 97.9
	2-methyl-1-butanol	1.7	0.9	92.3 84.0	6.0 15.0 0.0	0.0	0.0	0.0	98.3 99.1
	3-methyl-1-butanol	1.4	1.6	90.5 87.0	8.1 11.4 0.0	0.0	0.0	0.0	98.6 98.4
	1-hexanol	0.9	0.9	90.8 78.5	8.3 20.5 0.0	0.0	0.0	0.0	99.1 99.1
	(Z)-3-Hexenol	0.5	1.0	71.7 76.7	27.8 22.2 0.0	0.1	0.0	0.0	99.5 98.9
	2-phenylethanol	0.0	0.0	11.5 4.7	44.7 28.0 6.9	6.9	36.8	60.4 56.2 32.7
	Ethyl acetate	9.8	9.7	89.4 90.3	0.7 0.0	0.0	0.0	0.1	0.0	90.1 90.3
	Ethyl lactate	0.1	0.1	23.2 15.6	47.1 35.6 7.9	6.3	21.6	42.4 70.4 51.2
	Ethyl caproate	11.6 5.9	88.4 94.0	0.0 0.1	0.0	0.0	0.0	0.0	88.4 94.1
	Ethyl caprylate	8.0	7.7	88.4 92.3	3.4 0.0	0.2	0.0	0.0	0.0	91.8 92.3
	Ethyl caprate	4.9	6.3	92.4 93.7	2.5 0.0	0.2	0.0	0.0	0.0	94.9 93.7
	Acetaldehyde	5.8	8.9	71.2 91.1	3.6 0.0	0.3	0.0	19.2	0.0	74.8 91.1
	2-methylpropanal	16.6 12.5 83.4 87.5	0.0 0.0	0.0	0.0	0.0	0.0	83.4 87.5
	Propanal	3.8	8.7	49.4 91.2	32.7 0.0	1.2	0.0	12.8	0.0	82.2 91.3
	Butanal	5.9	12.8 57.1 87.2	25.7 0.0	0.8	0.0	10.4	0.0	82.9 87.2
	3-methylbutanal	14.2 12.7 85.3 87.2	0.3 0.0	0.0	0.0	0.2	0.0	85.6 87.3
	Pentanal	5.4	10.8 57.4 89.2	22.4 0.0	0.8	0.0	14.0	0.0	79.8 89.2
	Furfural	0.3	0.4	45.8 35.5	44.5 40.6 4.1	4.6	5.2	18.9 90.4 76.1
	1,1-diethoxyethane	3.9	7.1	70.7 92.6	2.2 0.2	0.4	0.0	22.8	0.0	72.9 92.9
	Linalool	1.2	0.9	95.4 92.8	3.2 6.3	0.0	0.0	0.3	0.0	98.6 99.1

Acknowledgements

The authors would like to thank ProSim, for a free license of BatchColumn and technical advice during this study. They are also grateful to UNGDA (Union Nationale du Groupement des Distillateurs d'Alcool), for analytical support.


Louise Goupil
email: louise.goupil@ircam.fr

Sid Kouider
email: sid.kouider@ens.fr

Developing a Reflective Mind: From Core Metacognition to Explicit Self-Reflection

Keywords: metacognition, confidence, error monitoring, development, self-reflection

Metacognition is the ability to monitor and control cognition. Because young children often provide inaccurate metacognitive judgments when prompted to do so verbally, it has long been assumed that this ability does not develop until late childhood. This claim is now challenged by new studies using nonverbal paradigms and revealing that basic forms of metacognition-such as the ability to estimate decision confidence or to monitor errors-are present even in preverbal infants. This new line of evidence suggests that young children adapt to their environment not only by considering their physical and social surroundings but also by reflecting on their own cognitive states.

PSYCHOLOGICAL SCIENCE

Metacognition is our ability to reflect on our own mental representations to regulate cognition and optimize learning. Given metacognition's importance for education, its development has been studied intensively since the 1970s. For several decades, on the basis of studies that primarily relied on verbal reports, researchers argued that young children have poor and inefficient selfreflective abilities (for reviews, see [START_REF] Flavell | Development of children's knowledge about the mental world[END_REF][START_REF] Sodian | Metacognition in infants and young children[END_REF]). Yet it now appears that young children's limitations largely reflect an inability to provide accurate metacognitive reports rather than an absence of metacognition per se. Indeed, a new line of investigation relying on nonverbal paradigms and focusing on error monitoring (i.e., detecting one's own mistake) and decision confidence (i.e., estimating the probability that a choice was correct) has revealed that even preverbal infants engage in rudimentary forms of metacognition. In this article, we briefly review this burgeoning literature before proposing a framework that integrates recent findings with more traditional research documenting a slow and effortful development of explicit aspects of metacognition.

Early Forms of Metacognition

Over the last decade, several approaches have been elaborated to examine the development of metacognition without relying on verbal reports. A first approach revealed that when 3-to 5-year-olds are trained to use a nonverbal confidence scale depicting pictures of a confident or a doubtful peer, they quickly learn how to use it appropriately [START_REF] Ghetti | Feeling uncertain and acting on it during the preschool years: A metacognitive approach[END_REF]. That is, they provide higher confidence judgments for correct compared with incorrect responses, thereby displaying metacognitive sensitivity (i.e., accurate evaluations of their performances). One limit of this approach, however, is that nonverbal confidence scales still require an understanding of the concept of confidence: To use them appropriately, children must understand how the symbolic representations depicted by the scale map onto their internal states of uncertainty. Thus, this type of measure cannot be extended to test younger children, who cannot be verbally instructed on how to use such a scale.

Consequently, a second approach consisted of adapting behavioral methods stemming from the animal literature. In particular, in the opt-out paradigm [START_REF] Hampton | Multiple demonstrations of metacognition in nonhumans: Converging evidence or multiple mechanisms?[END_REF], participants are presented with a binary choice, either in a condition in which they have no option but to choose by themselves (forced choice) or in a condition in which they are also given the additional option to avoid responding (i.e., opt out). The rationale behind this paradigm is that if participants are able to monitor their own uncertainty, they should opt out specifically when they are uncertain, and thereby avoid making errors in choosing between the two alternatives. In such a task, 3-year-olds [START_REF] Balcomb | Three-year-old children can access their own memory to guide responses on a visual matching task[END_REF] and even 20-month-olds, who cannot yet verbally communicate their knowledge states [START_REF] Goupil | Infants ask for help when they know they don't know[END_REF], have been found to opt out appropriately to avoid errors. In one of these experiments, 20-month-old toddlers had to remember the location of a toy hidden for variable durations (to induce several levels of difficulty) before pointing to indicate where they wanted to recover it (Goupil et al., 2016). Although toddlers in a control group had no option other than to decide by themselves, toddlers in a test group were given the extra option of asking their caregiver for help through nonverbal communication. In this test group, only two training trials allowed toddlers to understand that they could withhold their response when feeling uncertain and ask their caregiver for help instead. Toddlers opted out not only to avoid difficult choices but also to avoid making errors irrespective of task difficulty; even in easy trials, toddlers in the test group made fewer errors than toddlers in the control group. Importantly, the rate of correct choices remained the same across the two groups, demonstrating that the toddlers opted out specifically to avoid making errors. Overall, the results revealed that toddlers were not merely responding on the basis of risk avoidance (e.g., escaping from difficult choices) or other first-order (i.e., cognitive) mechanisms. Rather, the results showed that children appraised their probability of success (i.e., asked for help when confidence was low; see Goupil et al., 2016, for further information).

Converging evidence comes from a third approach elaborated by comparative psychologists to measure metacognition while ruling out first-order interpretations: postdecision wagering. In such a task, 5-year-old children place higher bets on correct compared with incorrect responses [START_REF] Vo | Young children bet on their numerical skills: Metacognition in the numerical domain[END_REF]. Recently, we used a totally nonverbal variant of this paradigm to demonstrate that preverbal infants also display such metacognitive sensitivity (Goupil & Kouider, 2016). Infants made a decision (18-month-olds pointed to indicate the location of a hidden toy; 12-month-olds gazed toward the location of a briefly presented face) followed by a measure of postdecision persistence. This measure relies on the fact that one will wait longer for a reward if one thinks that one's response was correct; it has proven to be a reliable proxy for decision confidence in rats and chimpanzees [START_REF] Beran | Go when you know: Chimpanzees' confidence movements reflect their responses in a computerized memory task[END_REF][START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF]. Infants persisted more after making a correct compared with an incorrect decision. Importantly, they did so in the total absence of external feedback on their performance. Further, their persistence did not vary with stimulus or task properties (i.e., memorization delay for 18-month-olds; duration of the masked-face presentation for 12-montholds), but rather constituted a characteristic pattern in which confidence decreased with difficulty for correct judgments but increased for errors [START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF]. This suggests that infants' postdecision persistence does not simply reflect properties of external events (e.g., that the face was presented for 100 ms vs. 300 ms), but rather is contingent on the accuracy of their own decisions and reflects a subjective evaluation of performance (see Goupil & Kouider, 2016).

One might still argue that it remains debatable whether postdecision persistence unambiguously reflects second-order (i.e., metacognitive) computations [START_REF] Fleming | Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation[END_REF] and that, ultimately, it may be difficult to make conclusions about this issue on the basis of behavioral data alone. Fortunately, a fourth approach using neuroimaging provides more decisive evidence, as it allows researchers to dissociate brain areas associated with metacognitive versus cognitive processes. Regarding confidence, experiments with both rats and monkeys revealed that pharmacological inactivation of specific prefrontal areas impairs metacognitive sensitivity while sparing perceptual decisions [START_REF] Lak | Orbitofrontal cortex is required for optimal waiting based on decision confidence[END_REF][START_REF] Miyamoto | Causal neural network of metamemory for retrospection in primates[END_REF]. Similarly, metacognition causally involves the prefrontal cortex in human adults [START_REF] Fleming | The neural basis of metacognitive ability[END_REF], and decision confidence is automatically encoded in the ventromedial prefrontal cortex even when no reports are required from the participants [START_REF] Lebreton | Automatic integration of confidence in the brain valuation signal[END_REF]. The metacognitive system supporting error monitoring also elicits characteristic neural activity originating in the anterior cingulate cortex and reflected in an electroencephalographic potential called the error-related negativity (ERN). This component is thought to reflect a second-order, postdecisional mechanism signaling a mismatch between a just-made (incorrect) decision and the choice that should have been made on the basis of the available evidence [START_REF] Charles | Decoding the dynamics of action, intention, and error detection for conscious and subliminal stimuli[END_REF].

Consistent with the idea that core metacognition emerges early in development, studies have shown that some of these key prefrontal structures are already functional in infancy [START_REF] Dehaene-Lambertz | The infancy of the human brain[END_REF]Goupil & Kouider, 2016). Although unfortunately no study to date has measured neural markers of decision confidence in young children, a few studies have shown that the mechanisms of error monitoring subtended by the anterior cingulate cortex are already functional [START_REF] Tamnes | Performance monitoring in children and adolescents: A review of developmental changes in the error-related negativity and brain maturation[END_REF]. For instance, we recently found that following perceptual decisions, infants as young as 12 months of age display an ERN component after making an incorrect choice (Goupil & Kouider, 2016).

Core Metacognition

The evidence reviewed above suggests that human infants are already endowed with rudimentary forms of metacognition. Several lines of research suggest that these abilities primarily rely on a phylogenetically ancient metacognitive system that is not strictly tied to explicit reports. Indeed, as mentioned above, confidence and error monitoring are present in several other species, including rats and monkeys [START_REF] Hampton | Multiple demonstrations of metacognition in nonhumans: Converging evidence or multiple mechanisms?[END_REF]. Furthermore, neuroimaging and computational-modeling studies in human adults have revealed that confidence and error monitoring can be reduced to simple computational mechanisms [START_REF] Fleming | Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation[END_REF][START_REF] Yeung | Metacognition in human decision-making: Confidence and error monitoring[END_REF] and can automatically induce selfregulation even when they are not introspectively accessed [START_REF] Logan | Cognitive illusions of authorship reveal hierarchical error detection in skilled typists[END_REF][START_REF] Nieuwenhuis | Error-related brain potentials are differentially related to awareness of response errors: Evidence from an antisaccade task[END_REF]. Consequently, modern frameworks emphasize the fact that metacognition does not reduce to its explicit manifestations and stress the importance of implicit forms of self-monitoring and regulation [START_REF] Proust | Metacognition and mindreading: One or two functions[END_REF][START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF].

Building on these views, we here propose that starting in the first years of life, humans are already endowed with a system of core metacognition allowing them to automatically evaluate and regulate their own cognition. Core metacognition is complementary to other core systems that evolved to fulfill a dedicated function (e.g., representing objects) and on which flexible and explicit capacities build later in development [START_REF] Carey | The origin of concepts[END_REF][START_REF] Spelke | Core knowledge[END_REF]. Whereas other core systems represent properties of the external world, core metacognition specifically evolved to enable the monitoring of internal cognitive states, allowing endogenous engagement in self-regulation. Specifically, core metacognition encompasses any mechanism whereby a firstorder (cognitive) representation (e.g., a belief ) is assessed through a second-order (metacognitive) process that evaluates its quality (e.g., the likelihood that the belief is correct given the sensory evidence) without necessarily reaching awareness or being represented explicitly. Importantly, core metacognition is not simply equivalent to cognitive control: It concerns processes that use metacognitive representations (i.e., representations that contain some information about the properties of an underlying cognitive representation; e.g., the reliability of a belief ) for self-regulation (for more details, see [START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]; for an alternative position on this issue, see [START_REF] Carruthers | Mindreading underlies metacognition[END_REF]). Such a system would be present not only in humans but also in other animals whenever their neural architecture implements second-order structures evaluating first-order representations computed in sensorimotor and other associative areas.

As with other core systems, core metacognition can be considered an innate component relying on the maturation of dedicated brain structures that were shaped by evolution to constrain and optimize our representational space. However, the emergence of core metacognition is certainly protracted compared with other core systems, such as naive physics. Indeed, whereas sensory systems develop rapidly over the first year of life, the prefrontal cortex shows a dramatic increase in synaptic density and long-range connectivity only by the end of the first year [START_REF] Dehaene-Lambertz | The infancy of the human brain[END_REF] and substantially matures until adolescence [START_REF] Lebel | Microstructural maturation of the human brain from childhood to adulthood[END_REF]. Thus, although the structures that support core metacognition are already functional during the first years of life, they remain largely immature and undergo substantial development throughout childhood. So far, the youngest age at which core metacognition has been observed is 12 months (Goupil & Kouider, 2016). Whether the maturation of the prefrontal cortex and its long-range connections are sufficient to support metacognition in infants below 1 year of age remains an open question.

Signature Limits of Core Metacognition

If core metacognition emerges early, why does selfreflection appear so limited and unreliable until much later in childhood, as evidenced by decades of research in developmental psychology? As mentioned above, a first developmental constraint concerns the maturation of the prefrontal areas supporting core metacognition that undergo substantial maturation during childhood. But beyond this slow maturation, the core metacognitive system presents several intrinsic limitations. Below, we characterize these restrictions and detail how the core system interacts with the late-developing system of explicit metacognition.

Core systems are defined not only by a dedicated function but also by distinctive signature limits (e.g., the core system dedicated to object representations is limited to a set size of four; [START_REF] Carey | The origin of concepts[END_REF]. A first signature limit of core metacognition is that it is restricted to the evaluation of simple perceptual and value-based decisions. Even human adults tend to shift to inferential, heuristic strategies relying on the interpretation of cues (e.g., response times)-as opposed to relying on optimal metacognitive computations-to evaluate complex decisions and reasoning [START_REF] Ackerman | Meta-reasoning: Monitoring and control of thinking and reasoning[END_REF].

A second and perhaps more important limitation of core metacognition is that, in and of itself, it is not sufficient to enable a crucial aspect of human metacognition: the ability to share metacognitive representations with other people [START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. This latter capacity relies on explicit metacognition, a heterogeneous system that supports the exploitation of consciously accessed metacognitive representations. Decades of developmental research suggest that this system requires the development of several additional abilities [START_REF] Flavell | Development of children's knowledge about the mental world[END_REF][START_REF] Sodian | Metacognition in infants and young children[END_REF] that progressively come together to enable children to engage in explicit metacognition and provide accurate metacognitive reports.

First, the ability to focus on metacognitive monitoring and inhibit the use of irrelevant information when providing self-reports is limited in children. Their inaccurate reports reflect an inability to select relevant sources of information (e.g., error signals) while inhibiting inappropriate ones (e.g., desires to succeed). This often results in a "liberal" metacognitive bias with a high propensity to give affirmative (e.g., "Yes I know") responses (i.e., overconfidence; [START_REF] Butterfield | Developmental aspects of the feeling of knowing[END_REF][START_REF] Lipowski | Preschoolers can make highly accurate judgments of learning[END_REF]. Importantly, this bias is also often observed in human adults [START_REF] Dunlosky | Metacognition[END_REF]. Children still lack the executive functions necessary to inhibit such prepotent responses and provide accurate metacognitive reports. Indeed, executive functions, and in particular inhibitory control, do not consistently drive behavior before 4 years of age and continue developing until late childhood [START_REF] Posner | Developing attention: Behavioral and brain mechanisms[END_REF]; see also [START_REF] Roebers | Executive function and metacognition: Towards a unifying framework of cognitive selfregulation[END_REF], for more details on this issue).

Another developmental constraint concerns the ability to shift from an implicit to an explicit mode of processing. The latter implies directing attention toward metacognitive representations in order to globally broadcast them, thereby rendering them conscious and reportable [START_REF] Dehaene | What is consciousness, and could machines have it[END_REF]. To illustrate this contrast between implicit and explicit metacognition, we can return to the neural signatures of error monitoring mentioned above. Although neural markers of implicit error monitoring are observable in the anterior cingulate cortex following incorrect responses and are reflected in the ERN component regardless of introspective access to the error, conscious error detection elicits broader activations in a frontoparietal network that are reflected in a later component called the error-related positivity [START_REF] Yeung | Metacognition in human decision-making: Confidence and error monitoring[END_REF]. This pattern is similar to what is found for conscious versus unconscious perceptual processes in adults and infants, as the former generally involves early responses that are restricted in time and space whereas the latter evokes late, long-lasting, and widespread activations [START_REF] Dehaene | What is consciousness, and could machines have it[END_REF][START_REF] Kouider | A neural marker of perceptual consciousness in infants[END_REF]. Taken together, these data suggest that consciously accessing metacognitive representations involves additional neural processes entailing their global broadcasting. Given that the mechanisms enabling conscious access are already in place during the first year of life [START_REF] Kouider | A neural marker of perceptual consciousness in infants[END_REF], it is plausible that, even in infancy, metacognitive representations eventually become conscious when they represent a very strong signal (e.g., when a decision differs greatly from the option favored by sensory evidence). Yet it is also probable that, initially, metacognitive representations remain for the most part unconscious, either because their representational content remains too weak to capture attention or because they are overwritten by stronger alternative signals (e.g., external feedback, desires to succeed).

Finally, beyond executive functions and conscious access, language and culture play a crucial role in building the late-developing system [START_REF] Flavell | Development of children's knowledge about the mental world[END_REF][START_REF] Sodian | Metacognition in infants and young children[END_REF]. Interactions with caregivers shape and normalize explicit aspects of metacognition [START_REF] Roebers | Executive function and metacognition: Towards a unifying framework of cognitive selfregulation[END_REF][START_REF] Schneider | The development of metacognitive knowledge in children and adolescents: Major trends and implications for education[END_REF], and cultural norms deeply modulate individual strategies when giving self-reports [START_REF] Ma | Sociocultural patterning of neural activity during self-reflection[END_REF]. Learning a set of linguistic expressions that are efficient to share metacognitive representations in one's own culture is a challenge, as shown by the fact that the acquisition of cognitive-state vocabulary lags behind the acquisition of other lexicons [START_REF] Bretherton | Talking about internal states: The acquisition of an explicit theory of mind[END_REF] and that even in human adults, efficient communication of confidence depends on linguistic convergence [START_REF] Fusaroli | Coming to terms: Quantifying the benefits of linguistic coordination[END_REF]. Thus, whereas core metacognition is likely to be an innate module evolved through natural selection (like other core systems; [START_REF] Carey | The origin of concepts[END_REF], the emergence of explicit metacognition is largely constrained by the development of higher-order cognitive functions and culturally situated learning.

Conclusion

We suggest that a core system of metacognition appears very early in development, whereas explicit and humanspecific forms of metacognition slowly emerge to progressively allow children to communicate their metacognitive representations to others. In the field of mind reading, distinctions between implicit and explicit processes have also been used to account for the finding that young children's spontaneous behaviors reveal a sensitivity to other people's beliefs years before they manage to pass explicit false-belief tasks [START_REF] Low | Implicit and explicit theory of mind: State of the art[END_REF], and similar arguments have been made regarding empathy [START_REF] Heyes | Empathy is not in our genes[END_REF]. How are these two aspects of mind reading and empathy related to the two metacognitive systems? Some evidence suggests that explicit mind reading and metacognition develop together [START_REF] Flavell | Development of children's knowledge about the mental world[END_REF][START_REF] Lockl | Knowledge about the mind: Links between theory of mind and later metamemory[END_REF], but little empirical data are available regarding implicit aspects. A stimulating avenue for future research will thus be to examine the relationships between these constructs while considering the fact that they cannot be reduced to their explicit manifestations. Another promising avenue for future research will be to examine the potential involvement of metacognition in early learning. Metacognition is a privileged tool to optimally acquire new information, as it allows organisms to assess their own knowledge states and flexibly adapt their strategies to learn optimally in the absence of external feedback [START_REF] Guggenmos | Mesolimbic confidence signals guide perceptual learning in the absence of external feedback[END_REF]. At the earliest stages of development, when everything remains to be learned, this ability might be one of the key ingredients that allow young children to learn actively and optimally.

Recommended Reading Goupil, L., & Kouider, S. (2016). (See References). An empirical article revealing that core metacognitive mechanisms are already present in 12-and 18-month-old infants. Proust, J. ( 2012). (See References). An opinion piece discussing the possibility that metacognition does not reduce to its explicit manifestations. [START_REF] Shea | Supra-personal cognitive control and metacognition[END_REF]. (See References). An opinion article arguing that whereas implicit metacognition is shared with several animal species and serves functions at the subpersonal level, explicit metacognition has evolved to fulfill a social function. [START_REF] Sodian | Metacognition in infants and young children[END_REF]. (See References). A review on the development of metacognition in young children.

Action Editor

Randall W. Engle served as action editor for this article.

Acknowledgments

We thank J. Sackur and J. Proust for discussions on the manuscript.

Funding

This research was supported by funding from the Agence Nationale de la Recherche (ANR-17-EURE-0017) and from the European Research Council to S. Kouider (METAWARE project).

Declaration of Conflicting Interests

The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article.