Yohan Reynaud
email: yohan.reynaud@improve-innov.com

Caroline Buffière
email: caroline.buffiere@inra.fr

Jérémie David
email: jeremie.david@inra.fr

Benoit Cohade
email: benoit.cohade@inra.fr

Mélissa Vauris
email: melissa.vauris@inra.fr

Michel Lopez
email: michel.lopez@improve-innov.com

Isabelle Souchon
email: isabelle.souchon@inra.fr

Didier Dupont
email: didier.dupont@inra.fr

Didier Remond
email: didier.remond@inra.fr

Benoît Cohade

Didier Rémond

Temporal changes in postprandial intragastric pH: Comparing measurement methods, food structure effects, and kinetic modelling

Keywords: Postprandial intragastric pH pH, stomach, pH catheter, wireless pH capsule, food matrix

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Luminal pH plays a key role in the disintegration of food and tablets/capsules in the stomach because it impacts both the biochemical structure of ingested items and the activity of digestive enzymes. Changes in protein macrostructure occur in the gastric system. For example, casein clotting has been well described and is a consequence of acidification, which slows the gastric emptying of the resulting particles (compared to that of globulins that remain soluble) [START_REF] Boirie | Slow and fast dietary proteins differently modulate postprandial protein accretion[END_REF]. Thermodynamic laws act on protein structure [START_REF] Dill | Dominant Forces in Protein Folding[END_REF]; indeed, protein structure and protein behaviour in fluids are both affected by temperature, pH, and ionic forces [START_REF] Carbonaro | Chemico-physical and nutritional properties of traditional legumes (lentil, Lens culinaris L., and grass pea, Lathyrus sativus L.) from organic agriculture: an explorative study[END_REF]. It seems likely that plant proteins also undergo aggregation, coagulation, denaturation, solubilisation, or precipitation as a result of decreased pH in the gastric system, and all these processes have an impact on digestibility [START_REF] Carbonaro | Solubility-Digestibility Relationship of Legume Proteins[END_REF]. These phenomena can additionally affect gastric dynamics like antral grinding, which plays a major role in bolus deconstruction, notably that of solid foods [START_REF] Marciani | Assessment of antral grinding of a model solid meal with echo-planar imaging[END_REF].

The acidification that occurs in the stomach results from the secretion of hydrochloric (HCl) acid, which is known to induce protein denaturation independently of pH [START_REF] Goto | Acid-induced folding of proteins[END_REF]). In addition to its impact on protein structure, pH greatly affects pepsin activity [START_REF] Kazir | Extraction of proteins from two marine macroalgae, Ulva sp. and Gracilaria sp., for food application, and evaluating digestibility, amino acid composition and antioxidant properties of the protein concentrates[END_REF] and, hence, the appearance of peptides [START_REF] Kratzer | The effect of pH on the digestion of proteins in vitro by pepsin[END_REF]. The effect of gastric pH on protein digestion has been well described for βlactoglobulin gels [START_REF] Dekkers | Impact of gastric pH profiles on the proteolytic digestion of mixed βlg-Xanthan biopolymer gels[END_REF] and egg-white lysozyme [START_REF] Jiménez-Saiz | Immunological behavior of in vitro digested egg-white lysozyme[END_REF], but few data are available concerning the effect of gastric pH on plant protein digestion. It is also clear that the food matrix influences the release of nutritional components of interest [START_REF] Le Feunteun | Impact of the Dairy Matrix Structure on Milk Protein Digestion Kinetics: Mechanistic Modelling Based on Mini-pig In Vivo Data[END_REF]. It may likewise affect acid diffusion in food particles [START_REF] Marcotte | Acid diffusion in solid foods[END_REF] and broad temporal changes in intragastric pH. Indeed, postprandial intragastric pH kinetics appeared to be different between solid [START_REF] Malagelada | Measurement of gastric functions during digestion of ordinary solid meals in man[END_REF] and liquid foods [START_REF] Kalantzi | Characterization of the human upper gastrointestinal contents under conditions simulating bioavailability/bioequivalence studies[END_REF]. However, data are relatively scarce and to our knowledge no direct comparison on postprandial intragastric pH from fully characterized liquid and solid foods exists. Some studies deal with gastric emptying but the kinetics of intragastric pH are still not well explored. Alterations in protein structure induced by pH conditions in the stomach could also affect pepsin-protein interactions and thus the degree of proteolysis. For dairy gels, it has been shown that pepsin mainly hydrolyses proteins that are in contact with gastric juices [START_REF] Nau | Spatial-temporal changes in pH, structure and rheology of the gastric chyme in pigs as influenced by egg white gel properties[END_REF]; this result suggests that the structural changes in protein networks that occur during digestion may impact the overall degree of proteolysis and the generation of peptides.

In humans, dogs, and monkeys, pH has generally been measured via three techniques: 1) by chyme aspiration via a simple gastric tube [START_REF] Malagelada | Measurement of gastric functions during digestion of ordinary solid meals in man[END_REF]; 2) by using single [START_REF] Gardner | Measurement of meal-stimulated gastric acid secretion by in vivo gastric autotitration[END_REF] or multichannel [START_REF] Simonian | Regional Postprandial Differences in pH Within the Stomach and Gastroesophageal Junction[END_REF] transnasal catheters; or 3) by employing wireless capsules that were either allowed to move freely in the chyme [START_REF] Cassilly | Gastric emptying of a non-digestible solid: Assessment with simultaneous SmartPill pH and pressure capsule, antroduodenal manometry, gastric emptying scintigraphy[END_REF] or that were fixed to gastric mucosa [START_REF] Pandolfino | Ambulatory Esophageal pH Monitoring Using a Wireless System[END_REF]. Solubilisation and absorption are pH dependent [START_REF] Charman | Physicochemical and Physiological Mechanisms for the Effects of Food on Drug Absorption : The Role of Lipids and pH[END_REF], and in most studies in which postprandial intragastric pH was recorded, the objective was to analyse drug performance. More recently, food scientists have become interested in the spatial and temporal changes in intragastric pH during digestion and their relationship with intrinsic food characteristics, such as pH or buffering capacity [START_REF] Bornhorst | Gastric pH Distribution and Mixing of Soft and Rigid Food Particles in the Stomach using a Dual-Marker Technique[END_REF][START_REF] Nau | Spatial-temporal changes in pH, structure and rheology of the gastric chyme in pigs as influenced by egg white gel properties[END_REF]. However, we still lack detailed knowledge on how changes in pH are affected by the food matrix, and this information is crucial if we wish to establish in vitro digestion protocol as accurately as possible [START_REF] Kong | Disintegration of solid foods in human stomach[END_REF]. The study described here used a miniature pig model to characterise postprandial changes in intragastric pH following the ingestion of food-containing meals. Foods differed in macrostructure (liquid vs. solid) and plant protein type. We measured pH using the three techniques described above: the chyme sampling method, the pH catheter method, and the wireless pH capsule method.

Materials and methods

Animal Handling & Surgery

All procedures were carried out in accordance with European Union regulations (Directive 2010/63/EU) and were approved by the Auvergne Animal Experimentation Ethics Committee (CEMEAA) and the French government (APAFIS#11001-2017082312525562v2).

We used four adult Yucatan miniature pigs (8 months old; bodyweight = 24.9 ± 1.2 kg).

Three weeks before the experiment, each pig was surgically fitted with a cannula made of silicone rubber (internal diameter: 12 mm, external diameter: 17 mm). It was placed lateroventrally at the stomach's point of greatest curvature (Fig. 1). The animals were housed in separate pens in a ventilated room kept at a constant temperature (21°C). Between experimental trials, the pigs received 500 g/d of a concentrate feed containing 16% protein, 1% fat, 4% cellulose, and 5% ash (Porcyprima; Sanders Centre Auvergne, France). This food ration was distributed in two equal portions, given at 800 and 1600 hours, and the pigs had ad libitum access to water.

Experimental foods and meals

In this study, we focused on food types that differed in macrostructure (solid vs. liquid) and plant protein type. The two solid foods were tofu (soy-based protein) and seitan (wheatbased protein), and the two liquid foods were soymilk (soy-based protein) and a pea emulsion (legume-based protein) The soymilk, tofu, and seitan were of commercial origin. The soymilk had been ultra-pasteurised (UHT). The pea emulsion was made using a commercial pea protein isolate (Pisane M9, Lot: N16231O04, Cosucra, Belgium) and commercial soy oil (Emile Noël, France). Pre-emulsion was carried out using a disperser (T-50 Homogeniser, Ultra-Turrax, IKA, Germany) and a 15 G dispersing element (IKA, Germany); the process lasted 1 min and was run at 10,000 rpm. The solution was then homogenised twice using a benchtop homogeniser (PandaPLUS 2000, GEA, USA) operated at 1,000 bar. We added maltodextrine, sugar, and soy oil to the foods to equilibrate protein levels (30.0 g), fat levels (23.1 g), and calorie contents (980 kcal). The portions of soymilk, pea emulsion, tofu, and seitan weighed 1,162 g, 1,175 g, 435 g, and 290 g, respectively.

Measuring intragastric pH

Each pig was given each of the meal in a randomly determined order. Postprandial gastric pH kinetics were characterised using the three different methods. Each meal was administered on three different days, and a different measurement method was used each time. Pigs were allowed to recover for at least one day between tests. All the meals were ingested in less than 15 min.

Ex vivo method

Samples of chyme were collected at the following time points: 5 min before food intake and 20, 40, 60, 90, 120, 180, and 270 min after food intake. Each time, 10-20 ml of chyme was obtained through the cannula via gravitational forces. The pH of the sample was then measured ex vivo using an ISFET probe (1001-004 ISFET, Sentron, Holland) following manual agitation.

2.3.2

In situ method 1: pH catheter system A disposable dual-sensor catheter (Chongquing Jinshan Science & Technology Co. Ltd., China) was placed inside the stomach antrum via the cannula; the distance between the cannula and the two sensors were 8.5 cm (Cat1) and 13.5 cm (Cat2), respectively (Figure 1).

The catheter was linked to an external pH meter (Ohmega Medical Measurement Systems, Holland) placed on the animal's back, and pH was measured every 10 s. The pH meter was calibrated before the experiment, and its continued functioning was verified after the experiment. The measurements began at least 5 min before a meal was ingested and continued for 5 h of the postprandial period.

In situ method 2: wireless pH capsule

The wireless pH capsule (length: 2.8 cm; model JSPC-1, Chongquing Jinshan Science & Technology Co. Ltd., China) was attached to the cannula by a silk thread. The pH sensor was located at the top of the capsule and was thus positioned 13.0 cm downstream from the cannula, in the antrum (Figure 1). The calibration procedure was the same as for the pH catheter. Measurements of pH were obtained every 3 s.

Physicochemical analysis

Just before the foods were given to the pigs, their pH was measured using the ISFET probe (2.3.1) following manual agitation.

For the tofu and seitan, median particle sizes (i.e., the theoretical sieve through which 50% of the mass of particles could pass) were assessed as described by Peyron et al. [START_REF] Peyron | Particle size distribution of food boluses after mastication of six natural foods[END_REF].

The buffering capacity (BC) of the meals was estimated under in vitro gastric conditions for three pH ranges ([2.00-4.00]; [4.00-6.00]; [6.00-initial pH]). Foods were added to 50 ml of simulated gastric fluid (SGF), which was made based on the method developed by INFOGEST [START_REF] Minekus | A standardised static in vitro digestion method suitable for food -an international consensus[END_REF]; the objective was a final protein concentration of 50 mg.mlSGF -1 . The mixtures were kept at 37°C using jacketed beakers equipped with magnetic stirring bars, and then, using a 1 M HCl solution (Lot: 00718, Grosseron SAS, France), pH was adjusted drop by drop to reach 2.00, 4.00, or 6.00. Glass electrodes (N61, SI Analytics GmbH, Germany) with thermometers (W2180-KOAX, SI Analytics GmbH, Germany) and pH meters (Titroline 7000, SI Analytics GmbH, Germany) were used. BC was calculated using the equation described by Gaucheron [START_REF] Gaucheron | Influence of pH on the heat-induced proteolysis of casein molecules[END_REF]Van Slyke, 1922):

BC(pH) = ( )×( ) (
) ×( )

Data and statistical analysis

For the two in situ methods, pH measurements were averaged across 6-min intervals (pH catheter method: 36 measurements; wireless pH capsule method: 129 measurements). The kinetics of intragastric pH were analysed using the repeated option of the SAS PROC MIXED procedure (SAS University Edition, v. 3.71; SAS Institute Inc., Cary, NC, USA); pig identity was treated as a random effect, whereas time, measurement method, food-type, and their interactions were independent factors. Significant interactions between time and method and between time and meal were found, so the LSMEANS procedure was used to test differences between methods and food types at specific time points. A similar procedure was used to analyse the BC data. The alpha level for our statistical tests was 0.05. The data presented in the results are the means ± standard error of the mean (SEM).

Regression equations

The regression analysis of the pH data obtained using the two in vivo methods was performed using XLSTAT (XLSTAT Quality v. 18.07, Addinsoft, France). The values employed were the means for the 6-min intervals. In the case of the pH catheter method, it was also necessary to average across the two sensors.

An eight-parameter modified logistic regression was utilised:

( ) = !"# + % &'( ) *+,-
./ (0 1 (2 1 32)) / (0 4 (232 4 )) 5 + 6

7 *+,--9:; <× ln?1+A - 2 B C D ln(2) F (1)
where was time since ingestion; pHMIN was the basal pH; pHMAX was the maximum pH;

pHInfl was the inflection pH (the pH where a slow change of trend was observed, see Supplementary data 1 for graphical explanation); t1, t2, and t3 were the times to, respectively, the initial rise in pH, global acidification, and acidification after inflection; and k1 and k2 were the constants associated with the neutralisation and acidification rates, respectively.

Supplementary data 1 illustrate the contribution of parameters to the shape of some modelled curves.

Results

Meal characteristics and basal pH

The pH values of the soymilk, pea emulsion, tofu, and seitan were 6.9 ± 0.2, 7.9 ± 0.1, 5.8 ± 0.1, and 5.6 ± 0.0, respectively. The final median particle sizes were 5.1 ± 0.1 mm for the tofu and 3.5 ± 0.1 mm for the seitan (n = 4 trials).

The BCs of the different food types are presented in Table 1. The soymilk, pea emulsion, and tofu exhibited the same profile: BC increased while pH decreased. Only the seitan displayed a higher BC at a pH of 6 than at a pH of 4. Differences between meals were observed for the three pH ranges. In generally, the BCs of food types with soy-based protein were higher. When the foods were ordered based on BC, the result was as follows: tofu > soymilk > pea emulsion ~ seitan.

Postprandial kinetics of intragastric pH

Comparison of the results for the two pH catheter sensors

The intragastric pH levels recorded with the two sensors (Cat1 and Cat2) during the postprandial period are illustrated in Figure 2. The overall statistical analysis (the final model included time, meal, sensor identity, meal*sensor identity) found that there was a significant difference between the pH levels recorded by Cat1 versus Cat2 (P < 0.001); there was also an interaction between sensor identity and the nature of the meal (P < 0.001). The basal (premeal) pH recorded by Cat2 was consistently higher than that recorded by Cat1 (P < 0.05).

When all the measurement data (across experimental days) were grouped, basal pH was 2.6 ± 0.4 with Cat1 and 4.9 ± 0.6 with Cat2. When food-type-specific analyses were performed, there was a difference between the pH levels recorded by Cat1 and Cat2 for the solid meals (tofu and seitan) (P < 0.0001) but not for the liquid meals (soymilk and pea emulsion). For the solid meals, the pH from Cat2 was higher than the pH from Cat1 for the entire postprandial period. No significant interaction between time and sensor identity was observed, which showed that the two sensors both picked up on the general patterns of temporal changes in postprandial pH.

Comparison of the results obtained with the pH catheter method and the wireless pH capsule method

First, the results from the pH catheter's two sensors were averaged. The postprandial intragastric pH levels associated with the four meals, as measured via pH catheter or wireless pH capsule, are shown in Figure 3. The overall statistical analysis (the final model included time, food type, measurement method, and food type*measurement method) found that there was a significant difference between the two measurement methods (P < 0.001) as well as a significant interaction between food type and measurement method (P < 0.001). The methods differed in their estimates of pH only in the case of the solid meals. Once again, no significant interaction between time and measurement method was observed across the different food types. Also, after solid meal intake, the gastric pH observed an inflection point, i.e. a given pH reached at a considered time, after which the pH variation was slower than if the pH kinetics was conventional sigmoid. Whereas pH kinetics of liquids had a sigmoid shape, pH kinetics of solids decreased more rapidly.

Comparison of the results from the ex vivo and in situ measurement methods

To compare the pH levels obtained using the different methods, measurements made at the time points corresponding to chyme sampling (n = 8) were employed (Figure 3). For the pH catheter data, the mean of the two sensors was used. When measuring basal pH, the ex vivo method yielded similar results to the in situ methods (P = 0.6289). The only interaction between time and measurement method was observed for the soymilk (P = 0.0430): there was a quicker drop in pH seen with the ex vivo method. For the other food types, changes in intragastric pH were similar over time for the ex vivo and in situ methods. That said, the ex vivo method yielded consistently lower pH values during the plateau associated with the liquid meals (Figure 3). For the soymilk, pea emulsion, and tofu, the mean relative standard error (SEM*100/mean; which expressed the degree of variability among the pigs) associated with the pH catheter values was lower than that associated with other methods (Figure 3).

Effects of food type

The Meal comparisons depicted in Figure 4A are based on the pH catheter data (averaged across Cat1 and Cat2), but similar conclusions were reached when data from the wireless pH capsules were employed (not shown). Time, food type, and the time*meal interaction were all significant (P < 0.001). When comparisons were made at each time point, there were no significant differences in intragastric pH between the soymilk and the pea emulsion. Tofu and seitan exhibited a single significant difference, which occurred at 6 min (P = 0.0154). The analysis revealed that, from 18 to 168 min after food ingestion, pH was significantly higher for liquid meals (soymilk and pea emulsion) than for solid meals (tofu and seitan) (P < 0.05).

While pH rapidly decreased exponentially in the case of the solid meals, the liquid meals reached a plateau where pH was 7. This plateau lasted about 2 h before pH levels declined.

After 192 min, pH was approximately 2 for all four food types.

Modelling the kinetics of postprandial intragastric pH

Table 2 summarises the parameter values obtained using the pH catheter method and the wireless pH capsule method. The curves obtained by modelling the pH catheter measurements are presented in Figure 4B. It was assumed that there was no inflection period when the regression analysis of the intragastric pH kinetics associated with the liquid meals was performed. Because the basal pH, pHMIN, was set to equal pHInfl, no t3 was required. After the initial rounds of regression, k1 was no longer sensitive to changes in conditions. To allow comparisons of t1 across models, k1 was fixed at 0.40.

The value of pHMIN was estimated from the regression analysis and was situated between 1.0 and 1.8, which is slightly lower than the basal pH as measured via in situ methods (about 3.5). Regardless of nature of the food type, pHMAX was quickly reached (t1 was always < 5 min). Estimates of pHMAX were similar for the different measurement methods (7.1-7.3 for the soymilk and pea emulsion and 6.5-6.8 for the seitan), except in the case of the tofu, for which the wireless pH capsule method yielded lower values than pH catheter method (4.5 vs 7.7), matching what was seen in the experimental data.

Based on the first derivative (data not shown), t2 seemed to be the time point at which the decrease in pH was at its fastest. This parameter was good at discriminating between liquid and solid meals. Indeed, for the tofu and seitan, t2 was less than 28 min. In contrast, for the soymilk and pea emulsion, t2 lay between 147 and 183 min.

Discussion

The translation to human of these results obtained in a pig model must be done cautiously. Indeed, although the size and the geometry of the minipig stomach is similar to those of the human stomach (1 -1,6 l), some physiological differences remain: proportion of the cardiac mucosa in the stomach is higher in pigs than in humans [START_REF] Kararli | Comparison of the gastrointestinal anatomy, physiology, and biochemistry of humans and commonly used laboratory animals[END_REF] and the gastric emptying time is considered as slower in pig (2 h -24 h) than in humans (10 min -2 h) [START_REF] Henze | The pig as a preclinical model for predicting oral bioavailability and in vivo performance of pharmaceutical oral dosage forms : a PEARRL review[END_REF]. Moreover, the position and the orientation of the pig stomach is different from those of the human stomach, because of the difference of the whole-body position during the digestion. In fact, the lengthwise axis (fundus -antrum axis) of the human stomach is vertical, whereas the lengthwise axis of the pig stomach is horizontal.

Comparison of the pH measurement methods

In this study, three methods for measuring postprandial intragastric pH were compared:

the ex vivo sampling of chyme, the in vivo use of pH catheters, and the in vivo use of wireless pH capsules. Compared to the ex vivo method, the in vivo pH catheter and wireless pH capsule methods both allowed pH to be recorded continuously. For the solid meals (tofu and seitan), no significant differences were seen between the ex vivo and in vivo measurements.

However, for the liquid meals, differences were observed during the pH plateau. After the pigs consumed liquid foods, their stomachs were pretty much filled and contained heterogenous contents. As a result, when the cannula was opened, the gastric contents leaked out in large quantities. This fact may have led to the lower buffering capacities that were observed and could explain the lower pH values obtained with the ex vivo method. Because the in situ methods did not disturb the pigs or their stomach contents and because they also allowed the continuous measurement of pH, they appear to be better suited to monitoring postprandial intragastric pH. Moreover, because they displayed less variability, the pH catheter data were more useful for evaluating the effect of meal on temporal changes in postprandial intragastric pH. With the seitan, the ex vivo method provided more repeatable results than did the in situ methods, probably because the chyme is more heterogeneous in the core of the antrum than near the mucosa. Lastly, in the wireless pH capsule method, no coupled external devices are needed, which is less constraining for the animal.

Overall, the pH catheter method and the wireless pH capsule method revealed similar temporal patterns in postprandial intragastric pH. However, in the case of the solid meals, the capsule consistently found lower pH values than did the catheter. This result is consistent with the results of a previous study that also compared the two methods [START_REF] Caparello | Traditional vs wireless intragastric pH monitoring: Are the two techniques comparable?[END_REF]. In that study, the capsules were attached to the stomach wall, and the authors interpreted the differences as resulting from the proximity of the pH sensor to the stomach parietal cells,

where HCl is secreted [START_REF] Schubert | Control of Gastric Acid Secretion in Health and Disease[END_REF]. The lower pH values recorded with the capsule were thus probably due to the lower BC of food occurring near the mucosa, in comparison to food found in the middle of the gastric compartment. This same result could arise for very heterogeneous chyme [START_REF] Bornhorst | Gastric pH Distribution and Mixing of Soft and Rigid Food Particles in the Stomach using a Dual-Marker Technique[END_REF]. Thus, we could hypothesise that, in this study, the wireless pH capsule remained close to the stomach wall while the pH catheter, because of its rigidity, occupied a more central position within the stomach.

pH spatial distribution and chyme heterogeneity

It is noteworthy that the pH catheter's two sensors obtained significantly different measurements. Both before and after food ingestion, the sensor at the tip of the catheter yielded higher pH values. These differences between the two sensors, and the differences between the catheter and the capsule, clarify the spatial distribution of pH values both in the empty stomach and the stomach after it is filled with a solid meal; similar findings were seen in a previous study for soft and rigid foods [START_REF] Bornhorst | Gastric pH Distribution and Mixing of Soft and Rigid Food Particles in the Stomach using a Dual-Marker Technique[END_REF][START_REF] Nau | Spatial-temporal changes in pH, structure and rheology of the gastric chyme in pigs as influenced by egg white gel properties[END_REF].

Unfortunately, in this study, we were unable to precisely determine the positions of either the catheter sensors or the capsule. Thus, although we tried to direct the catheter towards the pylorus, we do not know if it was propelled backwards towards the fundus, which might explain the higher pH values found by the sensor at the catheter's tip in the case of the solid foods. Indeed, when a similar catheter (albeit equipped with four sensors) was used in humans, a gradient in intragastric pH was observed following complete food ingestion: the pH in the proximal part of the stomach was higher than the pH in the antrum (mid/distal region) [START_REF] Simonian | Regional Postprandial Differences in pH Within the Stomach and Gastroesophageal Junction[END_REF]. In our study, no such difference was seen for liquid meals because of the large amount of liquid ingested, which rendered the gastric contents more homogeneous.

Because the ultimate goal of the study was to characterise mean changes in intragastric pH over the postprandial period, it seemed important to use the mean of the values recorded by the two sensors when analysing the effects of food type on gastric pH kinetics.

Effects of food type on gastric pH kinetics

This study found clear differences in the postprandial pH kinetics elicited by liquid versus solid meals. While pH rapidly decreased exponentially in the case of the solid meals, there was an S-shaped drop in pH associated with the liquid meals, where pH plateaued for more than 2 hours. These results were similar to those obtained in previous studies in humans [START_REF] Dressman | Comparison of Canine and Human Gastrointestinal Physiology[END_REF][START_REF] Gardner | Measurement of meal-stimulated gastric acid secretion by in vivo gastric autotitration[END_REF][START_REF] Malagelada | Measurement of gastric functions during digestion of ordinary solid meals in man[END_REF]). An initial plateau has also been observed for liquid meals [START_REF] Kalantzi | Characterization of the human upper gastrointestinal contents under conditions simulating bioavailability/bioequivalence studies[END_REF]. This plateau is probably caused by three phenomena: i) Meal volume and viscosity affect stomach distention and, as a consequence, the acid secretion rate [START_REF] Marciani | Effect of meal viscosity and nutrients on satiety , intragastric dilution , and emptying assessed by MRI[END_REF]; ii) proteins have higher buffering capacities when they occur in solutions than when they occur as solids, even if this pattern was not seen in our study; iii) pH results from the relative activity of H3O + and OH -ions in solution (aqueous phase) and, thus, ions inside the food network are not measured.

Meal volume

The volumes of the solid and liquid meals were different. The volume of the liquid meals was 1.1 L, and their stomach filling rate was therefore 80% (total stomach volume = 1.31 ± 0.09 L; measured in six Yucatan miniature pigs weighing 25.4 ± 0.6 kg). In contrast, the filling rate for the solid foods was less than 40%. Meal volume has been shown to affect gastric emptying for liquid, but not solid meals.

Buffering capacity of meals

Buffering capacity is linked to the ability of acidic amino acids (i.e., those with side chains containing carboxylic groups) to trap the hydronium ion [START_REF] Mat | Monitoring protein hydrolysis by pepsin using pH-stat: In vitro gastric digestions in static and dynamic pH conditions[END_REF]. Thus, for similar levels of protein, BC could be higher if quantities of glutamic acid and aspartic acid are greater. However, in this study, although wheat proteins contain more acidic amino acids (about 40%) than do soy proteins (about 30%), the seitan had a lower BC than did tofu. This finding underscores that, in addition to primary structure, protein conformation matters; it is important to consider whether or not acidic side chains are exposed when in the liquid phase. Furthermore, common methods for identifying amino acids cannot distinguish between glutamine and glutamic acid or between asparagine and aspartic acid.

The amount and output of acid secretions have been shown to directly correlate with a food's initial BC (regardless of protein amount or source) (Williams, Forrest, & Campbell, 1968) and with postprandial gastric acidity during the first five hours of digestion [START_REF] Gardner | Measurement of meal-stimulated gastric acid secretion by in vivo gastric autotitration[END_REF]. BC may thus help to shed light on changes in gastric pH over time. The longer plateau observed for the soymilk compared to the pea emulsion could be explained by the greater BC associated with the initial pH and the pH of 4 for soymilk compared to the values for the pea emulsion. The same explanation could hold when interpreting the differences seen between the seitan and the tofu at the beginning of digestion. While both food types had a similar initial pH (~ 5.7), the tofu and the seitan reached maximal pH levels of 5.8 and 6.7, respectively (pH catheter data). Tofu's higher BC could explain its resistance to neutralisation in mouth and its subsequent acidification in the stomach. Furthermore, tofu had a higher BC than did soymilk, although both food types are soy protein based; this dissimilarity could have resulted from differences in macrostructure.

Lastly, BC could change during gastric digestion because of proteolysis and the addition of endogenous components. Previous research found that gastric BC increased after meal intake [START_REF] Fordtran | Gastric Acid Secretion Rate and Buffer Content of the Stomach after Eating[END_REF], and, two hours later, its value was inversely correlated with the peak acid secretion rate. The authors of the study attributed this relationship to quicker gastric emptying, given that high acidity generally slows down the rate of gastric emptying [START_REF] Fordtran | Gastric Acid Secretion Rate and Buffer Content of the Stomach after Eating[END_REF]. Moreover, phase separation might have occurred, and thus buffer emptying would not have followed total gastric emptying.

Food particle size

Food particle size also plays a role in determining BC and thus gastric pH. Previous research explored the influence of particle size on pH distribution patterns in the pig stomach during digestion and showed that fine particles (D50: ~ 0.5 mm versus ~1.0 mm) promoted the diffusion of gastric acid within the stomach, which led to a more homogeneous pH [START_REF] Hunt | The role of the antrum in determining the acid secretory response to meals of different consistency[END_REF][START_REF] Mößeler | Effects of grinding intensity and pelleting of the diet -fed either dry or liquid -on intragastric milieu, gastric lesions and performance of swine[END_REF][START_REF] Mößeler | Impact of diets' physical form (particle size; meal/pelleted) on the stomach content (dry matter content, pH, chloride concentration) of pigs[END_REF][START_REF] Regina | Changes in gastric contents in pigs fed a finely ground and pelleted or coarsely ground meal diet[END_REF].

Another study examined acid diffusion patterns in an artificial stomach after the ingestion of solid meal and found similar results, with variation stemming mostly from particle size and meal temperature [START_REF] Marcotte | Acid diffusion in solid foods[END_REF]. In this study, the solid meals contained larger particles. Consequently, a more pronounced and persistent pH gradient within the stomach could be expected.

Regression analysis of gastric pH kinetics

Even if temporal changes in gastric pH were significantly different for liquid versus solid meals, we found that system kinetics could be described using the same equation, which made it possible to extract specific parameters. A recent modelling study [START_REF] Sams | Relevant pH and lipase for in vitro models of gastric digestion[END_REF] that employed data from previous research showed that the kinetics of postprandial pH could be described with an exponential function or a polynomial equation.

When we tested the fit between our pH data and a variety of functions, the coefficient of determination was higher for degree-four and degree-five polynomials and four-parameter logistic regressions (R² ~ 0.96-0.99) than for exponential functions (R² ~ 0.91-0.92). The most relevant regressions appeared to be those that included food type and/or physiological parameters, which is what Weinstein and colleagues found (Weinstein et al., 2013). They modelled postprandial gastric pH in healthy humans following the ingestion of liquid meal: like us, they observed a plateau in pH and determined that its duration depended on buffer concentration, gastric acid secretion rate, and gastric emptying time.

Conclusions

We compared the results yielded by three different methods for monitoring intragastric pH during the postprandial period in miniature pigs that had ingested liquid and solid meals.

Across food types, in situ pH measurements obtained with pH catheters or wireless pH capsules were robust and consistent with each other. In contrast, the ex vivo method, in which chyme was sampled via the cannula, seemed to be poorly suited for following intragastric pH levels during the digestion of liquid meals. Both in situ methods captured the food-typemediated differences in postprandial intragastric pH over time: for liquid meals, pH plateaued for approximately 2 h, but for solid meals, pH decreased quickly and immediately. Food macrostructure was thus clearly a determinant factor, as was food buffering capacity, which was assessed in vitro. Modelling temporal patterns of intragastric pH for liquid and solid meals will help clarify changes in pepsin activity in the stomach and the consequences for protein digestion. In addition, the equations that we defined could be used to differentially program an in vitro digester based on the food matrix to be studied.

Van Slyke, D. D. (1922). On the measurement of buffer values and on the relationship of buffer value to the dissociation constant of the buffer and the concentration and reaction of the buffer solution. Journal of Biological Chemistry,52(2), 525-570. Weinstein, D. H., Chow, C. C., Foruraghi, L., Zhao, X., Wright, E. C., & Whatley, M. (2013).

A new method for determining gastric acid output using a wireless pH-sensing capsule.

Alimentary

Tables legends

Table 1: Buffering capacity (BC) of the different food types for different pH ranges (n = 3, expressed in terms of 10 3 ; mean ± SEM). The results were analysed using a repeatedmeasures ANOVA where food type and pH*food type were fixed effects, and pH was a repeated effect. As all the effects were significant (P < 0.0001), a post-hoc LSMEANS test was performed. The letters (a through e) indicate when significant differences were present (P < 0.05). 1

Table 1

Buffering capacity (BC) of the different food types for different pH ranges (n = 3, expressed in terms of 10 3 ; mean ± SEM). The results were analysed using a repeated-measures ANOVA where food type and pH*food type were fixed effects, and pH was a repeated effect. As all the effects were significant (P < 0.0001), a post-hoc LSMEANS test was performed. The letters (a through e) indicate when significant differences were present (P < 0.05).

pH

Figure legends Figure 1

 1 Figure legends

Figure 2

 2 Figure 2 Temporal changes in postprandial intragastric pH (n = 4 animals) characterised with each of the catheter sensors over a 300-min period after (A) seitan, (B) tofu, (C) pea emulsion, and (D) soymilk ingestion. The measurements were averaged over 6-min intervals. The circles are the means for each interval, and the bars represent the SEM. The filled dark circles represent the data from the first pH catheter sensor (Cat1). The empty grey circles represent the data from the second pH catheter sensor (Cat2).

Figure 3

 3 Figure 3Temporal changes in postprandial intragastric pH (n = 4 animals) characterised with the three measurement methods over a 300-min period following meal ingestion. The effects of different food types were tested: (A) seitan, (B) tofu, (C) pea emulsion, and (D) soymilk. For the in situ methods, measurements were averaged over 6-min intervals. The circles are the means for each interval, and the bars represent the SEM. The filled dark circles represent the data from the pH catheter method (averaged across the two sensors; Cat). The empty grey circles represent the data from the wireless pH capsule method (WCap). The filled grey squares represent the data from the ex vivo chyme sampling method (ExV). The stars indicate time points where there were significant differences. For each food type, relative standard error (SEM*100/MEAN) was calculated for each 6-min interval for the Cat and WCap methods and for the chyme sampling points for the ExV method. The results are represented in the boxplots below. The values near the boxplots are the mean relative standard errors.

Figure 4 (

 4 Figure 4 (A) Temporal patterns in postprandial intragastric pH (n = 4 animals) characterised with the pH catheter method (Cat; pH values averaged across the two sensors) over a 300-min period following meal ingestion. Measurements were averaged over 6-min intervals. The circles are

Figure 1

 1 Figure 1Approximate position of the pH monitoring equipment within the stomach. The dark red line represents the pH catheter, and the two sensors are depicted using circles (Cat1 & Cat2). The thin blue line is the silk thread that was attached to the wireless pH capsule, which is represented by a rectangle (WCap). Cat1 and Cat2 were connected to a pH meter via the catheter. In contrast, the pH capsule wirelessly transmitted data to a pH meter.

Figure 2

 2 Figure 2 Temporal changes in postprandial intragastric pH (n = 4 animals) characterised with each of the catheter sensors over a 300-min period after (A) seitan, (B) tofu, (C) pea emulsion, and (D) soymilk ingestion. The measurements were averaged over 6-min intervals. The circles are the means for each interval, and the bars represent the SEM. The filled dark circles represent the data from the first pH catheter sensor (Cat1). The empty grey circles represent the data from the second pH catheter sensor (Cat2).

Table 2 :

 2 Parameter values and coefficients of determination obtained from the logistic regression analysis of intragastric pH kinetics for the different food types as assessed via the two in vivo measurement methods (pH catheter = Cat, wireless pH capsule = WCap).

Table 2

 2 Parameter values and coefficients of determination obtained from the logistic regression analysis of intragastric pH kinetics for the different food types as assessed via the two in vivo measurement methods (pH catheter = Cat, wireless pH capsule = WCap).

	Food type Method	R²	pHMIN	pHInfl	pHMAX	t1	t2	t3	k1	k2
		Cat	0.906	1.8	3.6	6.5	2	23	256	0.40	0.17
	Seitan
		WCap	0.946	1.0	3.6	6.5	4	18	89	0.40	1.00
		Cat	0.955	1.5	3.0	7.7	3	9	300	0.40	0.02
	Tofu
		WCap	0.960	1.4	2.8	4.5	1	28	125	0.40	0.10
	Pea	Cat	0.987	1.8	1.8	7.1	2	160	-	0.40	0.04
	emulsion	WCap	0.956	1.8	1.8	7.3	3	147	-	0.40	0.02
	Soymilk	Cat	0.983	1.8	1.8	7.3	2	174	-	0.40	0.04
		WCap	0.965	1.8	1.8	7.2	2	183	-	0.40	0.05

Acknowledgements

We thank Julien Hermet for managing the animal facilities as well as Philippe Lhoste and Yohan Delorme for caring for the experimental animals. We are grateful to Coline Simon for helping to develop our pH monitoring methods and to Cristian Trelea and Alain Riaublanc for assisting us in our modelling efforts, and to Jessica Pearce for correcting the English of the manuscript. Finally, we would like to acknowledge Denys Durand, for doing the animal surgeries.

Funding information

The present article is part of a CIFRE thesis (CIFRE n°2016/0719) funded by IMPROVE SAS and INRA.

Disclosure

The authors have no competing interests.

Author Contributions

YR and DR designed the animal experiments. DD, IS, ML, YR, and DR defined the food types. YR, CB, JD, BC, MV, and DR performed the experiments. YR and DR analysed the data. YR drafted the manuscript, and DR, DD, and IS helped revise it.


Marina Al Daccache

Mohamed Koubaa
email: m.koubaa@escom.fr

Dominique Salameh

Eugène Vorobiev

Richard G Maroun

Nicolas Louka

Marina Al Daccache

Control of the sugar/ethanol conversion rate during moderate pulsed electric field-assisted fermentation of a Hanseniaspora sp. strain to produce low-alcohol cider

Keywords: Hanseniaspora sp., pulsed electric fields, fermentation, cider, low alcohol content

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Over the last decade, an increase of the alcohol content in some alcoholic beverages such as wines has been observed worldwide [START_REF] Godden | Trends in the composition of Australian wine 1984-2014[END_REF][START_REF] Tilloy | Reduction of ethanol yield and improvement of glycerol formation by adaptive evolution of the wine yeast Saccharomyces cerevisiae under hyperosmotic conditions[END_REF]. This behavior is most probably largely related to the intensification of global warming, which leads to the accumulation of higher sugar contents in the fruits [START_REF] Alston | Too much of a good thing? Causes and consequences of increases in sugar content of California wine grapes[END_REF][START_REF] Samson | Pratiques oenologiques : les dernières tendances en lien avec l'évolution climatique? 23-28[END_REF]. Increasing the alcohol content in the fermented fruit juices is undesired due to the associated drawbacks such as the deceleration of the fermentation kinetics [START_REF] Malacrinò | The vinification of partially dried grapes: a comparative fermentation study of Saccharomyces cerevisiae strains under high sugar stress[END_REF], the reduced yeast activity, and the alteration of the beverage sensory quality [START_REF] King | The influence of alcohol on the sensory perception of red wines[END_REF].

Producing low alcoholic and non-alcoholic beverages such as wine, beer, and cider is attracting more and more attention due to the societal and health issues related to alcohol consumption. Several approaches have been described in the literature leading obtaining low-alcohol beverages [START_REF] Longo | Changes in volatile composition and sensory attributes of wines during alcohol content reduction[END_REF][START_REF] Schmidtke | Production technologies for reduced alcoholic wines[END_REF]. One of the proposed solutions to decrease the alcohol level consists of reducing the sugar content in the fruits by for example modifying the cultivation practices, the fermentation of early-harvested fruits, or by diluting or membrane filtration of the juice [START_REF] Novello | Viticultural strategy to reduce alcohol levels in wine[END_REF][START_REF] Palliotti | Changes in vineyard establishment and canopy management urged by earlier climate-related grape ripening: A review[END_REF]. However, it was observed that these methods seem to have a low impact on the ethanol concentration of the fermented juice [START_REF] De Toda | Reducing the sugar and pH of the grape ("Vitis vinifera" L. cvs. "Grenache" and Tempranillo") through a single shoot trimming[END_REF][START_REF] Heymann | Effects of extended grape ripening with or without must and wine alcohol manipulations on cabernet sauvignon wine sensory characteristics[END_REF][START_REF] Novello | Viticultural strategy to reduce alcohol levels in wine[END_REF][START_REF] Palliotti | Changes in vineyard establishment and canopy management urged by earlier climate-related grape ripening: A review[END_REF]. However, some of these techniques may alter the aroma and the mouthfeel of the product [START_REF] Cassano | Analyses of hydrodynamic resistances and operating parameters in the ultrafiltration of grape must[END_REF][START_REF] Echavarría | Fruit juice processing and membrane technology application[END_REF][START_REF] Heymann | Effects of extended grape ripening with or without must and wine alcohol manipulations on cabernet sauvignon wine sensory characteristics[END_REF][START_REF] Kontoudakis | Use of unripe grapes harvested during cluster thinning as a method for reducing alcohol content and pH of wine[END_REF][START_REF] Salgado | Alcohol reduction in red and white wines by nanofiltration of musts before fermentation[END_REF]. Another solution is to generate low-alcohol engineered yeasts, which are able to redirect the ethanol biosynthesis towards the production of by-products such as glycerol, acetaldehyde, and acetoin during the fermentation [START_REF] Lopes | Fermentation properties of a wine yeast over-expressing the Saccharomyces cerevisiae glycerol 3-phosphate dehydrogenase gene (GPD2)[END_REF][START_REF] Tilloy | Reducing alcohol levels in wines through rational and evolutionary engineering of Saccharomyces cerevisiae[END_REF][START_REF] Varela | Strategies for reducing alcohol concentration in wine[END_REF][START_REF] Varela | Evaluation of gene modification strategies for the development of low-alcohol-wine yeasts[END_REF]). Yet, another approach reducing the ethanol concentration during the fermentation is to stop the process before completion. Post-fermentation membrane separation technique including reverse osmosis [START_REF] Bui | A reverse osmosis for the production of low ethanol content wine[END_REF][START_REF] Catarino | Alcohol removal from beer by reverse osmosis[END_REF][START_REF] Pilipovik | Assessing dealcoholization systems based on reverse osmosis[END_REF], evaporative perstraction [START_REF] De Francesco | Effect of operating conditions during low-alcohol beer production by osmotic distillation[END_REF][START_REF] Varavuth | Experimental study on dealcoholization of wine by osmotic distillation process[END_REF], and pervaporation technology [START_REF] Del Olmo | Pervaporation methodology for improving alcohol-free beer quality through aroma recovery[END_REF][START_REF] Takács | Production of alcohol free wine by pervaporation[END_REF] are generally the most applied methods that are agreed to reduce the ethanol concentrations in the fermented juices.

Due to all of the above-mentioned issues, developing new strategies that preserve the sensory and organoleptic properties of the fermented fruit juices along with reducing the ethanol content is of paramount importance. Pulsed electric field (PEF) assisted fermentation process seems to be a promising technique to solve the above-mentioned problems. In fact, the interaction of microorganisms with electric fields has intrigued microbiologists for many years since electrical current can increase or inhibit the growth of microorganisms, depending on the experimental conditions [START_REF] Cho | Growth kinetics of Lactobacillus acidophilus under ohmic heating[END_REF]. The influential parameters include the intensity of the electric field, its temporal function (e.g. constant and pulsed), the treatment time, the cell type, and the characteristics of the medium [START_REF] Castro | The effect of the electric field on lag-phase, ethanol and β-galactosidase production of a recombinant S. cerevisiae growing on lactose[END_REF]. Thus, the PEF technology may represent a challenging technique to control the sugar/ethanol conversion rate in the fermented fruit juices. The review of the literature shows that the application of moderate PEF during the fermentation processes was the subject of a minor number of studies. Most of them were focused on the effect of PEF on the microbial cultures when treated prior to fermentation [START_REF] Ki | Effects of prefermentation and pulsed-electric-field treatment of primary sludge in microbial electrochemical cells[END_REF][START_REF] Mattar | S. cerevisiae fermentation activity after moderate pulsed electric field pre-treatments[END_REF]. In this regard, the aim of this work was to investigate the effect of moderate pulsed electric field treatment on apple juice fermentation using the yeast Hanseniaspora sp. towards cider production. In fact, the genus Hanseniaspora was found as a major yeast developed during spontaneous apple juice fermentation [START_REF] Pando Bedriñana | Genetic and phenotypic diversity of autochthonous cider yeasts in a cellar from Asturias[END_REF][START_REF] Valles | Yeast species associated with the spontaneous fermentation of cider[END_REF]. In addition, the positive impact of Hanseniaspora sp. in alcoholic fermentation has been demonstrated [START_REF] Capozzi | Impact of co-inoculation of Saccharomyces cerevisiae, Hanseniaspora uvarum and Oenococcus oeni autochthonous strains in controlled multi starter grape must fermentations[END_REF], which may positively contribute to the aroma profile of the fermented apple juice (de Arruda Moura Pietrowski, dos Santos, Sauer, Wosiacki, & Nogueira, 2012).

Materials and methods

Chemicals, yeast, and apples

Acetonitrile (CH3CN), ethanol (C2H6O), and peptone were purchased from Fisher Scientific (Illkirch, France). Yeast extract was bought from Sigma-Aldrich (Saint-Quentin-Fallavier, France). Fructose and glucose were supplied by Merck Millipore (Guyancourt, France).

Hanseniaspora sp. strain used in this work belongs to the authors' laboratory collection. The strain was isolated previously from spontaneous fermentation of a Lebanese apple juice obtained from the "Ace Spur" variety [START_REF] Al Daccache | New indigenous yeast strains "Hanseniaspora Meyeri-Libani" for the elaboration of cider[END_REF]. The Yeast-Extract-Peptone-Dextrose (YPD) medium (1 % yeast extract, 2 % peptone, 2 % dextrose) was used for the pre-culture yeast growth. The apples used in this work ("Ace spur" variety) were collected from Bqaatouta village (Keserwan region, Lebanon).

Fermentation processes

Hanseniaspora sp. pre-culture

The pre-culture of the Hanseniaspora sp. strain was performed by taking one colony from YPD agar (1 % yeast extract, 2 % peptone, 2 % dextrose, and 2 % agar) plate into 250 mL of YPD (1 % yeast extract, 2 % peptone, 2 % dextrose) medium under sterile conditions (20 min at 121 °C in an autoclave HMC HV-110L (HMC Europe GmbH, Germany)). The pre-culture was incubated during 45 h at 30 °C temperature and 250-rpm agitation on a shaker (Thermo Scientific, France). A volume of 1.5 L of sterile YPD (1 % yeast extract, 2 % peptone, 2 % dextrose) medium was introduced into a 2-L fermenter (LSL Biolafitte S.A.). After sterilization and cooling to 30 °C, the medium was inoculated by the pre-culture to get an initial concentration of 3.0*10 6 cells/mL. After 24-h incubation at 30 °C and 250 rpm, the pre-culture was used to inoculate the apple juice.

Apple juice control fermentations

The apples were first washed and then used to extract the juice using a fruit juice centrifuge separator (Moulinex, France). A volume of 1.5 L of apple juice was introduced into a 2-L sterile fermenter and inoculated by the pre-culture to get an initial concentration of 3.0*10 6 cells/mL.

Cider production was conducted for 100 h in batch mode, without medium circulation, at 30 °C and 250 rpm (Figure 1.A). The kinetics of growth, substrate consumption, and ethanol production were recorded during the fermentation. The content of soluble compounds was followed by measuring the °Brix using a digital refractometer (Anggur DR401, France). After recording the kinetics of growth and substrate consumption in batch mode without medium circulation, a treatment chamber was used to allow the circulation of the medium and the yeast treatment by PEF during the fermentation (Figure 1.B). A peristaltic pump allowed the medium circulation from the reactor to the treatment chamber and back into the reactor. A circulation flow rate of 75 mL/min, allowing obtaining the same growth and glucose consumption kinetics than in batch mode without circulation (Figure 1.A) was used. The total length of the tubes used was equal to 90 cm. The treatment chamber and the tubes were sterilized as above-mentioned.

Apple juice PEF-assisted fermentations

The impact of moderate pulsed electric fields on the growth, substrate consumption, and ethanol production by Hanseniaspora sp. yeast during apple juice fermentation was studied. For this purpose, the two electrodes of the PEF-treatment chamber were connected to a PEF generator made by the "Service électronique" of the University of Technology of Compiègne (France). This generator provides bipolar pulses of near rectangular shape, with the respective maximal delivered voltage and current of 400 V and 40 A. As the distance between the electrodes was set to 1.4 cm, the maximum field strength applied and used was 285 V/cm. A succession of N trains, each of them composed of n = 10 pulses was applied. Each pulse duration was set to ti = 100 μs, with a pulse period time of Δt = 1 ms. The time between the trains was set to Δtt = 1 s. First, the PEF treatments were carried out during the lag and the log fermentation phases, and then during each phase separately. The impact of PEF-treatment of the pre-culture, followed by apple juice fermentation without PEF-treatment was also studied. The effective PEF treatment tPEF was calculated as follows: tPEF (s) = N•n•ti (s). Thus, four PEF-assisted fermentations were investigated (Figure 2): 1) PEF-treatment for 12 h from the beginning of fermentation including the lag phase (tPEF = 21.43 s), 2) PEF-treatment for 12 h during the log phase (after 12 h of fermentation) (tPEF = 21.43 s), 3) fermentation of the medium inoculated with treated pre-culture during 6 h (tPEF =10.72 s) (during the lag and the log phases), and 4) fermentation of the medium treated for 12 h during the log phase (after 12 h of fermentation), which was inoculated by a pre-culture previously PEF-treated for 6 h (tPEF =10.72 s +21.43 s =32.15 s).

The current I (A) and the voltage U (V) were recorded using a data logger and software developed by the "Service électronique" of the University of Technology of Compiègne (France). The specific power consumption of PEF treatment W (J/mL) was estimated by the summation of the power consumptions during each pulse as given in equation (1).

J/mL = . .

Equation (1)

where U is the voltage (V), I is the current intensity (A), and V is the volume of the culture medium in the bioreactor (mL).

Determination of the growth, substrate consumption, and ethanol production kinetics

The kinetics of growth, substrate consumption, and ethanol production were recorded for all fermentations (control and PEF-assisted).

Determination of the growth kinetics

The growth kinetics were followed by the determination of the cell concentration (cells/mL) during the fermentation phases. Samples were taken periodically from the bioreactor, diluted several times in ultrapure water, and the cells were counted using a Thoma cell counting chamber (Preciss, France). To detect the viable cells, the samples were mixed volume to volume with a solution of 0.01 % methylene blue. The biomass concentration X (cells/mL), the ethanol production rate γ (gethanol/gbiomass.h), the biomass yield YX/S (gbiomass/gsubstrate), and the product yield (YP/S (gethanol/gsubstrate) were calculated respectively according to equations (2), (3), ( 4) and ( 5).

/ =

. .

Equation (2)

where a represents the number of cells counted, d represents the dilution factor, and b represents the number of squares counted in the chamber.

! " # $%& / ! '%( )) . ℎ = + , -+ .

/. , -.

Equation (3)

0 //1 ! '%( )) ! )2 ) 3 " ⁄ = / , -/ . 1 . -1 ,
Equation ( 4)

0 +/1 ! " # $%& ! )2 ) 3 " ⁄ = + , -+ . 1 . -1 , Equation (5)
where P0 and Pf represent respectively the initial and the final ethanol concentrations, X0 and Xf represent respectively the initial and the final biomass concentrations, and S0 and Sf represent respectively the initial and the final substrate concentrations.

The maximal specific growth rate µmax (h -1 ) was determined by plotting Ln X (cells/mL) versus the time (h). µmax (h -1 ) values corresponded to the slopes of the linear curves obtained during the exponential phases.

Carbon substrates quantification

The concentrations of carbon substrates were determined using high-performance liquid chromatography instrument LC-20AD (Shimadzu, Japan). The separation was performed in isocratic mode (a mixture of 1:4 (20:80) (v/v) water/acetonitrile with a flow rate of 3 mL/min), and using a Luna 5 μm NH2 250*4.6 mm column. The separation temperature was set to 40 °C and the molecules were detected using a low-temperature evaporative light scattering detector ELSD-LT II (Shimadzu, Japan). The samples taken during the fermentation processes were diluted 10 times in ultrapure water and a volume of 1 µL was injected. The compounds' assignation and quantification were performed using pre-established standard curves of glucose

(5-30 g/L) and fructose (2.5-30 g/L).

Ethanol quantification

10 Samples taken at different times of the fermentation processes were centrifuged at 6000 rpm for 10 min in order to eliminate the yeast suspensions and other apple particles. The ethanol content was determined by density measurements using a 5-mL glass pycnometer (Thermo Fisher, France). Calibration curve for ethanol quantification was performed using reconstituted solutions of apple juice/ethanol (1-10%, v/v).

Statistical analyses

Each experiment was repeated, at least, three times. One-way ANOVA was used for the statistical analysis of the data with the help of Statgraphics Plus software (version 5.1, Statpoint Technologies Inc., Warrenton, VA, USA). A significance level of 5% was taken for each analysis. The error bars presented in the figures correspond to the standard deviations.

Results and discussion

The impact of moderate PEF on ethanol accumulation was evaluated by treating Hanseniaspora sp. yeast during cider production. First, the different growth phases were identified during the conventional fermentation of the apple juice (without and with medium circulation, and without PEF application), which served as a control.

Results in figure 3.A show an increase in the biomass concentration by around 25% when the apple juice yeast suspension was PEF-treated during or after the first twelve hours of fermentation (Figure 2.A-B) and by around 45% when the inoculum was previously treated (Figure 2.C). These results concur with that of [START_REF] Mattar | Stimulation of Saccharomyces cerevisiae cultures by pulsed electric fields[END_REF] who reported the stimulation of Saccharomyces cerevisiae cells under moderate PEF. In addition, many other works have been described in the literature reporting the increase in the biomass concentration by the application of moderate PEF, which were recently summarized and reviewed [START_REF] Barba | Current applications and new opportunities for the use of pulsed electric fields in food science and industry[END_REF][START_REF] Koubaa | New challenges and opportunities of food fermentation processes: Application of conventional and innovative techniques[END_REF][START_REF] Mota | Fermentation at non-conventional conditions in food-and bio-sciences by the application of advanced processing technologies[END_REF]. This increase in the biomass concentration could be explained by the increase of the yeast cells' division during the PEF treatment. The values of the maximal growth rate, µmax (Table 1) indicate a ten-fold acceleration of the growth kinetics for the PEF-assisted fermentation treated during the first twelve hours (Figure 2.A). Consequently, the time to reach the stationary phase was reduced by 10 h. In addition, the treatment of the inoculum (Figure 3.C) showed a significant impact on the fermentation kinetics. In fact, higher µmax values were obtained compared to the control, which indicates that the stress applied to the yeasts during the pre-culture may influence the fermentation kinetics. The review of the literature shows that few studies have described the effect of electric field on the fermentation kinetics, which demonstrate mainly a significant acceleration of the PEF-assisted fermentations [START_REF] Berovic | The influence of galvanic field on Saccharomyces cerevisiae in grape must fermentation[END_REF][START_REF] Loghavi | Effect of moderate electric field frequency on growth kinetics and metabolic activity of Lactobacillus acidophilus[END_REF][START_REF] Mattar | S. cerevisiae fermentation activity after moderate pulsed electric field pre-treatments[END_REF][START_REF] Nakanishi | Effect of electric current on growth and alcohol production by yeast cells[END_REF].

Furthermore, the results presented in figure 3.A demonstrate that the impact of moderate PEF treatment may differ from one growth phase to another. For instance, although the same energy (J/mL) was consumed for the treatments during either the first 12 h (lag phase) (Figure 2.A) or the second 12 h (log phase) of fermentation (Figure 2.B), the results obtained are significantly different (Table 1). In fact, the fermentation rate was significantly faster for the PEF-treated culture during the lag phase. These results concur with those reported by [START_REF] Mattar | Stimulation of grape juice fermentation using moderate pulsed electric field treatment[END_REF] who noticed an acceleration of the fermentation kinetics when stimulating S. cerevisiae cells by PEF during the lag phase. It has been also reported that the electroporation effectiveness was greater when PEF was applied during the first hours of fermentation, compared to that applied after 24 h of fermentation [START_REF] Mattar | Stimulation of grape juice fermentation using moderate pulsed electric field treatment[END_REF]. These observations also concur with that of [START_REF] Mcdonald | Effects of pulsed electric fields on microorganisms in orange juice using electric fields strengths of 30 and 50 kV/cm[END_REF], who demonstrated that PEF has different effects on microorganisms depending on the growth phase.

The results in figure 3.A show as well that the impact of moderate PEF treatment of the inoculum for 6 h and the log phase of the apple juice culture for 12 h (Figure 2.D) was similar to that of the PEF treatment of the inoculum alone (Figure 2.C) for 6 h. In fact, no significant differences were observed between the cell growth kinetics.

The impact of PEF treatment was also studied in term of ethanol accumulation. The results presented in figure 3.B show that the ethanol content in the fermented apple juice depended on the PEF treatment applied, with significant differences observed. In all cases, the application of PEF led to decrease the ethanol content after 100 h of apple juice fermentation. To the best of our knowledge, this is the first time that moderate PEF is demonstrated to reduce the ethanol content in fermented beverages. In fact, some previous works such as that of Nakanishi and coworkers have reported an increase in the ethanol production when S. cerevisiae cells were treated by direct electric current [START_REF] Nakanishi | Effect of electric current on growth and alcohol production by yeast cells[END_REF]. These differences could be related to many factors including the PEF parameters and treatment duration, as well as the characteristics of the microorganism and the growth phase. The lowest ethanol decrease compared to the control was by 0.5% (v, v), observed for the fermentation assisted by PEF during the log phase. The highest ethanol decrease was by 1.6% (v: v), compared to the control, and was observed for the fermentations inoculated with treated yeasts during the pre-culture, without any PEF treatment of the apple juice yeast suspension (Figure 2.C). Furthermore, lower ethanol production rates γmax (Table 1) were obtained for all the pre-treated and treated samples. The lowest rate values were related to the pre-treated yeast and treated juice during the first 12 h of fermentation. The fastest growth kinetics observed for the treatment during the first 12 hours and the pre-culture treatment (Figure 3.A) are associated to the fermentations having the lowest ethanol production rate and content (Figure 3.B). The reasons behind could be explained by the fact that the yeast cells are consuming sugars to produce more biomass rather than producing ethanol. In fact, when the biomass yield is significantly higher for the treated fermentations compared to that of the untreated ones, this was compensated by a decrease in the ethanol yield (Table 1). It was recently reported that applying an electric field could affect the reaction pathways by different mechanisms [START_REF] Jiang | Study of mechanisms for electric field effects on ethanol oxidation via reactive force field molecular dynamics[END_REF]. For instance, the electric field can have a direct effect on the charged particles that can pull them away from the reaction zone [START_REF] Jiang | Study of mechanisms for electric field effects on ethanol oxidation via reactive force field molecular dynamics[END_REF], which may disturb the reactions of ethanol biosynthesis in the current study. In fact, during glycolysis, it is crucial to recycle the NAD + and oxidize the NADH, otherwise, a decrease in the glycolytic flux may occur, knowing that glycolysis is an important metabolic pathway for the ethanol production. A significant part of the NADH produced during the glycolysis is afterward oxidized during the ethanol formation [START_REF] Kutyna | Microbiological approaches to lowering ethanol concentration in wine[END_REF]. The alteration of the balance NAD + /NADH can redirect the carbon flux towards different end-points. This alteration could take place during the PEF treatment since it has an impact on the charged particles as mentioned above.

The yeast growth and ethanol production were obviously associated with the consumption of substrates. Here, mainly glucose and fructose were consumed during the apple juice fermentation. Similar to the above-discussed kinetics, the decrease in the concentrations of substrates depended on the PEF treatment (Figure 4). This behavior was previously observed when using mutants for ethanol production. For instance, [START_REF] Tilloy | Reducing alcohol levels in wines through rational and evolutionary engineering of Saccharomyces cerevisiae[END_REF] cultivated strains of S. cerevisiae wine yeasts under hyperosmotic stress in order to redirect the ethanol synthesis towards the glycerol formation. All the strains showed a reduced ethanol yield compared to the reference strain. In addition, the mutants showed reduced sugar consumption, and the authors established a correlation between the reduced ethanol yield, the high glycerol yield, and the decrease in the fermentative properties.

Regarding the energy consumption, the PEF-treatment of the pre-culture during 6 h consumed less energy (Table 1) than the other PEF-assisted fermentations and provided the highest ethanol reduction in the fermented apple juice. Applying 82 J/mL reduced the final ethanol concentration by 1.6 % (v: v), compared to the control. The conventional methods used to remove partial ethanol from beverages such as vacuum distillation requires higher energy consumption and higher cost than that demonstrated here [START_REF] Margallo | Life cycle assessment of technologies for partial dealcoholisation of wines[END_REF][START_REF] Schmidtke | Production technologies for reduced alcoholic wines[END_REF]. In addition, the use of genetically modified yeasts producing less ethanol is not only restricted by the consumers' resistance to genetically modified food products [START_REF] Chambers | Fermenting knowledge: the history of winemaking, science and yeast research[END_REF], but also by the complexity of the biological systems and their understanding which can limit the engineering processes [START_REF] Williams | Synthetic evolution of metabolic productivity using biosensors[END_REF]. From another perspective, the application of the conventional methods, involving the use of heat, may induce the loss of flavors and the organoleptic properties [START_REF] Scott | Removal of alcohol from beverages[END_REF]. In addition, retention of the compounds affecting the product aroma may occur during the membrane filtration [START_REF] Catarino | Alcohol removal from beer by reverse osmosis[END_REF]. Compared to the results obtained in the current study, it could be underlined that moderate PEF treatment could be a challenging solution to reduce the ethanol content in alcoholic beverages and to control the sugar/ethanol conversion rate without affecting the sensory properties of cider. In fact, it has been previously demonstrated that PEF-treated cider had a better color and aroma retention than the thermally processed samples (Azhuvalappil, Fan,

Conclusions

In this work, the impact of moderate PEF treatment on Hanseniaspora sp. cells was studied during the apple juice fermentation. For an effective PEF-treatment time of 10.716 s applied to the pre-culture during 6 h, the highest alcohol reduction was observed. The yeast concentration and the biomass yield increased significantly, while the ethanol production rate was reduced by the half. In addition, the yeast cells were more sensitive to moderate PEF treatment during the first hours of fermentation rather than during the log phase, even though the same energy was used. The yeast concentration and the biomass yield increased significantly for all the treatments.

However, the fastest kinetic was observed for the treatment during the first 12 h of fermentation.

The increased growth rates were accompanied by decreased ethanol rates and contents.

The results indicate the great potential for practical implementation of PEF-assisted fermentation technology to reduce the ethanol content during the fermentation processes and especially using Hanseniaspora sp. yeast cells. However, further investigations are required in order to understand the mechanisms behind the microbial stimulation and ethanol reduction. Table 1. Fermentation process performances, and energy consumptions during the PEF treatments. SD denotes standard deviation. "a" denotes significant differences with the untreated sample

  Figure captions:

Figure 2 .

 2 Figure 2. PEF treatment during the fermentation processes (pre-culture and apple juice PEFassisted fermentations). The symbols in the dashed inserts represent those used in figures 3 and 4 of the results and discussion section.

Figure 3 .

 3 Figure 3. Growth kinetics (A.) and ethanol kinetics (B.) for the control and the PEF-assisted fermentations, "a" denotes significant differences at the end of the fermentation, in comparison with the untreated samples.

Figure 4 .

 4 Figure 4. Fructose (A.) and glucose (B.) consumption kinetics for the control and the PEFassisted fermentations.

  Figure 2.

Geveke[START_REF] Azhuvalappil | Thermal and nonthermal processing of apple cider: Storage quality under equivalent process conditions[END_REF]. Nonetheless, organoleptic investigations are required in order to evaluate the volatile organic compounds and the aromatic profile of the untreated and PEFtreated ciders.

Acknowledgments 16 The authors wish to thank the Research council of Saint-Joseph University of Beirut, Project FS103 and the Lebanese National Council for Scientific research (CNRS-FS129). All the authors thank Dr. Nicolas Thiebault as well for his help in performing the HPLC analyses at ESCOM-Chimie (Compiègne, France).