Wenfan Cao

Julie Aubert

Marie-Bernadette Maillard

Francoise Boissel

Arlette Leduc

Jean-Luc Thomas

Stéphanie-Marie Deutsch

Bénédicte Camier

Ali Kerjouh

Sandrine Parayre

Françoise Boissel

Marielle Harel-Oger

Gilles Garric

Anne Thierry

Hélène Falentin
email: helene.falentin@inrae.fr

Fine-Tuning of Process Parameters Modulates Specific Metabolic Bacterial Activities and Aroma Compound Production in Semi-Hard Cheese

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

Figures and tables can be found at the end of the document.

Introduction

The quality of cheese is markedly dependent on the microorganisms used as starter, for both acidification and aromatization purposes. The activity of microorganisms during cheese manufacture and ripening induces modifications to all milk constituents (carbohydrates, proteins and lipids), which in turn leads to the development of cheese flavor. Indeed, flavor development is a dynamic biochemical process that is impacted by (i) milk composition; (ii) curd processing and ripening conditions, and (iii) enzymes naturally present in cow milk and (iv) the indigenous microorganisms or added as starter [START_REF] Mcsweeney | Biochemistry of Cheese Ripening[END_REF] . Cheese microorganisms are the primary source of the enzymes that influence flavor development. Flavor compounds include sapid compounds (mainly organic acids, peptides and amino acids), alongside added NaCl, and volatile aroma compounds.

The microbiological and biochemical processes involved in cheese aromatization have been well deciphered in recent decades. In industrial cheeses, microorganisms developing aroma compounds in cheeses essentially originates from selected strains inoculated as a starter culture in the milk at the beginning of the cheese making. For example, in Cheddar and Emmental cheeses, the production of diacetyl and propionic acid by Lactococcus (L.) lactis and Propionibacterium (P.) freudenreichii, respectively, has been monitored during cheese making [START_REF] Desfossés-Foucault | Assessment of Probiotic Viability during Cheddar Cheese Manufacture and Ripening Using Propidium Monoazide-PCR Quantification[END_REF][START_REF] Thierry | Influence of Thermophilic Lactic Acid Bacteria Strains on Propionibacteria Growth and Lactate Consumption in an Emmental Juice-like Medium[END_REF] and the corresponding metabolic pathways have been established. The ability of microorganisms to produce aroma compound is highly species and strain-dependent, as shown by the results of screening to select the best producers [START_REF] Thierry | Strain-to-Strain Differences within Lactic and Propionic Acid Bacteria Species Strongly Impact the Properties of Cheese-A Review[END_REF] . Many studies show the impact of starters and/or ripening cultures in the differential production of aroma compounds in industrial cheeses: addition of different strains of P. freudenreichii in Raclette cheese [START_REF] Thierry | The Addition of Propionibacterium Freudenreichii to Raclette Cheese Induces Biochemical Changes and Enhances Flavor Development[END_REF] , associations of different strains of P. freudenreichii and lactic acid bacteria in Emmental cheese [START_REF] Thierry | A Simple Screening Method for Isovaleric Acid Production by Propionibacterium Freudenreichii in Swiss Cheese[END_REF][START_REF] Thierry | Propionibacterium Freudenreichii Strains Quantitatively Affect Production of Volatile Compounds in Swiss Cheese[END_REF] , diversification of aroma compounds according to the strain of L. paracasei in Cheddar cheese [START_REF] Stefanovic | Evaluation of the Potential of Lactobacillus Paracasei Adjuncts for Flavor Compounds Development and Diversification in Short-Aged Cheddar Cheese[END_REF] . However, little is known regarding the impact of process implemented in the cheese manufacture on the metabolism of bacteria and thus on the production of aroma compounds.

Environmental factors such as pH, salt and temperature are well known to influence the metabolism of bacterial cells in a culture medium in vitro. In cheese, process parameters such as the salting step, ripening temperature and duration of different stages can drastically influence the organoleptic quality of cheeses. The effects of salt and temperature have been shown to modulate bacterial growth and, as a consequence, the aroma compounds in cheeses [START_REF] Rehman | Influence of Ripening Temperature on the Volatiles Profille and Flavour of Cheddar Cheese Made from Raw or Pasteurised Milk[END_REF][START_REF] De Filippis | Metatranscriptomics Reveals Temperature-Driven Functional Changes in Microbiome Impacting Cheese Maturation Rate[END_REF][START_REF] Dugat-Bony | Overview of a Surface-Ripened Cheese Community Functioning by Meta-Omics Analyses[END_REF] . In Cheddar cheeses made with raw milk, a ripening period at 8 °C instead of 1°C increased drastically volatiles compounds and NSLAB growth [START_REF] Rehman | Influence of Ripening Temperature on the Volatiles Profille and Flavour of Cheddar Cheese Made from Raw or Pasteurised Milk[END_REF] . In Caciocavallo Silano cheese, a traditional Italian cheese inoculated with an undefined whey culture, an increase in the ripening temperature from 16°C to 20 °C promoted the expression of genes related to proteolysis, lipolysis and amino acid and lipid catabolism and significantly increased the cheese maturation rate and the aroma compounds content [START_REF] De Filippis | Metatranscriptomics Reveals Temperature-Driven Functional Changes in Microbiome Impacting Cheese Maturation Rate[END_REF] .

The aim of the present study was to determine how certain variations in process parameters can influence the metabolism of bacteria and therefore whether the choice of process parameters might constitute a lever to modulate the volatilome of semi-hard cheeses.

Our strategy was first of all to develop a reproducible, standard semi-hard model cheese (Std) which was inoculated with three strains: a L. lactis ssp lactis biovar diacetylactis strain for curd acidification and diacetyl production, a Lactiplantibacillus (L.) plantarum strain to mimic the role of a non-starter lactic acid bacterium, and a P. freudenreichii strain to produce diverse aroma compounds during ripening. The second step was to establish how process parameters modulate the formation of aroma compounds, by modifying three process parameters: reduced stirring time (Stir15min), reduced salting time (Salt7h) or higher ripening temperature (Rip16°C), tested independently. Sampling was performed throughout manufacture and ripening in order to assess the effects of the technological changes versus the Std in terms of biochemistry, bacterial growth and survival and aroma compound content.The relative contributions of time, changes to process parameters were fully discussed.

MATERIALS AND METHODS

Microorganisms and culture conditions

The consortium used to manufacture the model cheese was composed of three bacterial strains originating from the CIRM-BIA (INRAE, France) collection: L. lactis ssp lactis biovar diacetylactis CIRM-BIA1206, L. plantarum CIRM-BIA465 and P. freudenreichii CIRM-BIA122.

The strains were stored at -80°C as glycerol stocks (15% v/v). All strains were grown without agitation at 30°C in standard broths: the M17 broth containing 0.5% (w/v) lactose for L. lactis, Man, Rogosa and Sharpe broth (MRS, pH 5.4) for L. plantarum, and YEL broth [START_REF] Malik | An Evaluation of the Taxonomy of Propionibacterium[END_REF] for P. freudenreichii.

Before cheese making, the frozen strains were revived at 30°C for 2 days in broth media. L. lactis and L. plantarum were then transferred three times at 1% (v/v) in commercial full-fat UHT cow's milk (Delisse®, France). L. lactis was first inoculated in 10 mL milk for 48 h and then in 100 mL milk for 24 h. The final culture of L. lactis was performed in 6 L milk and incubated for 41 h to reach a targeted pH of between 5.3 and 5.5. Subcultures of L. plantarum were produced in the same manner but in different milk volumes: 30 mL for the second culture and 2 L for the final culture targeting a pH of between 6.3 and 6.4. The revived P. freudenreichii was subcultured twice at 1% (v/v) in sterile milk ultrafiltrate supplemented with 10 g/L casein hydrolysate (Organotechnie, La Courneuve, France) and 50 mM sodium L-lactate (Galaflow SL60; Société Arnaud, Paris, France) (medium abbreviated as UF), prepared as previously described [START_REF] Guyomarc'h | Surface Properties Associated with the Production of Polysaccharides in the Food Bacteria Propionibacterium Freudenreichii[END_REF] . The first subculture of P. freudenreichii was performed in 10 mL UF for 72 h. It was then transferred into 0.5 L UF and incubated for 60 h. Before cheese making, the cell concentration in the final culture of P. freudenreichii was evaluated by spectrophotometry at 600 nm. An appropriate culture volume, corresponding to a final population of 6.10 6 colony-forming units (CFU) per mL in the milk used for cheese making, was centrifuged for 5 min at 6000 g. The cell pellet was then suspended in 10 mL UHT milk and inoculated into the cheese milk.

Plate counting was also used to control milk quality. The standardized milk was analyzed by plate count agar (PCA) and VRBL agar to check for an absence of contamination. Around 10 3 CFU/mL mesophilic bacteria, ~ 10 2 CFU/mL thermophilic bacteria and <1 CFU/mL coliforms were detected after each pasteurization and each standardization. These levels were judged to be acceptable for cheese making.

Semi-hard cheese manufacture

Four biological repetitions of cheese production were performed at the pilot scale. The repeatability and reproducibility of the standard cheeses were assessed by making them in two or three different vats, respectively, in order to generate duplicates or triplicates. The standard and three variant itineraries were all performed twice in parallel in three different vats. The choice of vat was randomized to prevent a potential 'vat effect'.

To make each cheese, cow's milk was freshly collected (Entremont, Montauban de Bretagne, France) and treated in batches as follows (Figure 1). The cow's milk was pasteurized at 76°C for 20 s, skimmed and standardized to 30 g fat and 36 mg calcium per kg of milk by adding fat and CaCl 2 solution, as described by Leyva Salas et al. (2018) [START_REF] Leyva Salas | Antifungal Activity of Lactic Acid Bacteria Combinations in Dairy Mimicking Models and Their Potential as Bioprotective Cultures in Pilot Scale Applications[END_REF] . Final cultures of L. lactis and L. plantarum were inoculated in the standardized milk at ~10 5 CFU/mL. The inoculated milk then underwent prematuration at 14°C for 18 h before cheese manufacture. P. freudenreichii was added at a rate of 6.10 6 CFU/mL in matured milk and stirred for 20 min. The milk was then pumped into vats (180 kg/vat) for cheese production.

The standard itinerary for cheese making (Std), represented schematically in Figure 1, was modified from a previously described method [START_REF] Leyva Salas | Antifungal Activity of Lactic Acid Bacteria Combinations in Dairy Mimicking Models and Their Potential as Bioprotective Cultures in Pilot Scale Applications[END_REF] . Briefly, the matured milk was warmed at 33°C for around 30 min. When the pH reached 6.5, 0.25 mL/kg of commercial rennet (520 mg/L chymosin, Carlina 145/80, Dupont Danisco, Dangé, Saint Romain, France) was added to the milk. Gelation time was approximately 20 minutes and firming time was 10 minutes, for a total clotting time of 30 minutes. After cutting the coagulum to the size of corn grains (5x5x5 mm), the curd was stirred for 30 min at 33°C, followed by washing and draining steps. Washing was carried out by replacing 25% of the whey with water at the same temperature (33°C). The washed coagulum was then transferred by gravity into a 56 x 44 cm container and was pre-pressed at 1.96 kPa for 30 min. After pre-pressing, the curd was cut into six pieces (22 x 14 x 12 cm), each weighing around 3.7 kg, which were then molded into cylinders (Ø= 39.6 cm, h = 13.1 cm) and placed on a horizontal press for pressing. This involved three steps: 50 kPa for 30 min, 70 kPa for 1.5 h and then 120 kPa for 2 h. The cheeses were left in their molds overnight at room temperature (the curd temperature falling from 27°C to 23°C during this period) and then demolded on the third day of cheese making, when each cheese weighed around 2.5 kg. The demolded cheeses were then salted by immersion at 12°C for 10 h in saturated brine. After overnight drying at the same temperature, the cheeses were vacuum-packed in plastic bags (La Bovida, France) on the fourth day of cheese making and ripened at 13°C for 7 weeks.

Three variant cheese making itineraries, namely, Stir15min, Salt7h and Rip16°C (Figure 1), were also followed. These variants differed from the standard itinerary by only one parameter for each variant. Stirring time was reduced from 30 min to 15 min under the Stir15min itinerary; salting time was reduced from 10 h to 7 h under the Salt7h itinerary and ripening temperature was raised from 13°C to 16°C under the Rip16°C itinerary. All other production steps were the same as in the standard itinerary.

Sample collection

Samples were collected at nine different production stages, ranging from the inoculated milk to cheese ripened for 7 weeks (Figure 1, Table 1). Fresh cheese curds were collected at 0, 2 h and 4 h after the start of molding (C m , C m+2h , C m+4h ) and at the demolding stage (C dm ). During the ripening period, cheeses were sampled at the start of ripening (C 0w ) and after 4 and 7 weeks of ripening (C 4w , C 7w ).

The samples were subjected to microbiological, physicochemical and biochemical analyses as detailed in Table 1. After pH measurements, the milk samples were aliquoted into sterile jars.

Cheese samples were cut with a sterile knife to eliminate the rind (1 cm thick around the cheese surface). Then core samples of around 20 g were cut aseptically for microbial enumeration. The rest of the cheese was mixed using a blender to obtain small cubes with dimensions of less than 1 cm [START_REF] Thierry | Influence of Thermophilic Lactic Acid Bacteria Strains on Propionibacteria Growth and Lactate Consumption in an Emmental Juice-like Medium[END_REF] and then aliquoted into sterile jars.

Samples for microbial enumeration were stored at 4°C and analyzed within 24 h. Samples destined for physicochemical (except pH) and biochemical analyses (except volatile compounds) were stored at -20°C. For the analysis of volatile compounds, 2.5 ± 0.1 g of samples were added, in triplicate, to 22 mL PerkinElmer vials and stored at -80°C until analysis.

Microbiological enumeration

Before enumeration, 10 g of curd or cheese samples were added to the filter stomacher bag (Humeau, Treillière, France), and blended in 90 g 2% (w/v) trisodium citrate (45°C) for 3 min at maximum speed. Ten-fold serial dilutions of milk samples or citrate-cheese solutions were then prepared in peptone salt water. The populations of L. lactis, L. plantarum and P. freudenreichii in the samples were determined using the pour plate technique on M17 agar, MRS agar (pH 5.4) and YEL agar, respectively. The L. lactis strain was grown aerobically at 30°C for 24 h, while anaerobic incubation at 30°C was implemented for L. plantarum and P. freudenreichii for 48 h and 1 week, respectively, as described previously [START_REF] Deutsch | Lysis of Lysogenic Strains of Lactobacillus Helveticus in Swiss Cheeses and First Evidence of Concomitant Streptococcus Thermophilus Lysis[END_REF] .

Counting results were noted as CFU per gram of sample (CFU/g). The bacterial populations at each production stage were expressed as means ± standard deviation for four biological replicates under the standard itinerary, and for two replicates using the three variant itineraries.

Physicochemical characterization

pH was measured using a pH meter (WTW pH 3100, Weilheim, Germany) equipped with a puncture electrode (LoT406-M6-DXK GmbH, Mettler Toledo, Urdorf, Switzerland) and temperature probe (WTW 325/HC), by direct insertion into the fresh milk and cheese samples.

Other analyses were performed on samples after being thawed and equilibrated for 3 h at room temperature. The dry matter content was determined by drying samples for 7 h at 102 ± 2°C [START_REF] Idf | Fromages et Fromages Fondus-Détermination de l'extrait Sec Total[END_REF][START_REF] Idf | Crème et Lait Concentré Non Sucré Détermination de La Matière Sèche[END_REF] .

Fat content was measured using the Gerber-Van Gulik method with a butyrometer [START_REF] Lait | Détermination de La Teneur En Matière Grasse[END_REF] . The calcium content was assessed by atomic absorption spectroscopy [START_REF] Brule | Etude de la teneur en éléments minéraux des produits obtenus lors de l'ultrafiltration du lait sur membrane[END_REF] , results being first expressed as g/100 g of cheese and then converted into the content in dry matter (g/100g DM). Chloride concentrations were determined using a chloridometer based on coulometric titration (Corning 926 Chloride Analyzer, Humeau Laboratoires, La Chapelle-sur-Erdre, France); the results were first expressed as g/100g of cheese and then converted into the content in moisture (g/100g moisture).

Evaluation of proteolysis

The frozen samples were thawed and equilibrated for 3h at room temperature before nitrogen determinations. Total nitrogen (TN) was determined using the Kjeldahl method [START_REF] Idf | Determination of the Total Nitrogen Content[END_REF] ; it was then converted to total protein content by multiplying by a factor of 6.38 [START_REF] Idf | Determination of the Protein Content of Processed Cheese Products[END_REF] . The degree of proteolysis was characterized from the non-casein nitrogen content (NCN) (which corresponds to the nitrogen fraction soluble at pH4.6) and from the nitrogen content (NPN), corresponding to the nitrogen fraction soluble in 12% trichloroacetic acid. NCN and NPN were measured according to the method described by Gaucher et al. [START_REF] Gaucher | Proteolysis of Casein Micelles by Pseudomonas Fluorescens CNRZ 798 Contributes to the Destabilisation of UHT Milk during Its Storage[END_REF] .

Extraction and quantitation of sugars and organic acids by HPLC

Sugars and organic acids in the samples were quantified using high performance liquid chromatography (HPLC). The extraction method was adapted from that described by Leyva Salas et al. [START_REF] Leyva Salas | Identification and Quantification of Natural Compounds Produced by Antifungal Bioprotective Cultures in Dairy Products[END_REF] . Briefly, frozen samples were first thawed and equilibrated for 3 h at room temperature. Then milk samples were directly diluted 40-fold in the H 2 SO 4 solution (0.005 M) and filtered in Vivaspin® 2 (10 kDa MWCO) by centrifugation at 9000 g for 20 min. Curd and cheese samples were first blended in deionized water at 40°C (1:4 w/w) in a filter bag and incubated at 40°C for 1 h. The suspensions were then centrifuged (3000 g, 30 min, 4°C) and the supernatants filtered on Whatmann 40 paper. The filtrates were diluted in the H 2 SO 4 solution (0.005 M): 6-fold for C m curds, 4-fold for C dm curds and C 0w cheeses, and 2-fold for ripened cheeses (C 4w and C 7w ). The diluted filtrates were then filtered once more (CHROMAFIL® Xtra PVDF-45/13, 0.45 μm pore size, Machery-Nagel GmbH & Co. KG, Germany). HPLC analysis and the identification of metabolites were performed according to the method described by Leyva Salas et al. (2019) [START_REF] Leyva Salas | Identification and Quantification of Natural Compounds Produced by Antifungal Bioprotective Cultures in Dairy Products[END_REF] .

Analysis of volatile compounds by GC-MS

Volatile compounds were analyzed using headspace (HS) trap extraction coupled to gas chromatography-mass spectrometry (GC-MS). The principle of HS-CGMS has been described elsewhere in detail, including the linearity ranges and limit of detection of 6 of the compounds identified in the present study [START_REF] Pogačić | A Methodological Approach to Screen Diverse Cheese-Related Bacteria for Their Ability to Produce Aroma Compounds[END_REF] . The samples were injected in a random order, with standards (mixture of nine volatiles: four esters (ethyl acetate, ethyl propanoate, ethyl butanoate and ethyl hexanoate), two aldehydes (3-methylbutanal and benzaldehyde), two ketones (2-heptanone and 2nonanone), 2,3-butanedione, dimethyl disulfide and 3-methylbutanol) and blank samples (boiled deionized water) to monitor possible MS drift and carryover. Compounds were eluted on an Elite-WAX ETR (30 m x 0.25 mm ID x 0.25 µm, PerkinElmer USA) column. They were identified by comparing their mass spectral data and linear retention indexes (LRI) calculated on a polar column with that of reference standard compounds, and with data from Library NIST 2008 (Scientific Instrument Services, Ringoes, NJ, USA) and PubChem. The data were processed as described elsewhere [START_REF] Pogačić | A Methodological Approach to Screen Diverse Cheese-Related Bacteria for Their Ability to Produce Aroma Compounds[END_REF] . Briefly, raw data files were converted to time-and mass-aligned chromatographic peaks areas, using the XCMS open source package implemented with the R statistical language [START_REF] Smith | XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification[END_REF] . The volatile compounds were semi-quantitied using the abundance of one selected mass fragment (m/z), in arbitrary units. Moreover, previous calibration curves of diacetyl spiked in cream [START_REF] Leyva Salas | Identification and Quantification of Natural Compounds Produced by Antifungal Bioprotective Cultures in Dairy Products[END_REF] were used to calculate approximate concentrations of diacetyl in milk after prematuration step.

Statistical analysis

The effect of ripening time, process parameters and their interactions were considered to quantitatively identify, without bias, levers for organoleptic quality. Data were analyzed in a mixed model framework, which explicitly accounted for the correlations between repeated measurements within each type of cheese making, as follows:

(1) where is the observed quantity of metabolites under itinerary (Std, Stir15min, Salt7h,

Rip16°C) at the sampling stage (M LAB , M LAB+P , C m , C dm , C 0w , C 4w , C 7w ) for cheese making k (1, 2, 3, 4)
. The symbols and represent the fixed effects due to the itinerary and the sampling stage, respectively. is the interaction effect between the itinerary and the sampling stage. The symbol refers to the random effects of cheese making, and accounts for the correlation between repeated measurements within each cheese making cycle. We assumed that are independent and normally distributed and that followed the normal probability distribution with a mean equal to zero. We also assumed the independence between these random effects and .

Three models of mixed analysis of variance were performed depending on the question and available data and were fitted by maximizing the log-likelihood using the nlme R package.

-

Effect of milk maturation on milk composition

We first analyzed four variables (two bacterial counts, pH, dry matter and 10 metabolites and volatile compounds, see above) measured at the first two sampling stages under the standard itinerary. Model (1) was simplified into a model with a single fixed effect due to the sampling stage (M LAB, M LAB+P ) and the cheese making random effect. From the test of the fixed effect, we considered metabolites and volatile compounds with p-values lower than 0.05 as being statistically different between the M LAB+P and M LAB sampling stages.

-

Comparison of the four itineraries during ripening

We focused on the compounds measured during all four itineraries (standard + three variants) at the three last sampling stages (C 0w , C 4w , C 7w ). For each compound, we first of all tested the significance of the interaction term in the model (1) using a likelihood ratio test, and only retained it in the model if the p-value was greater than 0.05. In a second step, we computed estimated marginal means for the itinerary fixed effect and performed comparisons between each variant in the itinerary (Stir15min, Salt7h and Rip16°C) and the standard itinerary (Std) using the emmeans R package.

Raw p-values were adjusted for multiple comparisons using the Tuckey method and the level of significance was fixed at 0.05. For the (I × PS) interaction, the sum of the values for the five production stages was divided by 5, and the results were subsequently referred to as the "global mean" of all production stages. For the (I × RS) interaction, the sum of the values for the three ripening stages was divided by 3 and the results were subsequently referred to as the "global mean"

for ripening stages (2). The global means under the variant conditions were then compared to the standard.

Plate counts, pH and dry matter content at each production stage were expressed as means in four biological replicates for the standard itinerary, and two replicates for the variant itineraries. Other concentrations were expressed as means in two biological replicates for both the standard and variant itineraries.

-Impact of sampling stages

We analyzed 27 variables (three bacterial counts, pH, salt, dry matter and 21 metabolites and volatile compounds) from the five last sampling stages (C m , C dm , C 0w , C 4w , C 7w ) under the Stir15min and Std itineraries. We followed the same two-step procedure as described in the paragraph above.

All these analyses were implemented using R software version 3.6.1.

-Principal Component Analysis

A principal component analysis (PCA) was performed with 31 centered and scaled variables (three bacterial counts, expressed as log-transformed data, pH, dry matter, MFFB, Ca2, Cl, NCN, NPN and 21 metabolites and volatile compounds) for the 32 cheese samples, using XLSstat for Microsoft Excel (Addinsoft 2019, https://www.xlstat.com.).

RESULTS

3.1-Evolution of composition during cheese manufacture and ripening

Dry matter (DM) and pH were chosen as physicochemical indicators to assess the repeatability and reproducibility of the Std cheese. These parameters were monitored in four biological repetitions of the cheese (Figure 2). Before cheese making, the M LAB milk contained 11.65 ± 0.26 g/100 g DM and the pH was 6.59 ± 0.02. At the molding stage, the DM content in cheese was around 40.53 ± 3.21 g/100 g. It then rose progressively with pressing to reach 52.03 ± 1.37 g/100 g at demolding.

The DM content remained stable thereafter. The pH in cheese fell from 6.36 ± 0.05 to 5.45 ± 0.07 during molding and remained at around 5.19 ± 0.06 until the end of ripening. For the DM content and pH, the standard deviation (SD) was less than 3% of the mean value at all stages, thus confirming the repeatability and reproducibility of Std cheese manufacture. The only exception was observed regarding DM at molding, where the SD was 8%. This relatively high SD value was due to the non-homogeneity of the fresh cheese sample at that time.

In order to evaluate the influence of variant itineraries on the physicochemical evolution of cheese, six composition parameters (i.e., DM, pH, protein, fat, calcium and chloride) were monitored throughout the production of cheeses under both the standard and variant itineraries.

The physicochemical composition of cheeses at each production stage is summarized in Table 3.

The evolution of pH in variant cheeses displayed similar behavior and no significant differences were found compared to the Std cheese. The global mean DM content in Stir15min cheese was 3.9% lower than in the Std cheese, which corresponded to a difference of 1.98 g/100g cheese (p<0.05). Salt7h cheese also contained significantly less DM than the Std cheese and the difference was 1.5% (p<0.05).

The total protein and fat contents in dry matter did not vary during production regardless of the itinerary. Their contents were around 42.6 g/100g DM for total protein and 47.9 g/100 g DM for fat.

However, the moisture content in the fat-free basis (MFFB) of cheese was significantly higher in Stir15min cheese than in the Std cheese (p<0.001), with a difference of 2.4%. This was due to a lower DM content in Stir15min cheese.

The calcium content in Std cheese increased gradually from 0.61 ± 0.09 g/100g cheese at molding to 0.74 ± 0.04 g/100g at the end of ripening. Compared to Std, the Stir15min itinerary induced a significantly lower calcium content in cheese (-5.9%, p<0.05). However, the calcium content in dry matter did not differ significantly (p>0.1). The chloride level in the core of the Std cheese were initially 0.35 ± 0.05 g/100g, corresponding to 0.72 ± 0.08 g/100g moisture. This increased over time to reach 0.83 ± 0.03 g/100 g cheese at four weeks of ripening, and was then maintained at around 0.8 g/100g (1.8 g/100g moisture) during the later ripening period. Chloride levels in cheese and in the moisture fraction were both significantly affected by the interaction between the ripening stage and the itinerary (I  RS). Stir15min cheese displayed a much more rapid rise in the chloride content during the early ripening period and this was ultimately 10.9% higher than in the cheese (p<0.05). Compared to the Std cheese, the Salt7h cheese contained 8.6% less chloride in the cheese (p = 0.054) and 10.9% less chloride in moisture (p<0.05).

3.2-Overview of bacterial evolution during cheese manufacture and ripening

During the present study, the kinetic growth of the bacterial consortium was monitored during cheese production according to the standard itinerary (Std). This enabled an assessment of the repeatability and reproducibility of the Std cheese and the construction of a standard profile for microbial evolution in a model cheese (Figure 2). The standard deviation of population values was less than 0.5 log 10 units for all bacterial strains at all stages, thus confirming the repeatability and reproducibility of the Std cheese.

The L. lactis and L. plantarum strains were inoculated in standardized milk at 5.7 and 5.2 log 10 CFU/g, respectively, while P. freudenreichii was inoculated at 6.1 log 10 CFU/g in matured milk.

During the prematuration step (14°C for 18 h), the L. lactis population increased significantly Table 2 summarizes the populations of each strain and in the four cheese types. In Stir15min cheese, the global mean of the L. lactis population was significantly lower than in Std cheese (p<0.05). However, the difference was less than 0.5 log 10 units at each production stage. During the ripening period, a significant (I  RS) interaction effect was detected for L. lactis (p<0.001), which means that the course of its evolution during ripening stages was dependent on the itinerary.

Between 0 to 7 weeks of ripening, the population of L. lactis decreased under both Std and Rip16°C itineraries, but this fall was more marked under Rip16 °C than Std (-1.43 and -0.61 log 10 CFU/g, respectively). After seven weeks of ripening, the L. lactis population in Rip16°C cheese was significantly lower than in Std cheese (p<0.001) with a difference of 0.8 log 10 units.

3.3-Evolution of nitrogen fractions

Figure 3 shows the evolution of nitrogen fractions under all itineraries. In standard conditions, NCN represented less than 6% of total nitrogen (TN) before ripening, and NPN was lower than 2% TN. At the start of ripening, the nitrogen fractions in cheese at 13°C increased rapidly and reached 14.3 ± 0.6% for NCN and 5.8 ± 0.1 % for NPN at four weeks of ripening. Thereafter, the nitrogen fraction contents continued to increase but at a lower rate. By seven weeks of ripening, the Std cheese contained 16.7 ± 0.1% NCN and 7.2 ± 0.2% NPN.

The nitrogen fractions in Stir15min and Salt7h cheeses did not differ from those in the Std.

However, cheese ripened at 16°C (Rip16°C) displayed significantly higher levels of proteolysis (p<0.05) when compared to the Std cheese, with an NCN content of 18.06 ± 0.69% (+ 8.2%) and a NPN content of 8.90 ± 0.08% (+23.2%) after seven weeks of ripening. An interaction between the effects of ripening stage and itinerary was observed for the NPN content, which increased much more rapidly at a higher ripening temperature.

3.4-Evolution of sugar and organic acid levels in milk and cheese during production

The complete metabolome of milk before and after prematuration can be seen in Table 4. Lactose and citric acid (48 and 2 g/L, respectively) were the two main carbon sources in milk and did not vary significantly after prematuration. Lactic acid was only detected in matured milk. The acetic acid content increased significantly by 3.4-fold (p=0.09) during prematuration. Notable changes to the volatile profile were also observed, with an increase in several compounds in mature milk: hexanoic acid (1.2-fold), octanoic acid (1.7-fold), diacetyl (17.6-fold) (p<0.05) and acetoin (6.2fold, p = 0.054).

Overall, 12 molecules were detected, including two sugars (lactose and galactose) and eight organic acids (pyruvic, citric, succinic, lactic, acetic, propanoic, butanoic and phenyllactic acids), as well as two ketones (diacetyl and acetoin). However, the levels of diacetyl and acetoin were too low to be reliably quantified by HPLC, so instead they were semi-quantified from GC-MS data. According to the previously published calibration curve [START_REF] Leyva Salas | Identification and Quantification of Natural Compounds Produced by Antifungal Bioprotective Cultures in Dairy Products[END_REF] diacetyl reached between 300 and 400 ng/g of milk during the prematuration step.

Table 5 presents the concentrations of ten compounds in both standard and variant cheeses throughout manufacture and ripening. All molecules displayed significant variations in their concentrations in the cheeses during production (p<0.001). Based on the trends in evolution, three groups of molecules were distinguished: (a) lactose and citric acid were consumed over time, (b)

galactose and lactic acid were first produced during cheese manufacture and then consumed, and (c) pyruvic, succinic, acetic, propanoic, butanoic, phylactic acids were produced and accumulated over time (Figure 4). During the standard cheese manufacture, lactose levels fell rapidly during molding to reach less than 10 g/kg by demolding. Lactose continued to be consumed thereafter and the level was at 0.79 ± 0.06 g/kg by seven weeks of ripening. Citric acid concentrations decreased to 0.15 ± 0.01 g/kg in cheese before ripening and were not detectable in ripened cheeses. Levels of other organic acids rose throughout production, especially during the ripening period.

The Stir15min itinerary was compared with the standard itinerary throughout the production cycle (Table 5). The interaction effect between itinerary and production stage (I × PS) was significant for citric, succinic, lactic and acetic acids (p<0.05). Stir15min cheese had a lower citric acid content at the molding stage (-16.5%), and a lower lactic acid content during the middle stages of cheese production (around -10%). At the end of the ripening, it had a higher content in succinic (+ 40.2%) and acetic acids (+18.3%). Other molecules did not display any significant (I × PS) interaction effect. However, the global mean of lactose contents was higher in Stir15min cheese (+43.5%), while that of pyruvic and butanoic acids was lower (around -20%). Propanoic acid could only be detected by HPLC during the ripening phase. The itinerary by ripening stage (I × RS) interaction effect was significant for propanoic acid because of its higher content in Stir15min cheese at seven weeks of ripening (+13.6%). When all itineraries were compared during ripening, the (I × RS) interaction effect was significant for all molecules except galactose. The only difference between

the Salt7h cheese and Std cheese was the succinic acid content, which was +57.0% higher in the latter. The Rip16°C cheese was the most contrasted sample. The levels of ten molecules differed significantly from those in the Std cheese (p<0.05). Six organic acids accumulated during ripening (pyruvic, succinic, acetic, propanoic, butanoic and phenyllactic acids) and were produced more rapidly in Rip16°C than in Std (Figure 4 A-F). The differences in concentration ranged from 34.1% to 158.7%, depending on the molecule (p<0.001). The contents of these acids were also the highest among all cheese types. By contrast, the lactic acid content in Rip16°C cheese by seven weeks of ripening was 45.3% (p<0.001) lower than in the Std cheese and the lowest among all cheese types.

3.5-Volatile compound profile in cheese

A total of 14 volatile compounds was detected (Table 6), including four ketones (acetone, butan-2one, diacetyl, acetoin), six acids (acetic, propanoic, butanoic, hexanoic, octanoic, and methylbutanoic acids), three alcohols (propan-1-ol, 2-methylbutan-1-ol and 1-hydroxypropan-2one), and one sulfur compound (dimethyl disulfide). All compounds were detectable in cheese from the molding stage, but they varied in abundance over manufacture and ripening. Propan-1-ol, 2methylbutan-1-ol, DMDS and the acids were produced abundantly during ripening and their abundance increased as a function of ripening time. In cheeses ripened for four and seven weeks the high concentrations in acetic, propanoic and butanoic acids induced an overloading of the GC capillary column, thus impairing the accuracy of their quantification. These three acids were therefore quantified using HPLC and only their later concentrations were considered for statistical analyses (Table 5).

The comparison between Std and Stir15min cheeses showed a significant (I × PS) interaction effect for 2-methylbutanol, 2-methylbutanoic acid (p<0.05). Stir15min cheese always contained higher levels of those two molecules during the ripening period, while the difference versus the Std cheese diminished with ripening time (Figure 4G and4H). After seven weeks of ripening, the 2-methylbutanol and 2-methylbutanoic acid contents in Stir15min cheese were more than 10% higher than in Std. DMDS was significant in both (I × PS) and (I × RS) interaction effects, as the Stir15min cheese contained 51.8% (p<0.001) less DMDS than Std at the end of ripening. As for molecules where the interactions were not significant, the global mean of diacetyl and acetol intensities in ripened cheese was lower in Stir15min cheese, the difference being -47.0% (p<0.1) and -35.8% (p=0.056), respectively.

During ripening, the volatile compound profile of Salt7h cheese did not differ from that of Std.

Concerning the Rip16°C cheese, the (RS × I) interaction effect was significant for 2-methylbutanol, 2-methylbutanoic and hexanoic acids (p<0.001). Compared to the Std cheese, the contents in 2methylbutanol (p<0.001), 2-methylbutanoic (p<0.05) and hexanoic acids (p<0.1) were higher at four weeks of ripening (Figure 4G-I). At the end of ripening, the Rip16°C cheese contained +82.7% 2-methylbutanol, +60.4% 2-methylbutanoic and +49.8% hexanoic acids (p<0.001).

3.6-Global analysis of the cheese curd profile during cheese production

As shown in Figure 5, PCA was performed on 31 variables describing the microbial and biochemical composition of the cheeses. The first two principal components (F1 and F2) accounted for 83.04% of total variability. F1, which described 62.65% of variability, was positively associated with proteolysis parameters, the NaCl content, viable counts of L. plantarum and P. freudenreichii and most acids and alcohols (Figure 5A). By contrast, F1 was negatively associated with viable counts of L. lactis, pH, and the contents in sugars and citric acid, two ketones (acetone, butan-2one) and MFFB. F2, which accounted for 20.38% of variability, was positively correlated with contents in diacetyl, acetoin, lactic acid and acetol, and viable counts of L. lactis.

The observation map (Figure 5B), showed five cheese groups distinguished as a function of ripening stages. Fresh cheese curds collected at the molding stage (C m ) appeared in the low left quadrant. At the manufacturing steps, the points corresponding to demoulding (D) and after salting (C0w) moved vertically and positively along F2 axis concomitantly to L. lactis growth, lactate, acetol and diacetyl production. Cheeses after demolding (D) and at the start of ripening (C 0w )

showed similar profiles and were located together in the upper left quadrant. During ripening, points progressively moved positively along F1 axis and negatively along F2 axis concomitantly with L. plantarum and P. freudenreichii growth, reduction of lactate, acetol, diacetyl and production of esters and proteolysis. Cheeses ripened for four weeks (C 4w ) and seven weeks (C 7w ) were mainly in the upper right and lower right quadrants, respectively. Fresh curds were negatively associated with F1 and were therefore characterized by higher pH values, moisture and lactose contents, and viable L. lactis counts. Ripened cheeses (C 4w and C 7w ) were mainly associated with higher counts of L.

plantarum and P. freudenreichii, higher proteolysis levels and higher contents in most flavor compounds. Demolded cheeses (C dm ) and young-aged cheeses (C 0w ) were separated from cheeses at molding (C m ) and old-aged cheeses on the F2 and were characterized by high L. lactis counts and higher contents in acetoin, diacetyl, lactic acid, and acetol. In order to facilitate comparison of the four cheese types, the cheeses samples were color-coded relative to the itinerary applied (Figure 5B). In the groups of cheeses before ripening, the cheeses produced with variant processes were not differentiated from the Std cheese. However, during ripening, cheese ripened at 16°C for four weeks displayed the same profile as the cheese ripened at 13°C for seven weeks. The accelerated ripening at higher temperature was more obvious among cheeses ripened at 16°C for seven weeks.

Their profiles contrasted with other cheeses, revealing the highest aroma content and more advanced proteolysis.

4-Discussion

4.1-Standard cheeses as a model cheese

In this work, we developed a model semi-hard cheese with high reproducibility (less than 3% of variation) in terms of moisture (54%), fat/dry matter (48%), protein/dry matter (42%) and growth of starter and ripening bacterial species. For that purpose, we focused in particularly on optimizing the cutting size of curd before stirring, the molding step, the pressing step using a horizontal press and progressive pressing, and the use of plastic vacuum bags to prevent any microbial contamination of the cheese surface during ripening. We grew our home-made starter cultures using publicly available strains obtained from the CIRM-BIA collection. The ability of L. lactis ssp. lactis biovar.

diacetylactis CIRM-BIA1026 to produce diacetyl, and the previous observation of low lysis under carbon source deficiency conditions (data not shown) were features of interest in this work. L. plantarum and P. freudenreichii were chosen as adjunct bacteria as they contribute to cheese texture and flavor by proteolysis and the production of flavor components [START_REF] Tarnaud | Differential Adaptation of Propionibacterium Freudenreichii CIRM-BIA129 to Cow's Milk Versus Soymilk Environments Modulates Its Stress Tolerance and Proteome[END_REF] . Compliance with the schedule guaranteed a highly reproducible growth of each strain separately during preparation of the inoculum and overall in the milk during the prematuration step and in cheeses. We suggest that this model cheese could be reproduced by other scientists interested in the biochemistry of semi-hard cheeses. Until now, most scientific studies performed on cheese have used Cheddar cheese [START_REF] Desfossés-Foucault | Assessment of Probiotic Viability during Cheddar Cheese Manufacture and Ripening Using Propidium Monoazide-PCR Quantification[END_REF][START_REF] Leclercq-Perlat | Temperature and Relative Humidity Influence the Ripening Descriptors of Camembert-Type Cheeses throughout Ripening[END_REF][START_REF] Fernández-Esplá | Effect of Adding Propionibacterium Shermanii NCDO 853 or Lactobacillus Casei Ssp. Casei IFPL 731 on Proteolysis and Flavor Development of Cheddar Cheese[END_REF] which is produced in large quantities throughout the world but which includes a specific step of dry salting (salt is added directly to the grains: cheddarization). The production of our model cheese, which includes a brine salting step, is representative of a broader panel of semi-hard cheeses such as Tommes, Raclette, Edam, Gouda, Manchego, Provolone and Castelmagno cheeses.

-Microbial and biochemical changes in Std cheese

The activity of microorganisms during cheese making and ripening induces modifications to all curd constituents which in turn leads to the development of cheese flavor. Flavor development is a dynamic biochemical process that is influenced by (i) the type and composition of milk, (ii) processing parameters, and (iii) the microorganisms and enzymes present in the cheese matrix. The cheese microbiota is the primary source of enzymes that influence flavor development through the degradation of carbon sources, proteins and lipids.

4.2.1-Utilization of lactose, production of lactic and acetic acids by LAB

The conversion of lactose to lactic acid is essential for the production of all types of cheeses involving a bacterial acidification step. Lactic acid causes acidification and a refreshing acid taste, which is particularly noticeable in young cheeses. In the Std cheese we followed the degradation of lactose in milk, in curd and in cheeses up to seven-week ripened cheeses. As expected, the utilization of lactose was very rapid during the acidification of milk and curd and was concomitant with L. lactis growth. It occurred during the prematuration step and the acidification of curd during pressing (Figure 1; Table 5) and 80% of lactose was consumed at the demolding step. The L.

plantarum strain was also able to use lactose (API gallery results, data not shown) and to produce lactic acid. Lactose levels were very low (nearly zero) as from the fourth week of ripening.

Acidification was progressive during ripening because no fungi used the lactic and acetic acids released by lactic acid bacteria. Acetic acid could be produced by all three strains and probably resulted from the successive fermentations by L. lactis, L. plantarum and then P. freudenreichii during ripening, the latter being the main contributor, as suggested by the time-course of production (Figure 4C).

4.2.2-Utilization of citric acid, production of diacetyl and acetoin by L. lactis

Citric acid is another carbon source for certain lactic acid bacteria strains. The strain of L. lactis used during the present study belongs to the biovar diacetylactis. As early as the prematuration step in the milk tank at 14°C (M LAB+P ), it produced high levels of diacetyl (Table 4, fold change  17.6)

and consumed high levels of citric acid (Table 4, 90% at the demolding step) in line with the findings of Passerini et al. [START_REF] Passerini | The Carbohydrate Metabolism Signature of Lactococcus Lactis Strain A12 Reveals Its Sourdough Ecosystem Origin[END_REF] . The high production of diacetyl during the early stage of cheese production imparts the formation of a buttery flavor. Oxygen is required for this synthesis [START_REF] Liu | From Waste to Taste-Efficient Production of the Butter Aroma Compound Acetoin from Low-Value Dairy Side Streams Using a Natural (Nonengineered) Lactococcus Lactis Dairy Isolate[END_REF] .

Diacetyl production could also be explained by the introduction of oxygen being pumped and stirred in the prematuration tank [START_REF] García-Quintáns | The Citrate Transport System of Lactococcus Lactis Subsp. Lactis Biovar Diacetylactis Is Induced by Acid Stress[END_REF] . The production of diacetyl reached its highest level after four weeks of ripening. The diacetyl content had fallen by the end of the ripening, which probably resulted from its reduction into acetoin by lactic acid bacteria. Acetoin is generally produced in much larger quantities, i.e. 10-to 50-fold higher than diacetyl concentrations [START_REF] Hassan | Flavour Compounds in Cheese (Review)[END_REF] . It is noteworthy that the partial removal of lactoserum, which was replaced by warm water before molding, led to the loss of one quarter of the diacetyl (and other soluble metabolites) produced before this step.

4.2.3-Primary and secondary proteolysis, a necessary step for volatile production by the bacterial consortium

Proteolysis is essential to flavor formation. The production of small peptides and free amino acids results from the activity of the added coagulant and/or of milk plasmin, in conjunction with cell envelope microbial proteinases and cytoplasmic peptidases [START_REF] Mcauliffe | Symposium Review: Genomic Investigations of Flavor Formation by Dairy Microbiota[END_REF] . Short peptides and amino acids contribute to the basic flavor of cheeses. Free amino acids are further catabolized into many soluble and volatile compounds. Some of these compounds impact cheese aroma, such as volatile carboxylic acids, aldehydes and alcohols. A range of amino acid-converting enzymes are involved in their formation [START_REF] Smit | Flavour Formation by Lactic Acid Bacteria and Biochemical Flavour Profiling of Cheese Products[END_REF][START_REF] Yvon | Cheese Flavour Formation by Amino Acid Catabolism[END_REF] .

Primary and secondary proteolysis principally occurred during the four first weeks of ripening (Figure 3). According the literature, the primary proteolysis (Figure 3, NCN raising from 6 to 16 % of nitrogen fraction) is ensured by surface exposed protease from L. lactis, combined with those of L. plantarum. The secondary proteolysis (Figure 3, NPN) is difficult to attribute. Three aroma compounds derived from the catabolism of amino acids: 2-methylbutan-1-ol and 2-methylbutanoic acid likely resulted from the catabolism of isoleucine by P. freudenreichi. [START_REF] Thierry | Conversion of L-Leucine to Isovaleric Acid by Propionibacterium Freudenreichii TL 34 and ITGP23[END_REF][START_REF] Thierry | Isovaleric Acid Is Mainly Produced by Propionibacterium Freudenreichii in Swiss Cheese[END_REF] . All of the corresponding pathways have been described as being expressed in P. freudenreichii [START_REF] Dalmasso | A Temporal -Omic Study of Propionibacterium Freudenreichii CIRM-BIA1T Adaptation Strategies in Conditions Mimicking Cheese Ripening in the Cold[END_REF] . DMDS results from the catabolism of methionine [START_REF] Yvon | Cheese Flavour Formation by Amino Acid Catabolism[END_REF] .

4.2.4-Production of volatiles resulting from P. freudenreichii metabolism

P. freudenreichii produced aroma compounds via three main pathways: the fermentation of lactic acid, lipolysis, and branched-chain amino acid catabolism.

Lactic acid can be metabolized by a number of pathways to various compounds which may contribute to cheese flavor. P. freudenreichii is a ripening culture that is widely used in the manufacture of Swiss-type cheeses, where it uses lactic acid as the main carbon source [START_REF] Thierry | Beneficial. Propionibacterium Spp. In Encyclopedia of Dairy Sciences 2nd Edition, Four-Volume set[END_REF] .

Consequently, its growth is reliant on the consumption of lactic acid only. P. freudenreichii converts lactic acid into propanoic acid, acetic acid and carbon dioxide. Unlike acetic acid and carbon dioxide, which are also produced by LAB, propanoic acid was only produced by P.

freudenreichii. It accumulated gradually in the cheese during ripening.

Milk fat is another essential source for the development of cheese flavor during ripening. Lipolysis, i.e. the hydrolysis of milk fat by lipolytic esterases, results in the formation of free fatty acids (FFAs, namely acids in Table 6 and Figure 5), which even at low concentrations will contribute to cheese flavor, either positively (desirable pungent notes) or negatively (rancid notes), depending on the type of cheese [START_REF] Collins | Lipolysis and Free Fatty Acid Catabolism in Cheese: A Review of Current Knowledge[END_REF][START_REF] Thierry | Lipolysis and Metabolism of Fatty Acids in Cheese[END_REF] . FFAs also act as precursors for certain flavor compounds such as methylketones, secondary alcohols, esters, and lactones [START_REF] Mcauliffe | Symposium Review: Genomic Investigations of Flavor Formation by Dairy Microbiota[END_REF] .

Microorganisms are the principal sources of lipolytic enzymes in cheese. In ripened cheese involving P. freudenreichii fermentation, the species is considered to be the main actor in milk fat lipolysis. It has been demonstrated that P. freudenreichii is responsible for the formation of up to 96% of FFAs in Emmental cheeses [START_REF] Dherbécourt | Time Course and Specificity of Lipolysis in Swiss Cheese[END_REF] , thanks to the activity of an extracellular lipolytic esterase [START_REF] Mukdsi | The Secreted Esterase of Propionibacterium Freudenreichii Has a Major Role in Cheese Lipolysis[END_REF] .

By contrast, LAB esterases have received much less attention because they are mainly intracellular and have weak lipolytic activity on milk fat [START_REF] Collins | Lipolysis and Free Fatty Acid Catabolism in Cheese: A Review of Current Knowledge[END_REF][START_REF] Thierry | Lipolysis and Metabolism of Fatty Acids in Cheese[END_REF] . In our model cheese, the butanoic and hexanoic acids generated during ripening likely resulted from lipolysis by P. freudenreichii.

4.3-Influence of the fine-tuning of process parameters on the profile of aroma compounds

4.3.1-Effect of a reduced salting time on the formation of aroma compounds

The Sal7h itinerary differed from Std by a 10.9% reduction in the salt concentration of the water phase of cheese. It also resulted in a significant rise in succinic acid levels after seven weeks of ripening (57%, Table ). This delayed effect on succinic acid could be explained by the slow diffusion of salt (sodium chloride) to the cheese core. Indeed, in pressed and brine-salted cheeses, diffusion rates range from 0.1 to 0.45 cm 2 /day 45 . The results of chloride measurements showed that NaCl was only dispersed homogeneously in the core of the cheese after four weeks of ripening, in line with the size of the model cheeses (see 2.2). We can hypothesize that a lower salt concentration in the core might activate bacterial metabolic activity and cause an accumulation of succinic acid that cannot be degraded by any of the three inoculated species which do not possess a complete tricarboxylic acid pathway capable of metabolizing succinic acid. To the best of our knowledge, succinic acid exerts no influence on cheese flavor, but the lower salt level might directly impact cheese taste (not tested). Therefore, under the conditions of the present study, the reduction in salt did not modify the profile of aroma compounds. It is likely that the salt reduction applied was not sufficiently contrasted; salt levels can affect both bacterial growth and metabolic activities in a strain-dependent manner, as previously illustrated on L. lactis 46,47 and P.

freudenreichii in Swiss-type cheeses 48,49 .

4.3.2-Effect of a reduced stirring time on the formation of aroma compounds

A reduction in stirring time selectively reduced the buttery flavor-associated compounds produced by L. lactis and increased the amounts of some compounds that are associated with "aged-cheese" and 'Swiss-cheese related flavor' notes, and resulted from the activities of P. freudenreichii. At both ripening times (after four and seven weeks), the reduction in stirring time that we applied increased i) the lactose content throughout manufacture and ripening (Table 4 and5), ii) the NaCl content (Table 3), iii) the secondary proteolysis during ripening (Figure 3), iv) moisture during cheese making (Table 3).

Because of the higher contents in lactose (>50 %) and MFFB, we expected that the LAB would produce more lactic acid. However lactic acid content of Stir15min was not significantly higher than in the Std cheeses. One explanation might be the counter-effect of salt (+10.9%) on the overall equilibrium of the metabolic ecosystem, thus illustrating the complexity of predictive microbiology when one process parameter exerts pleiotropic effects (salt, lactose, MFFB).

The slightly higher secondary proteolysis during ripening, currently attributed to higher bacterial metabolic activities -since no lysis was observed-might rather have resulted from the proteolytic activity of L. plantarum, as previously demonstrated for some L. plantarum strains in Cheddar cheese 50,51 . Moreover, Stir15min decreased DMDS (-52%, p<0.001), diacetyl and acetol concentrations (-47% and -36%; p<0.1). The higher level of MFFB (+2.4% in ripened cheeses) caused a positive effect on P. freudenreichii activity and significantly increased levels of aroma compounds such as propanoic, acetic, butanoic and succinic acids (from +14% up to +159%; p<0.05), and 2-methylbutanol and 2-methylbutanoic acid, during the ripening period (+15% and +16% at C7w, p<0.001 and p<0.1) (Table 5). It is noteworthy that the fine-tuning of stirring time on the day of cheese making can lead to a drastic rise (+40.2%) in succinic acid levels, two months after production.

4.3.3-Effect of a higher ripening temperature on aroma compound formation

After both periods of ripening, a higher ripening temperature significantly increased secondary proteolysis (p<0.05, Figure 4), the contents in aroma compounds (2-methylbutanol and carboxylic acids such as acetic and propanoic acids) and lipolysis, as indicated by butanoic and hexanoic acids (Table ). The rise in acids seen during ripening (from +34% to 159%; p<0.001 at 7 weeks of ripening, Table , Figure 4) arose from P. freudenreichii metabolism. A higher lactic acid consumption (-45.3 %, p<0.05) was also a sign of more pronounced P. freudenreichii activity. The ACP built from the global metabolome of cheeses (Figure 5) showed that cheeses ripened at 16°C

for four weeks displayed the same profile as the cheeses ripened at 13°C for seven weeks, indicating that a three degree rise in the ripening temperature accelerated the maturing process by three weeks. Similarly, in traditional Italian cheeses, an increase of 4°C in the ripening temperature has been seen to promote the expression of genes related to proteolysis, lipolysis and amino acid/lipid catabolism, and significantly increase the cheese maturation rate [START_REF] De Filippis | Metatranscriptomics Reveals Temperature-Driven Functional Changes in Microbiome Impacting Cheese Maturation Rate[END_REF] . These authors suggested the contribution of non-starter lactic acid bacteria to the aroma profile of cheeses.

Bacterial populations little affected by process parameters

The changes observed in the metabolome resulted from changes to bacterial activity and not from differences in cultivable cell counts.

The variant itineraries modified the aromatic profile of cheese by modulating the metabolic activity of the cheese community. This modulation could either be direct, i.e., temperature changing the metabolic activities of bacteria, and/or indirect, i.e., stirring and salting time initially changed the physicochemical composition of the cheese matrix, which in turn influenced bacterial activity.

It is generally accepted that process parameters can impact the growth of bacterial species during food fermentation. Surprisingly, during our study, the changes made in variant itineraries did not affect growth kinetics, probably because we only adjusted the process parameters to a minor and insufficient extent. The only effect observed was a significant loss of the cultivability of L. lactis during ripening, which might have resulted from both cell lysis and/or the switch from a cultivable state to a viable but non-cultivable (VBNC) state 52,53 . But although L. lactis cells are in a VBNC state, their metabolic activity may still influence the aromatic profile of cheeses.

Fine-tuning of process parameters to produce more healthy and hedonic cheeses

Our aim during this work was to evaluate how fine-tuning of process parameters during the production process modulate bacterial aroma compounds of cheese. The conclusions are that, even without impact on microbial growth, fine-tuning of process parameters, like stirring time or ripening temperature influence the final organoleptic quality of cheese by promoting specific bacterial metabolisms. A higher ripening temperature and a shorter stirring time increased the content in Propionibacterium-related aroma compounds. It would have been interesting to perform sensory evaluations of the cheeses in order to determine whether the differences in flavor were perceived and appreciated or not. However, a 10.9% reduction in salt content did not influence the amount of quantified metabolites in the cheeses, excepted for succinic acid. It nevertheless remains noteworthy that this shorter salting step would be beneficial to consumer health. Indeed, there have been calls for decades for less salt in the diet in order to improve public health. The WHO recommends a daily salt intake of less than 5 g/day 54 , but in 2017 the global average salt intake remained high and is still estimated at around 15 g/day 55 . A shorter salting time that does not affect aromatic profiles might offer an ideal solution for manufacturers to produce low-salt cheeses with a shorter production time. Similar studies on reducing the salt content in cheese showed that Nareduced cheeses tasted bitter and were therefore organoleptically unsatisfactory 56 . Further sensory analyses involving less salty cheeses are necessary to confirm their organoleptic quality and conclude as to the positive effects of a shorter salting time.

(

  p<0.001) to reach 7.5 log 10 CFU/mL in the mature milk with an average doubling time (DT) estimated at 3.1 h, while L. plantarum grew very slowly (DT = 19.7 h). Following molding, the bacteria in milk were concentrated by approximately ten-fold in curd, so that L. lactis, L. plantarum and P. freudenreichii levels in molded cheese (C m ) reached at 8.6, 6.4 and 7.0 log 10 CFU/g, respectively. The L. lactis population reached the highest level before ripening (9.2 log 10 CFU/g) and then fell slightly to reach 8.5 log 10 CFU/g at the end of ripening. L. plantarum grew rapidly during molding (DT = 5.4 h); the highest population was reached at four weeks of ripening (8.4 log 10 CFU/g) and remained stable thereafter. The growth of P. freudenreichii occurred during molding (DT = 5.4 h) and the first four weeks of ripening. The final population of P. freudenreichii reached 8.5 log 10 CFU/g.

Figure 1 :

 1 Figure 1: Schematic representation of the standard cheese making itinerary and three variant itineraries, and kinetic sampling throughout production.

Figure 2

 2 Figure 2 Kinetic evolutions during cheese manufacture and ripening under the standard itinerary regarding (A) viable counts of the three bacterial species, (B) dry matter and (C) pH. M LAB : milk containing lactic acid bacteria (L. lactis and L. plantarum); M LAB-P : milk containing lactic acid bacteria and P. freudenreichii; C m : molded cheese; C dm : demolded cheese; C 0w : cheese ripened for 0 weeks; C 4w : cheese ripened for 4 weeks; C 7w : cheese ripened for 7 weeks. Values are the means of four biological replicates of cheese production.

Figure 3 :

 3 Figure 3: Evolution of (A) the non-casein nitrogen fraction (NPN) and (B) the non-protein nitrogen fraction (NPN) throughout cheese production under the standard and variant itineraries. Std in black, Stir15min in yellow, Salt7h in green, Rip16°C in red. Values are the means of four and two biological replicates of cheese production for Std and others respectively.

Figure 4 :

 4 Figure 4: Accumulation of pyruvic (A), succinic (B), acetic (C), propanoic (D), butanoic (E) and phenyllactic acids (F), 2-methylbutanol (G), 2-methylbutanoic (H) and hexanoic acids (I) throughout cheese production. ND: not detected. Std in black, Stir15min in yellow, Salt7h in green, Rip16°C in red. Values are the means of four and two biological replicates of cheese production for Std and others respectively.

Figure 5 :

 5 Figure 5: Principal component analyses of microbial, physicochemical and biochemical components in cheeses throughout production under the standard and variant itineraries. A: variables, B: observations. Std in black, Stir15min in yellow, Salt7h in green, Rip16°C in red. M: molded cheese; D: demolded cheese; C 0w : cheese ripened for 0 weeks; C 4w : cheese ripened for 4 weeks; C 7w : cheese ripened for 7 weeks. Values are the means of four and two biological replicates of cheese production for Std and others respectively. Ellipses were arbitrarily drawn to delimit group of samples to facilitate reading.

3 c

 3 Odor descriptions from thegoodscentscompany.com and foodb. ca (2020).

4 5 6 Figures 1 Figure 1

 611 Figures 1

1

 1 Figure 1 1

  Figure 3 1 2

Table 1 : Samples collected throughout cheese production and their corresponding analysis.

 1

	Collection	Sampling stage	Sample	Microbial	Physicochemical			Biochemical
	time			label	analysis	analysis				analysis
					Enumeration pH	DM (1) Ca 2+ Fat	NaCl Protein	Sugars and	Volatile
											(TP, NCN, NPN) (2)	organic	compounds
												acids
	0.5 h		Milk inoculated with LAB	M LAB	x	x	x	x	x		x	x	x
	18 h		Milk inoculated with P.	M LAB+P	x	x	x	x	x		x	x	x
			freudenreichii
	19.5 h		Cheese at molding	C m	x	x	x	x	x		x	x	x
	21.5 h		Cheese molded for 2h	C m+2h		x	x	x
	23.5 h		Cheese molded for 4h	C m+4h		x	x	x
	2 nd day		Cheese at demolding	C dm	x	x	x	x	x		x	x	x
	3 rd day		Cheese before ripening	C 0w	x	x	x	x	x	x	x	x	x
	4 weeks	Cheese ripened for 4 weeks	C 4w	x	x	x	x	x	x	x	x	x
	7 weeks	Cheese ripened for 7 weeks	C 7w	x	x	x	x	x	x	x	x	x
	(1)	DM: Dry matter
	(2)	TP: Total protein; NCN: Non-casein nitrogen; NPN: Non-protein nitrogen.

Table 2 . Evolution of the populations of the three bacterial species during the production of model semi-hard cheeses manufactured according to one standard and three variant itineraries a . These values are the means of two biological replicates. The last four columns present (I X PS) and (I X RS) interactions, as well as the global mean of the bacterial population under the itinerary analyzed.

 2

		Cheese production
	Bacteria	Itinerary

a A description of the itineraries is available in Figure

1

c I, itinerary; PS, all production stages. d global mean is the estimated marginal mean e RS, ripening stage. f NS, not significant. g *, p ≤ 0.1; **, p ≤ 0.05; ***, p ≤ 0.001. h -, not relevant

Table 3 . Physicochemical evolution of cheeses during the production of model semi-hard cheeses manufactured according to one standard and three variant itineraries a . Reported values are the means of two biological replicates. The last four columns present (I X PS) and (I X RS) interactions, as well as the global means of each composition under the analyzed itinerary.

 3

	Parameters	Itinerary	Cheese production

b I, itinerary; PS, all production stages. c Global mean is the estimated marginal mean d RS, ripening stage. e NS, not significant. f *, p ≤ 0.1; **, p ≤ 0.05; ***, p ≤ 0.001. g -, not analyzed h MFFB, moisture content in the fat-free basis of cheese.

Table 5 , Concentrations of sugars and organic acids during the production of model semi-hard cheeses manufactured according to one standard and three 1 variant itineraries a . 2

 5

		Cheese production
	Compounds,
	g/kg	itinerary

a Values are the means of two biological replicates. b I, itinerary; PS, all production stages. c Global mean is the estimated marginal mean d RS, ripening stage. e NS, not significant. f *, p ≤ 0.1; **, p ≤ 0.05; ***, p ≤ 0.001. g -, not analyzed h ND, not detected

Table 6 : Volatile compounds detected by GC-MS in cheese during the production of model semi-hard cheeses 1

 6

		Chemical class	CAS	Volatile compounds a	Identification b	LRI	m/z Odor descriptor c
			Number	(abbreviated code or trivial name)
		Ketones	67-64-1	Propan-2-one (acetone)	LRI, DB, S	804	58	Solvent; ethereal; apple; pear
			78-93-3	Butan-2-one	LRI, DB, S	892	72	Acetone-like ethereal fruity camphor
			431-03-8 Butane-2,3-dione (diacetyl)	LRI, DB, S	979	86	Strong butter sweet creamy pungent caramel
			513-86-0 3-Hydroxybutan-2-one (acetoin)	LRI, DB, S	1284 88	Sweet buttery creamy dairy milky fatty
			116-09-6 1-hydroxypropan-2-one (acetol)	LRI, DB	1298 43	Pungent sweet caramellic ethereal
		Alcohols	71-23-8	Propan-1-ol	LRI, DB, S	1054 59	Alcoholic fermented fusel musty
			137-32-6 2-methylbutan-1-ol	LRI, DB, S	1231 70	Roasted wine onion fruity fusel alcoholic
								whiskey
		Sulfur compounds 624-92-0 (Methyldisulfanyl)methane (DMDS: dimethyl	LRI, DB, S	1068 94	Sulfurous vegetable cabbage onion
				disulfide)
		Acids	64-19-7	Acetic acid (C2)	LRI, DB, S	1448 60	Sharp pungent sour vinegar
			79-09-4	Propanoic acid (C3)	LRI, DB, S	1533 74	Pungent acidic cheesy vinegar
			107-92-6 Butanoic acid (C4)	LRI, DB, S	1631 60	Sharp acetic cheese butter fruit
			116-53-0 2-Methylbutanoic acid	LRI, DB, S	1674 87	Pungent acid roquefort cheese
			142-62-1 Hexanoic acid (C6)	LRI, DB, S	1829 60	Sour fatty sweat cheese
			124-07-2 Octanoic acid (C8)	LRI, DB, S	1933 60	Fatty waxy rancid oily vegetable cheesy
	2	a IUPAC name

b Identification based on: LRI, calculated linear retention index; DB, mass spectral data from Library NIST 2008, and S, Standard.

Acknowledgements

We are grateful to the INRAE STLO Dairy Platform (https://www6.rennes.inrae.fr/plateforme_lait_eng/) for providing help and support (doi: 10.15454/1.557240525786479E12), to the CNIEL for funding and fruitful discussions on the interpretation of the results (TANGO Project) and to S. Le Feunteun and S. Bouhallab for mind mapping interpretations for the discussion part.

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J Hofmann
email: julien.hofmann@grenoble-inp.fr

C Thiébaut

M Riondet

P Lhuissier

S Gaudion

M Fivel

Influence of microstructure on mass loss caused by acoustic and hydrodynamic cavitation

The proposed study investigates the damage mechanisms of martensitic stainless steel X3CrNiMo13-4 exposed to cavitation using two complementary experimental apparatus: ultrasonic horn (MUCEF) and hydrodynamic tunnel (PREVERO). Cavitation testing has been carried out on two different metallurgical states: QT780 and QT900 corresponding to coarse and fine microstructure respectively. Acoustic cavitation erosion tests have been performed on the MUCEF equipment inspired from the ASTM G32 standards but specially designed to be installed inside X-Ray tomographs. The ultrasonic horn operates at 20 kHz and the tested specimen is located at 500 µm from the horn tip. Hydrodynamic cavitation erosion tests were conducted with classic experimental conditions of PREVERO device: a cavitation number of 0.87 corresponding to a flow velocity of 90 m.s -1 and an upstream pressure of 40 bars. For acoustic cavitation, mass loss has been identified as dependent of the microstructure while for hydrodynamic cavitation the mass loss is identical whatever the microstructure size.

Introduction

Cavitation, i.e. formation of vapour bubbles in a liquid after a local pressure drop, is a very common phenomenon in hydraulic devices such as water turbines, propellers or pumps [START_REF] Franc | Fundamentals of cavitation[END_REF]. These cavities develop in high-velocity regions which are usually associated to low pressures according to Bernoulli equation. Bubbles or cavitation clouds later collapse where the pressure increases. When this collapse occurs at the vicinity of a solid wall, micro-jets and/or shock waves are generated and directed toward the solid surface. Each bubble collapse is local and violent since the resulting stress can reach few gigapascals at a strain rate as high as 10 6 s -1 on a surface of a few squared micrometres [START_REF] Franc | Fundamentals of cavitation[END_REF]. If the load generated by the collapse is greater than the yield stress, a pit is printed on the surface. Repetition of bubbles collapse increases the work-hardening of the material. This leads to damage and ultimately to mass loss [START_REF] Krella | The resistance of S235JR steel to cavitation erosion[END_REF][START_REF] Caupin | Cavitation in water: a review[END_REF]. The latter depends on the type of apparatus used for testing the material with the experimental conditions as well as the type of material tested [START_REF] Kim | Advanced experimental and numerical techniques for cavitation erosion prediction[END_REF]. The aim of the present work is to compare the effect of the microstructure of the X3CrNiMo13-4 martensitic stainless steel on the damage process for acoustic and hydrodynamic cavitation. The study proposes to compare the mass loss for the two types of microstructure for acoustic and hydrodynamic cavitation. The differences in the material response is discussed based on a microstructural analysis.

Material and methods

Material

In the present study, the low-carbon martensitic stainless steel X3CrNiMo13-4 was used as the tested material. The two metallurgical states QT780 and QT900, corresponding respectively to coarse and fine microstructure, have been studied. Both grades were austenitised at 1100 °C and quenched. The differences in the two grades QT780 and QT900 lays in the tempered sequence, 600 °C for 2*4 h and 540 °C for 4 h respectively. The resulting materials are composed of a 𝛼 ′ martensitic matrix with lamellar reversed austenite, 𝛾 𝑟𝑒𝑣 and residual delta ferrite, 𝛿𝐹𝑒. Presence and proportion of these phases were confirmed using XRD analysis. Due to the different tempering conditions, the percentage of reversed austenite in each grade is different: respectively 11.9 vol.% and 2.7 vol.% for QT780 and QT900 grades. The reversed austenite can transform to martensite when submitted to plastic deformation: this is known as the transformation-induced plasticity (TRIP) effect. This transformation is associated to a volumetric expansion, which produces compressive forces that delay crack initiation and propagation. The low-carbon X3CrNiMo13-4 is hence widely used for water turbine manufacturing due to its high mechanical strength and high corrosion resistance. Figure 1(a) and Figure 1(b) show the orientations maps for the grades QT780 and QT900 respectively at the initial state with the martensitic matrix 𝛼 ′ and reversed austenite lamellas 𝛾 𝑟𝑒𝑣 . These observations using EBSD technique reveal that the QT780 grade is coarser than the QT900 grade.

Cavitation erosion testing

For testing material, two experimental devices were used for generating acoustic and hydrodynamic cavitation. For MUCEF device, a sonotrode vibrating at 20 kHz with a peak-to-peak amplitude of 50 µm was used for generating acoustic cavitation. The distance of 500 µm between the specimen and tip was measured using x100 optical camera (Dino-Lite AM4815ZTL, Taiwan) and adjusted using a micrometric motorized stage (OWIS LTM120, Germany). This in-house setup, using indirect method and inspired from the ASTM G32 standard, is depicted in Figure 2(a). Hydrodynamic cavitation testing was performed using the famous 40 bars flow tunnel called PREVERO located at LEGI laboratory. Water was accelerated using a centrifugal pump rotated by an 80 kW electric engine. Tap water was pressurized using nitrogen gas on the free surface of a water tank. As shown in Figure 2(b) on a cross section view, specimen were located at 2.5 mm from the nozzle wall where the velocity is approximately equal to 90 m.s -1 leading to the formation of cavitation bubble clouds. Hydrodynamic cavitation erosion tests were conducted at a constant cavitation number σ = 0.870 ± 0.001.

Results and discussions

Figure 3 shows the mass loss as a function of the exposure time to cavitation for both grades QT780 (coarse microstructure) and QT900 (fine microstructure) for acoustic and hydrodynamic cavitation. For each measurement point, the specimen were dried carefully and then weighted using an analytical balance. Incubation time, defined as the exposure time required to observe first mass loss, is greater for hydrodynamic cavitation than acoustic cavitation. The incubation times are respectively 1 h and 2 h for the grades QT780 and QT900 for acoustic cavitation while it is approximately 5 h for both grades for hydrodynamic cavitation. For hydrodynamic cavitation, the type of microstructure has no effect on the incubation time and both mass loss curves are identical. According to MUCEF experiments, the mass loss measured for the coarser microstructure (QT780) is more important than for the finer (QT900). This is consistent with the ranking of the ultimate strengths of these two grades. It is concluded that the mass loss curves depend on the microstructure only for acoustic cavitation whereas hydrodynamic cavitation could not distinguish the behaviour of the two grades. This difference of material response according to the type of cavitation could be attributed to the size of the bubble. It has been previously shown that hydrodynamic cavitation erosion creates large pits (20-250 µm) at a small pitting rate and, conversely, acoustic cavitation erosion creates small pits (5-30 µm) at a high pitting rate (5). Bubbles size generated by acoustic cavitation is thus closer to the characteristics size of the microstructure while the collapse of hydrodynamic cavitation bubbles does not differentiate the grains.

Conclusion

Influence of the microstructure on mass loss was investigated on the X3CrNiMo13-4 martensitic stainless steel exposed to acoustic and hydrodynamic cavitation. By analysing the microstructure, we might draw the conclusion that the microstructure size has an influence on the mass loss when exposed to acoustic cavitation and not for hydrodynamic cavitation. In-situ synchrotron X-ray tomography

Figure 1 :

 1 Figure 1: Electron backscatter diffraction (EBSD) orientation maps for grade (a) QT780 and (b) QT900 of X3CrNiMo13-4 martensitic stainless steel. (c) Inverse pole figures.

Figure 2 :

 2 Figure 2: (a) MUCEF device used for generating acoustic cavitation. Specimen is located at 0.5 mm from the sonotrode (indirect method). (b) Cross section view of PREVERO device used for generating hydrodynamic cavitation. A constant cavitation number σ of 0.789 has been used for experiments.

Figure 3 :

 3 Figure 3: Mass loss as a function of exposure time in acoustic (MUCEF) and hydrodynamic cavitation (PREVERO) for the QT780 and QT900 grades.

analysis will be further conducted for observing cracks propagation and for contributing to the construction of a damage model.