# Effect of operating conditions on food waste fermentation to enhance VFA production and propionic acid selectivity

Mathilde Bourgeois, Sabine Taussac, Nicolas Bernet, Renaud Escudié, Eric Trably

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Mathilde Bourgeois, Sabine Taussac, Nicolas Bernet, Renaud Escudié, Eric Trably. Effect of operating conditions on food waste fermentation to enhance VFA production and propionic acid selectivity. Environmental Technology and Innovation, 2025, 39, pp.104324. 10.1016/j.eti.2025.104324. hal-05254027

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# Effect of operating conditions on food waste fermentation to enhance VFA production and propionic acid selectivity

Mathilde Bourgeois, Sabine Taussac, Nicolas Bernet, Renaud Escudié, Eric Trably*

INRAE, Univ. Montpellier, LBE, 102 Avenue des étangs, Narbonne F-11100, France

## ARTICLE INFO

**Keywords:**

Fermentation

Food waste

VFA

Propionic acid

Microbial communities

## ABSTRACT

Food waste fermentation offers a promising approach for the production of volatile fatty acids (VFAs). However, controlling the VFA profile, particularly enriching propionic acid, remains a challenge due to the complexity of microbial communities involved. This study investigates the influence of key operating parameters—initial pH, temperature, and substrate concentration—on VFA production and microbial community dynamics. Batch fermentation experiments using reconstituted food waste were conducted under varying conditions. The highest propionic acid selectivity was achieved at an initial pH of 9, a temperature of 35 °C, and a substrate concentration of 7.8 g VS/L, with statistically significant improvement over other conditions. Microbial community analysis based on 16S rDNA sequencing revealed distinct profiles shaped by the operational settings. Notably, increased relative abundances of *Enterobacteriaceae*, *Lachnospiraceae*, and *Exiguobacterium* spp. were associated with higher propionic acid production. These results highlight the strong interplay between fermentation conditions, microbial ecology, and metabolite profiles, providing insights for optimising food waste valorisation towards selective VFA production.

## 1. Introduction

Food waste (FW) is increasingly recognised as a valuable resource for the recovery of high-value products due to its high biodegradability and abundance worldwide. Rich in organic matter – including carbohydrates, proteins and lipids – FW provides all essential nutrients for microbial growth. These characteristics make FW an ideal substrate for biological processes such as anaerobic digestion (AD) or dark fermentation (DF) (Wang et al., 2022).

Dark fermentation was initially explored for hydrogen production, but it is now acknowledged for its broader potential to generate bio-based molecules – primarily volatile fatty acids (VFAs) – through the activity of mixed microbial communities (Castro-Fernandez et al., 2024). Compared to hydrogen, whose yield is limited by the Thauer limit (2-3 mol H₂ per mol glucose) (Giduthuri and Ahring, 2023), VFAs can be produced at higher yields. Moreover, their wide range of industrial applications – including food additives, chemical intermediates, pharmaceuticals or flavouring agents – makes them particularly attractive.

Bio-based VFAs have also been identified as a promising carbon source for biological nutrient removal from wastewaters, as well as for bioplastics or biodiesel production (Atasoy et al., 2018; Moronkola et al., 2025). In such applications, VFAs are typically used as a mixture resulting from the fermentation of organic waste. However, the specific composition of this mixture significantly affects process performance. For instance, acetic and propionic acids are especially effective for biological nutrient removal, improving nitrate

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: eric.trably@inrae.fr (E. Trably).</footnote>
reduction (C. Li et al., 2015). In polyhydroxyalkanoates (PHA) production, the initial VFA composition not only influences the overall yield, but also the type of monomers produced – such as 3-hydroxyvalerate or 3-hydroxybutyrate (Colombo et al., 2016; Montiel-Jarillo et al., 2021).

In addition, VFAs can serve as a carbon source for further microbial conversion into platform molecules. For example, *Cupriavidus necator* has been shown to efficiently produce acetoin from renewable sources, with particularly high yields when propionic acid is used (Härrer et al., 2021). This highlights again the critical importance of VFA mix composition for downstream application and emphasises the promise of propionic acid production through dark fermentation.

Despite this growing interest, selectively producing specific VFAs from organic waste remains a significant challenge due to the complexity of the substrate and the diversity of microbial communities involved (Atasoy et al., 2018; Duong and Nga, 2024). While pure cultures – such as *Acetobacter* and *Gluconobacter* for acetic acid, *Propionibacterium* and *Acidipropionibacterium* for propionic acid and *Clostridium tyrobutyricum* for butyric acid - can be used for targeted organic acid production, these systems require sterile conditions and simple substrates, making them costly (Agnihotri et al., 2022; Bhatia and Yang, 2017; Wainaina et al., 2019).

In mixed culture systems like AD, complex substrates are first hydrolysed into monomers (monosaccharides, amino acids and glycerol) by hydrolytic bacteria, which are then converted into various fermentation products during the acidogenesis step. Acidogenic bacteria - primarily from the *Firmicutes*, *Bacteroidetes*, *Proteobacteria* and *Chloroflexi* phyla - play a central role in this conversion (Duong and Nga, 2024; Yuan et al., 2019). These microbes convert monomers into pyruvate and then into different organic acids through various metabolic pathways: formic acid via hydrogenase, lactic acid via lactate dehydrogenase, acetic acid via aldehyde dehydrogenase and acetate oxidase, propionic acid via either Wood-Werkman pathway (involving oxaloacetate transcarboxylase) or acrylate pathway (from lactate with acrylyl-CoA reductase and propionyl-CoA transferase), butyric acid via phosphotransbutyrylase and butyrate kinase, and valeric acid through other intermediates (Bhatia and Yang, 2017; Harirchi et al., 2022a).

Although key microbial pathways have been identified, the factors governing their activity in mixed cultures remain insufficiently understood (Yuan et al., 2019). Operational parameters such as pH, hydraulic retention time, temperature, and substrate composition and concentrations are known to influence VFA production and profiles by modulating metabolic pathways. However, these effects are highly dependent on the type of substrate and inoculum used (Cui et al., 2024; Duong and Nga, 2024).

To promote the sustainable valorisation of food waste through the production of high-value compounds, this study aims to develop a fermentation process that converts food waste into VFAs, with a particular focus on propionic acid. Batch experiments will be conducted to assess the impact of key operational parameters – initial pH, temperature and food waste concentration – on both VFA yield and composition, as well as microbial community dynamics. This approach will help to clarify how these parameters influence VFA production and identify the microbial families favoured under different conditions. The findings are expected to offer valuable insights for optimising food waste fermentation toward targeted propionic acid production.

## 2. Materials and methods

### 2.1. Inoculum and substrate

The substrate used for batch tests consisted of freshly reconstituted food waste from frozen products to simulate typical food waste from natural feedstocks in France, as previously described (Dauptain, 2021; Noguer, 2022). The mixture included (by fresh mass): 15% meat, 10% yoghurt, 15% red fruits, 10% breaded fish, 20% French fries, 15% vegetables (carrots, broccoli, mushrooms, green beans, potatoes) and 15% bread. This composition closely resembles that of European food waste (Albizzati et al., 2021). The macromolecular composition was defined based on the packaging information and was of 62% VS carbohydrates, 22% VS proteins and 16% VS lipids (Magdalena et al., 2023). Total Solids (TS) and Volatile Solids (VS) were measured using standard methods (Simmons, George, 1993), and the ratio was VS/TS=0.97. The anaerobically biodegradable organic matter was estimated based on a Flash BMP (Biochemical Methane Potential) measurement (Perémé et al., 2021) of 451 ± 6 mL CH₄ / g VS. Considering that the complete conversion of 1 g COD leads to 350 NmL CH₄, the resulting maximum anaerobically biodegradable COD was estimated at 1.28 ± 0.2 g COD/ g VS (Noguer et al., 2022). The food waste ingredients were mixed and diluted with water, then ground using a kitchen blender. The particle size was determined using a granulometer and was 215.6 ± 273.2 µm (Roslan et al., 2023). To fulfil the European legislation (Official Journal of the European Union, L 54, 26 February 2011), the mixture underwent a hygienisation step at 70°C for 1 h.

As microbial inoculum, activated sludge originating from an urban wastewater treatment plant was collected after the secondary tank. This inoculum was chosen for its capacity to degrade complex organic matter and to contain fermentative gut bacteria. This sludge was freeze-dried and stored at -80°C according to the proper storage method determined by Dauptain et al. (2021). Alternatively, it was used directly in its liquid form after measuring TS and VS. Prior to inoculation, the sludge was heat-treated at 90°C for 15 min to avoid methanogenic activity.

### 2.2. Experimental design and set-up

The screening of the three following operating parameters for optimising fermentation for propionic acid production was investigated in three series of batches: initial pH, temperature and food waste concentration. Each series compared four different conditions, with four replicates per condition. A control condition was established at the beginning of the study and repeated for each experimental series. Fermentation conditions are detailed in Table 1.

Batch tests were carried out in 600 mL flasks with a 200 mL working volume. The flasks were sealed with a septum to allow sampling via a needle and a steel ring. Bicarbonate buffer (NaHCO₃) at 0.16 M was used for all the experiments except for the substrate
concentration screening, where buffer concentration was adjusted to the substrate concentration, without exceeding 0.25 M to prevent microbial inhibition by Na⁺ ions. Distilled water was used to fill out the working volume of the flasks to 200 mL. The ratio of substrate to inoculum on a VS basis was kept constant throughout the experiments at 8.67, corresponding to a concentration of 7.8 g VS/L for the substrate and 0.9 g VS/L for the inoculum. Before starting the experiments, the pH was adjusted to the desired value using an 8 M NaOH solution. Flasks were flushed with N₂ to remove O₂. The flasks were then incubated at the appropriate temperature using either a water bath or a temperature-controlled room (24°C and 35°C). After 8 days of fermentation, samples were collected from the liquid phase for further metabolite and microbial analysis, centrifuged at 13,500 rpm for 15 min (MC-12 Benchmark 16100 g or MiniSpin, Eppendorf, 12100 g). The two phases were separately stored at -20°C for further use.

## 2.3. Analytical methods

During fermentation, the flask headspace was analysed using a Micro Gas Chromatograph (MicroGC) directly connected to the fermentation bottles with automatic sampling and analysis. A Gas Chromatograph (GC) was also used with manual injections as an alternative method. Both analyses were used to quantify H₂, N₂, O₂, CH₄, and CO₂. The volume of biogas produced was calculated using the pressure difference. Metabolite concentrations in the liquid phase were measured using two analytical devices: for volatile fatty acid (VFA) measurement, a gas chromatograph (Clarus 580 from Perkin Elmer) was used, equipped with an autosampler. The column was an Alltech FFAP EC 1000, 15 m column, coupled with a flame ionisation detector (FID) at 280°C. Dinitrogen, flowing at 6 mL/min, was used as a gas carrier. The quantification of VFAs ranged between 100 mg/L and 1000 mg/L (±2%). Prior to analysis, samples were filtered through a 0.2 µm filter and diluted twice using an internal standard. To complement the GC analysis, HPLC (High-Performance Liquid Chromatography) was also used to quantify volatile fatty acids (acetate, propionate, isobutyrate, butyrate, isovalerate, valerate and caproate), carboxylic acids and alcohols (glycerol, ethanol, 1,3-propanediol, formic acid, fumaric acid, succinic acid, lactic acid, pyruvic acid), for concentrations varying between 0.1 and 10 g/L. The separation was performed on an Aminex HPX-87H 300 × 7.8 mm (Biorad, Hercules, CA, USA) column, after going through a protective pre-column (Microguard cation H refill cartridges, Biorad, Hercules, CA, USA). The detection was done with a refractometer. Samples were filtered through a 0.2 µm filter and adjusted to a pH between 1 and 4 using a sulfuric acid solution before analysis.

## 2.4. Calculation and statistical analysis

Final metabolite concentrations were measured in g/L and converted to g COD/L using the theoretical COD calculation based on the number of electrons involved in the oxidation reactions. This conversion was necessary to calculate the COD mass balance and reaction advancement (expressed as the ratio of the COD measured at a given time to the biodegradable COD introduced - g COD/g COD biodegradable in) and for determining the selectivity of specific metabolites among the total metabolite produced, especially propionic acid C3 (g COD C3/g COD metabolite).

Measurements were performed for each replicate, and the data presented are the average values ± standard deviations. Differences between conditions (groups of quadruplicates) were assessed using the non-parametric Kruskal-Wallis test followed by the Wilcox test, using the Holm correction. Two groups were considered significantly different if they had a p-value< 0.05. Data were represented in boxplots, with statistical grouping indicated (a, b, c, d). This analysis was conducted on final propionic acid selectivity, comparing all controls from different batches (pH 9, T = 35°C, [FW] =7.8 g VS/L) against other parameters.

## 2.5. Microbial analysis

The final samples of microbial communities were analysed and compared with each other when the production of metabolites during fermentation revealed results to could be exploited. For this reason, one sample per condition was sequenced, with the exception of the pH 8 condition, while two samples were sequenced for the FW 100 g VS/L condition.

Samples of 2 mL were collected and centrifuged at 13,500 rpm for 15 min. The resulting pellets were used for DNA extraction and

<table><caption>**Table 1**

Operating parameters of the batch tests.</caption><thead><tr><th>Experimental batch</th><th>Condition</th><th>Initial pH</th><th>Temperature (°C)</th><th>Food waste concentration (g VS/L)</th></tr></thead><tbody><tr><td rowspan="4">pH screening</td><td>pH 7</td><td>7</td><td>35</td><td>7.8</td></tr><tr><td>pH 8</td><td>8</td><td>35</td><td>7.8</td></tr><tr><td>pH 9</td><td>9</td><td>35</td><td>7.8</td></tr><tr><td>pH 10</td><td>10</td><td>35</td><td>7.8</td></tr><tr><td rowspan="4">Temperature screening</td><td>T24</td><td>9</td><td>24</td><td>7.8</td></tr><tr><td>T30</td><td>9</td><td>30</td><td>7.8</td></tr><tr><td>T35</td><td>9</td><td>35</td><td>7.8</td></tr><tr><td>T45</td><td>9</td><td>45</td><td>7.8</td></tr><tr><td rowspan="4">Food waste concentration screening</td><td>FW7.8</td><td>9</td><td>35</td><td>7.8</td></tr><tr><td>FW25</td><td>9</td><td>35</td><td>25</td></tr><tr><td>FW50</td><td>9</td><td>35</td><td>50</td></tr><tr><td>FW100</td><td>9</td><td>35</td><td>100</td></tr></tbody></table>
purification with FastDNA™ SPIN Kit for Soil (MP, France). DNA amount and purity in extracts were confirmed by spectrophotometry (Infinite NanoQuant M200, Tecan, Austria). Extracted DNA was stored at -20°C until further use. The V3-4 region of the 16S rRNA gene was amplified as presented by Carmona-Martínez et al. (2015). The resulting PCR products were purified and loaded onto the

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Metabolites concentration after the 8-day fermentation batch for A) pH screening experiments, B) temperature screening experiments, and C) FW concentration screening experiments.</figcaption></figure>
Illumina MiSeq cartridge according to the manufacturer's instructions for sequencing paired 250 bp reads. Sequencing-related work was done at the GeT PlaGe sequencing centre of the Genotoul Life Science network in Toulouse, France (get.genotoul.fr). The demultiplexing, reads cleaning, assembly and quality checking were done using Mothur v. 1.48.0 and taxonomic and functional assignment using SILVA 132. Sequences were submitted to Bioproject n°PRJNA1152534.

The microbial community data were analysed using R packages *phyloseq* and *microeco* to represent the relative abundances of families and order the samples using the redundancy analysis (RDA) based on environmental data (experimental conditions and metabolite production) and relative abundance at the family level in the different samples analysed. When taxonomic assignment at the family level was not meaningful, genus-level assignments were examined to try to determine the family behind the mentioned cluster by referring to the genus names on the List of Prokaryotic names with Standing in Nomenclature (LPSN).

## 3. Results and discussion

### 3.1. Influence of initial pH on metabolite production

Initial pH was adjusted at four different values: 7; 8; 9, and 10. After 8 days of fermentation, metabolites were measured in the liquid phase (Fig. 1A). The total metabolite accumulation at the end of the fermentation was similar for all conditions, ranging from 5.67 ± 0.58–5.86 ± 0.46 g/L for pH 7–9, with a slight increase at pH 10 (6.43 ± 0.68 g/L). The reaction advancement based on COD exceeded 75% in all conditions, with the maximum at pH 8, reaching 90.9 ± 16.5% g COD/g COD introduced (Table 2). These results suggest an efficient fermentation process, with sufficient substrate conversion into metabolites - mostly VFA, yielding 0.67 ± 0.06–0.80 ± 0.10 g VFA/g VS (Table 2). The main metabolites produced were acetic (45–83 % on mass basis), propionic (5–29 %) and butyric acids (4–28 %) for all conditions, with smaller amounts of isovaleric acid (around 2 %), valeric acid (0–2 %), and ethanol (3–8 %). However, the metabolite composition varied according to the initial pH: at pH 7, acetic and butyric acid were the main metabolites, with lower amounts of propionic acid (0.65 ± 0.27 g/L) and ethanol. At pH 8, the proportions were similar but with a higher concentration of propionic acid (0.82 ± 0.18 g/L) and less butyric acid. The highest propionic acid concentration was observed at pH 9 (1.71 ± 0.13 g/L), making it the second most abundant metabolite. At pH 10, propionic acid dropped to 0.37 ± 0.13 g/L, while acetic acid dominated (5.36 ± 0.91 g/L), with a sharp decrease in butyric acid and ethanol. As the main parameter, the selectivity of propionic acid is shown in Fig. 2 as a proportion of propionic acid relative to the total metabolite concentration on a COD basis. Differences between conditions were statistically evaluated using the non-parametric test of Kruskal-Wallis, followed by the Wilcoxon tests. These tests were selected based on the non-normality and/or non-homoscedasticity of the data. The highest selectivity for propionic acid was obtained at pH 9, accounting for 31.9 ± 5.4 % of the total metabolites. This value was significantly higher than those at other pH levels, where selectivity ranged between 7.7 ± 2.9 % and 14.5 ± 5.3 %. These results suggest that the initial pH significantly influences the resulting metabolite composition.

Alkaline fermentation (pH ≥7) was found to be a suitable condition for substrate conversion and VFA production, with satisfactory total reaction advancement in all conditions. This aligns with existing literature, where alkaline conditions are known to favour VFA production rather than hydrogen (Chen et al., 2013; Garcia-Aguirre et al., 2017; Zhang et al., 2020). The alkaline pH promotes substrate solubilisation and hydrolysis, particularly for proteins, which make up 22% of food waste VS. This enhances their availability for microbial activity and subsequently, their conversion into VFA (Dahiya et al., 2015; Ma et al., 2019; Maspolim et al., 2015; Yuan et al., 2006, 2019), as they cannot be converted into H₂ (Perat et al., 2024). This supports the higher metabolite production observed at pH 10. While previous studies on food waste fermentation reported strong metabolic shifts with initial pH adjustment (Ali et al., 2021; L. Feng et al., 2009; Jiang et al., 2013), this study quantified this effect on specific VFA production. Dahiya et al. (2015) observed similar VFA accumulation at pH levels 9 and 10 as in the present study (6.3 g VFA/L – 5.17 g VFA/L), as well as propionic acid concentration (1.4 g/L at pH 9) from food waste fermentation, but they did not have a particular interest for this metabolite. Here, pH

<caption>**Table 2**</caption>

Yields of main metabolites at the end of 8-day batch fermentation. RA corresponds to Reaction Advancement in % of COD of total metabolites produced divided by the biodegradable COD introduced at the beginning. Values are expressed as the mean of 4 replicates ± standard deviation. The green lines correspond to the control conditions.

<table><thead><tr><th rowspan="2">Experiments</th><th colspan="7">Yield (g/g VS)</th></tr><tr><th>Conditions</th><th>Lactate</th><th>Acetate</th><th>Propionate</th><th>Butyrate</th><th>Total VFA</th><th>RA</th><th>Final pH</th></tr></thead><tbody><tr><td><strong>pH screening</strong><br>(T = 35°C; [FW] = 7.8 g VS/L)</td><td>pH7</td><td>0</td><td>0.34 ± 0.04</td><td>0.08 ± 0.04</td><td>0.20 ± 0.03</td><td>0.67 ± 0.06</td><td>86.0 ± 7.8</td><td>6.65 ± 0.10</td></tr><tr><td></td><td>pH8</td><td>0</td><td>0.34 ± 0.04</td><td>0.10 ± 0.02</td><td>0.17 ± 0.02</td><td>0.70 ± 0.05</td><td>90.9 ± 16.5</td><td>6.91 ± 0.08</td></tr><tr><td></td><td>pH9</td><td>0</td><td>0.35 ± 0.04</td><td><strong>0.22 ± 0.02</strong></td><td>0.09 ± 0.02</td><td>0.70 ± 0.03</td><td>83.0 ± 9.2</td><td>6.99 ± 0.04</td></tr><tr><td></td><td>pH10</td><td>0</td><td>0.69 ± 0.12</td><td>0.05 ± 0.02</td><td>0.04 ± 0.01</td><td>0.80 ± 0.10</td><td>76.7 ± 5.5</td><td>7.45 ± 0.05</td></tr><tr><td><strong>T screening</strong><br>(pH 9; [FW] = 7.8 g VS/L)</td><td>T24</td><td>0</td><td>0.56 ± 0.01</td><td>0.05 ± 0.01</td><td>0.05 ± 0.01</td><td>0.69 ± 0.02</td><td>71.7 ± 1.3</td><td>6.93 ± 0.01</td></tr><tr><td></td><td>T30</td><td>0</td><td>0.49 ± 0.08</td><td>0.06 ± 0.01</td><td>0.08 ± 0.01</td><td>0.65 ± 0.07</td><td>66.0 ± 6.0</td><td>7.16 ± 0.17</td></tr><tr><td></td><td>T35</td><td>0</td><td>0.58 ± 0.04</td><td><strong>0.09 ± 0.02</strong></td><td>0.08 ± 0.02</td><td>0.78 ± 0.04</td><td>78.5 ± 5.9</td><td>7.08 ± 0.03</td></tr><tr><td></td><td>T45</td><td>0</td><td>0.63 ± 0.09</td><td>0.02 ± 0.01</td><td>0.06 ± 0.01</td><td>0.73 ± 0.09</td><td>74.6 ± 3.6</td><td>7.12 ± 0.04</td></tr><tr><td><strong>[FW] screening</strong><br>(pH 9; T = 35°C)</td><td>FW7.8</td><td>0</td><td>0.57 ± 0.05</td><td><strong>0.13 ± 0.06</strong></td><td>0.07 ± 0.01</td><td>0.79 ± 0.03</td><td>78.9 ± 1.8</td><td>7.11 ± 0.02</td></tr><tr><td></td><td>FW25</td><td>0</td><td>0.40 ± 0.05</td><td>0.04 ± 0.01</td><td>0.16 ± 0.03</td><td>0.62 ± 0.03</td><td>67.4 ± 1.9</td><td>6.39 ± 0.19</td></tr><tr><td></td><td>FW50</td><td>0</td><td>0.23 ± 0.01</td><td>0.01 ± 0.00</td><td>0.17 ± 0.01</td><td>0.47 ± 0.01</td><td>57.9 ± 2.6</td><td>5.71 ± 0.04</td></tr><tr><td></td><td>FW100</td><td>0.19 ± 0.05</td><td>0.05 ± 0.01</td><td>0.01 ± 0.00</td><td>0.06 ± 0.03</td><td>0.12 ± 0.03</td><td>34.5 ± 1.1</td><td>4.83 ± 0.08</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Boxplot's representation of the propionic acid selectivity and the statistical differentiation of groups (Wilcox test, Holm correction) based on initial pH, temperature, and food waste concentration.</figcaption></figure>

9 yielded the highest proportion of propionic acid among other VFA, confirming the results of Moretto et al. (2019) who observed 31 % propionic acid at pH 9 in acidogenic fermentation of urban waste. Although Maspolim et al. (2015) and Zhao et al. (2018) identified pH 9 as optimal for total VFA accumulation, the effect on VFA composition was not particularly investigated. Overall, it was concluded that initial pH affected both substrate solubilisation and metabolic pathways, resulting in different final VFA composition profiles. In addition, the influence of the initial pH on propionic acid selectivity is the specific observation of this study, while previous research showed a selection of the microbial pathways between hydrogen, lactate or VFA production only (Feng et al., 2018).

## 3.2. Influence of temperature on metabolite production

Fermentation tests were carried out at four different temperatures (24, 30, 35 and 45°C), and the fermentation products were analysed after 8 days. The total metabolite production ranged from 5.29 ± 0.58 g/L to 6.35 ± 0.51 g/L (Fig. 1B), with a maximum at 35°C. Accumulation was lower at 30°C, with intermediate levels at 24°C and 45°C. The main metabolites remained the same, i.e. acetic, propionic, and butyric acids, with ethanol concentrations varying significantly with temperature. At 24°C, succinate was present and acetic acid was the dominant metabolite, while propionic acid was found at a low concentration (0.40 ± 0.07 g/L). At 30°C, the concentration of propionic acid was similar (0.45 ± 0.07 g/L), but acetic acid decreased, and butyric acid slightly increased. At 35°C, propionic acid reached the highest concentration (0.67 ± 0.16 g/L), becoming the second most abundant metabolite (13.05 ± 2.86 % on COD basis). Fermentation at 45°C led to a similar total production but favoured the production of acetic acid, butyric acid, and ethanol, minimising the propionic acid concentration. The selectivity of propionic acid was the highest at 35°C, reaching 13.1 ± 2.9 %, significantly more than at any other temperature tested (Fig. 2). At 45°C, only 3.0 ± 0.9 % of the metabolites produced on a COD basis were propionic acid, with higher amounts of acetic acid, butyric acid, and ethanol.

The fermentation temperature showed a significant impact on metabolite production. In this study, the lowest temperature (24°C) probably resulted in a lower enzymatic activity, as evidenced by the presence of succinic acid after 8 days, which is an intermediate of the reductive fermentation pathway, generally converted into propionate or butyrate (Cui et al., 2024; Esquivel-Elizondo et al., 2017; Narisetty et al., 2022). At 30°C, the lower metabolite production indicated less degradation of organic matter (66.0 ± 6.0 % of total COD introduced being converted into metabolites), significantly lower than other conditions. A higher production was observed at 35°C, with the best concentration and proportion of propionic acid. This is in line with the literature where mesophilic conditions (35–37°C) were commonly identified as appropriate for VFA production (Jiang et al., 2013; Wainaina et al., 2019; Zhang et al., 2020). At this temperature, most mesophilic bacteria effectively grow, thus allowing a wide diversity of metabolic pathways and the production of various end-products. Interestingly, this temperature also favours propionic acid production over other VFAs in this study. While thermophilic conditions (above 50°C) have been shown to favour substrate solubilization through enhanced enzymatic activities (Garcia-Aguirre et al., 2017), these conditions do not necessarily increase the total VFA production (Moretto et al., 2019). Nonetheless, some other studies observed an enhancement in VFA accumulation under thermophilic conditions (Garcia-Aguirre et al., 2017; Jiang et al., 2013). As thermophilic processes (>55°C) require a higher energy demand, they were not preferred in this study. Here, the fermentation at 45°C changed the composition of metabolites without affecting the total metabolite production. The selectivity of propionic acid was very low (3.0 ± 0.9 %), and the effluent was composed mainly of acetate, butyrate, and ethanol. It is therefore
likely that slightly different metabolic pathways were predominant at 45°C compared to 35°C. The combination of alkaline pretreatment and the temperature of 35°C was already reported to favour acetic and propionic acid production, while fermentation at 55°C favoured butyric acid accumulation (Yuan et al., 2019). In this study, a partially similar process was observed, where fermentation at an alkaline pH of 9 and a temperature of 35°C showed the best proportion of propionic acid. In addition, an experimental temperature of 45°C was found here to significantly change the composition of the products during food waste fermentation, as reported by Perez-Esteban et al. (2024) for long-term fermentation. Temperature is therefore confirmed as a determining factor in the production of specific metabolites in fermentation, with an effect observed even in batch conditions. The key observation is the influence on the propionic acid content in the metabolites. This effect also depends on the substrate and inoculum used, as the selected microbial communities seem to be responsible for this difference in metabolic profile (Cavinato et al., 2017; Jiang et al., 2020; Perez-Esteban et al., 2024).

### 3.3. Influence of initial FW concentration on metabolite production

The concentration of food waste was also increased from the control value of 7.8 g VS/L to 100 g VS/L (7.8, 25; 50; 100 g VS/L) to assess its impact on the fermentation process. The total metabolite concentration consistently increased with the initial food waste concentration, rising from 6.30 ± 0.19 g/L to 34.59 ± 1.79 g/L (Fig. 1C). As the amount of degradable substrate increased, a higher concentration of total metabolites was expected. However, the reaction advancement (%g COD metabolites/ g COD biodegradable introduced) after 8 days, reflecting the conversion of the provided organic matter, exhibited the opposite trend, decreasing from 78.9 ± 1.8 % at the lowest concentration to 34.5 ± 1.12 % at 100 g VS/L (Table 2). This indicates that a higher substrate concentration resulted in reduced substrate conversion during the 8-day fermentation period. In addition, the final pH also dropped while substrate concentration increased, suggesting a higher acidification of the fermentation. Interestingly, the metabolite composition was also affected by the initial concentration. At the lowest substrate concentration, the composition was similar to previous experiments: acetic acid was the main metabolite, followed by propionic acid (reaching a maximal concentration of 1.00 ± 0.47 g/L) and butyric acid. When increasing the initial concentration, the proportion of butyric acid increased at the expense of propionic acid. Specifically, propionic acid concentration even decreased with higher food waste concentration, while acetic and butyric acid concentrations increased. This resulted in very low yields for propionic acid production: around 4 % of VS from FW was converted into propionic acid in the conditions of 25 g VS/L, and lower than 1 % for higher food waste concentrations (Table 2). For an initial substrate concentration of 50 g VS/L, a significant amount of caproic acid was produced (2.41 ± 1.42 g/L), while this medium-chain fatty acid was not observed in other conditions. Interestingly, at the highest food waste concentration, total VFA production declined, with lactic acid becoming the main metabolite at 19.05 ± 5.47 g/L. Acetic and butyric acids, as well as ethanol, were present but in smaller amounts. As shown in Fig. 2, the selectivity of propionic acid was significantly higher in the low-concentration batches, reaching 19.4 ± 9.3 % g COD/ g COD metabolites produced. This selectivity was significantly lower for all the other conditions (higher concentrations of food waste), varying between 2.4 ± 0.2 and 6.5 ± 2.6 %. In these batch tests, the production of other VFAs (acetic and butyric acids), but also caproic and lactic acids, was favoured. The initial food waste concentration influenced the total metabolite production and composition, thereby significantly affecting metabolic pathways.

The substrate concentration has been shown to affect metabolic pathways in fermentation (Yuan et al., 2019). However, this effect strongly depends on the feedstock used, and only a little information is available on the fermentation of food waste to produce propionic acid. In the present study, the optimal concentration for propionic acid production was the lowest tested, i.e. 7.8 g VS/L of food waste. In fact, providing a larger quantity of available substrate to the microorganisms led to different orientations of the degradation and conversion processes. The lower reaction advancement obtained with a FW concentration of 100 g VS/L, 34.5 ± 1.1 % of COD provided converted into metabolites, suggests that the substrate conversion was slower. Caproic acid, a medium-chain fatty acid (MCFA), was significantly produced after 8 days at an initial concentration of 50 g VS/L, whereas at the lowest concentration of food waste, acetate, propionate, and butyrate were the main metabolites. The production of caproic acid could have resulted from lactic acid chain elongation, as reported by Candry et al. (2020), occurring at pH levels between 5.5 and 6. Consistently, at the 50 g VS/L concentration, the final pH was in this range, likely favouring this process. Operating at a concentration of 100 g VS/L resulted in a sharp pH drop below 5 due to a rapid production of organic acids, reaching up to 11.77 ± 1.86 g VFA/L. At this low pH, most VFA remain undissociated (pKa 4.76–4.88) and can diffuse to microbial cells, thereby inhibiting their metabolism (Duong and Nga, 2024; Feng et al., 2018). Acidogenic and hydrolytic bacteria were likely inhibited, contributing to the observed impact on hydrolysis activity and the lower substrate conversion yield (Duong et al., 2022). In contrast, lactic acid bacteria, which are more resistant to low pH, likely became dominant under these conditions (Detman et al., 2021; Feng et al., 2018), resulting in a high concentration of lactic acid (19.05 ± 5.47 g/L) in the fermentation broth. The growth of LAB has probably reinforced the inhibition of acidogenic bacteria through their ability to produce bacteriocins (Detman et al., 2021). Moretto et al. (2019) found similar results in continuous stirred tank reactors (CSTR) by increasing the organic loading rate (OLR). That was particularly detrimental to VFA production by reducing the solubilisation of COD and VS, leading to decreased production yields. Reactor overloading likely resulted in VFA production failure, even in batch systems. The organic load should create a high pressure on the microorganisms, thus influencing the microbial community development, which seems to be detrimental for propionic acid production. The adjustment of initial concentration is therefore a key point for driving process routes. Although higher VS concentrations are preferable for industrial applications, it appears from this study that they are not suitable to the controlled production of VFAs. A lower concentration is preferable to increase the degradation of the organic matter and the proportion of propionic acid among the metabolites. The value of 7.8 g VS/L was chosen in this study relative to the other tested values, but it is not an absolute optimum.

Overall, the metabolite profile exhibited a mixture of VFA, with propionic acid reaching a concentration of 1.71 ± 0.13 g/L and
representing up to 31.89 ± 5.39 % of the metabolites produced, while acetic acid remained the main product. To enhance the recovery of propionic acid from this mixture, a subsequent polishing step could be considered to degrade the other metabolites (acetate and butyrate), thereby enriching the effluent in propionate. This can be achieved by implementing Microbial Electrolysis Cell (MEC) technology using electroactive microorganisms to specifically degrade the VFAs without consuming the propionate, as the latter is more difficult to degrade than the acetate. Magdalena et al. (2023) demonstrated such an approach, achieving complete acetate degradation to hydrogen within the first few days of the process.

## 3.4. Effect of experimental parameters on microbial community composition

See Fig. 3

### 3.4.1. Effect of pH

Microbial community composition was analysed using metabarcoding across samples at pH 7, 9, and 10 (no sample at pH 8). Relative abundances at the family level are presented in Fig. 3. The main bacterial orders across all samples were *Clostridiales*, *Enterobacteriales*, *Bacillales*, and *Lactobacillales*, with the *Clostridiales* order representing over 50 % of the microbial community in all conditions. *Enterobacteriales* was the second most abundant order at pH 7, while *Bacillales* was second at pH 9 and 10. *Lactobacillales* contributed 3.3–12.1 % across all samples. Family composition varied notably with the initial pH. Within the *Clostridiales* order, *Clostridiaceae* was the major family present in all samples, between 30.8 % at pH 9–52.8 % at pH 10, with substantial change within the clusters 1, or 2. *Family XI* (likely *Tissierellaceae*) was present in all samples, while *Peptostreptococcaceae* was observed only at pH 9 (2.3–7.3 %). Concerning the *Bacillales* order, the *Bacillaceae* family accounted for 25.6 % at pH 10 but was absent elsewhere. *Family XII* (*Exiguobacterium* genus) was < 5 % at pH 7, increased to 10–22 % at pH 9, and decreased to 6.8 % at pH 10. From the *Enterobacteriales* order, the *Enterobacteriaceae* family declined from 26.9 % to 27.4 % at pH 7–9.8–12.7 % at pH 9 and was absent at pH 10. In *Lactobacillales*, the *Leuconostocaceae* family accounted for 7–11 % at pH 7 (not displayed), while *Enterococcaceae* was 3.2–6.2 % at pH 9 and 10. The composition of the microbial community was then different in the samples obtained at different pH levels.

The adjustment of the initial pH is known to influence microbial communities and the metabolic pathways at the beginning of the process, as observed in the present work (Maspolim et al., 2015; Zhang et al., 2020). Cui et al. (2024) showed that changing the fermentation pH led to a complete restructuring of the microbial community, in relation to the change in metabolite production. In these experiments, the main metabolic pathways were the production of acetic acid, propionic acid, butyric acid and ethanol, in different proportions depending on the pH. Increasing the initial pH showed a decrease in *Enterobacteriaceae* relative abundance and an increase in families from the *Bacillales* order, which might be related to the shift from acetate/ butyrate type fermentation to acetate/ propionate type at pH 9 and acetate type at pH 10. Among the *Clostridiales* order, families shifted from *Clostridiaceae* 1, *Family XI*, and *Peptostreptococcaceae* to *Clostridiaceae* 2 mainly. This change was likely linked to the observed metabolic shift, with less evidence as the

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Final composition of microbial communities in relative abundance at the family level (15 more abundant families) for samples at the end of pH screening experiments, temperature-screening experiments and food waste concentration screening experiments. Most genera from *Family XI* (*Clostridiales*) were assigned to the *Tissierellaceae* family according to LPSN. *Exiguobacterium* was the only genus from cluster Family XII (*Bacillales*), belonging to the *Bacillaceae* family according to LPSN.</figcaption></figure>
*Clostridiaceae* family remained dominant. Families within the *Lactobacillales* order are often associated with lactic acid production, and families *Leuconostocaceae* and *Enterococcaceae* accounted for 6.3–11.1 % of the microbial communities in all conditions. As lactic acid production typically occurs naturally at the beginning of the fermentation of waste, it was assumed that these families were associated with the production of this metabolite, which was subsequently reconsumed (Roslan et al., 2023). Additionally, they likely contributed to ethanol production as observed in all samples, through hetero-lactic fermentation (Detman et al., 2021). Changes in the microbial community composition due to pH have already been reported by Maspolim et al. (2015) during waste activated sludge fermentation, where alkaline conditions (pH 8 and 9) led to a strong enrichment in acidogenic bacteria. This was associated with better substrate solubilization, but lower VFA production was observed above these pH values as acidogenic bacteria were probably inhibited. Indeed, Zhao et al. (2018) reported that a pH above 9 could inhibit acidogenic bacteria. In the present study, the initial pH also modified the microbial composition, suggesting that inhibition of some acidogenic bacteria at pH 10 was responsible for the higher relative abundance of different families (*Bacillaceae*, *Clostridiaceae* 2) and reduced propionic acid production. However, the change in the proportion of *Enterobacteriaceae* family depending on initial pH was observed here for the first time. The composition of the microbial community is effectively impacted by the initial pH, as the latter selects for organisms that are tolerant of the applied pH. These adapted bacteria can grow more efficiently at the start of fermentation process and consequently dominate the community (Cui et al., 2024; Shi et al., 2022). In addition, the same microorganisms may function differently depending on the pH, as demonstrated by Cui et al. (2024), due to changes in the NADH/NAD+ ratio, which governs the equilibrium and promotes certain enzymatic reactions.

### 3.4.2. Effect of temperature

The final microbial community across all temperature changes was dominated by four bacterial orders (i.e., *Clostridiales*, *Bacillales*, *Enterobacteriales*, and *Lactobacillales*), though their relative abundances varied significantly with temperature (Fig. 3). The proportion of *Clostridiales* order increased when increasing the temperature, from 26.5 % at 24°C to 86.8 % at 45°C. In contrast, the *Bacillales* order decreased from 35.5 % at 24°C to 12.4 % at 45°C and *Enterobacteriales* from 37.7 % to 0 % at 45°C. *Lactobacillales* order, represented by the family *Streptococcaceae*, accounting for less than 1 % at 45°C (not displayed). At family level, the composition shifted with the temperature, in relation to the observations made at order level. At 24°C, *Enterobacteriaceae* and *Family XII* (*Bacillales*) made up 73.2 % of the community, dropping to 49.5 % at 30°C, and by 35°C, the *Enterobacteriaceae* almost disappeared. At 45°C, the community was dominated by *Clostridiales* families (*Clostridiaceae* 1 and 2, *Family XI*, and *Ruminococcaceae*) along with *Bacillaceae*, while families like *Caldicoprobacteraceae*, *Defluviitaleaceae*, and *Streptococcaceae* emerged only at higher temperatures (not displayed).

The fermentative microbial community was strongly influenced by temperature, particularly at 45°C. Literature suggests that working at higher temperatures may require an inoculum adapted to thermophilic growth to effectively convert dissolved organic matter into metabolites (Moretto et al., 2019). At 45°C, some families, which were not detectable at lower temperatures, were predominant (*Clostridiaceae* 2, *Caldicoprobacteraceae*, *Defluviitaleaceae*, *Bacillaceae*, and some *unclassified Clostridiales*), indicating a higher tolerance to elevated temperatures compared to other families that dominated at 24–30–35°C (*Clostridiaceae* 1, *Family XII Bacillales*, and *Enterobacteriaceae*). Overall, the microbial community at 45°C was largely dominated by the *Clostridiales* order (86.8 %), while it was more balanced at lower temperatures: *Enterobacteriaceae* and *Family XII* (*Bacillales*) were very abundant at 24°C and 30°C, probably meaning that these bacteria have a lower optimal growth temperature. The decline of the *Enterobacteriaceae* family at 35°C was probably due to competition from bacteria with higher growth rates from the *Clostridiales* order, which became more abundant. It appears that *Enterobacteriaceae* and *Family XII* (*Bacillales*) were unable to withstand the fermentation temperature of 45°C and therefore did not develop, bringing into the community more resistant bacteria. Zhang et al. (2020) also observed a shift in the microbial composition at a temperature of 55°C with an increase in the proportions of *Romboutsia*, *Bacillus*, *Proteiniphilum*, and *Defluviitoga* genera compared to a 35°C fermentation. The increased proportion of *Bacillus* at 55°C correlated with the observed rise in the *Bacillaceae* family in this study, to which *Bacillus* belongs. It was assumed that these bacteria are typical of high-temperature waste fermentation due to their greater resistance to temperature. The change in the microbial community composition might also be responsible for changes in metabolic products. At 45°C, the lower proportion of propionic acid could be due to the disappearance of microorganisms responsible for its production, outcompeted by better-adapted bacteria. The adaptation of the microbial community was already demonstrated (Jiang et al., 2020; Perez-Esteban et al., 2024) and is here related to the different metabolite production observed, in the specific fate of food waste fermentation at alkaline pH.

### 3.4.3. Effect of food waste concentration

The bacterial orders constituting the final microbial community were also composed of *Clostridiales*, *Enterobacteriales*, *Lactobacillales*, and *Bacillales* (Fig. 3). *Clostridiales* consistently dominated the community, representing over 50 % in all samples. *Lactobacillales*, initially low at 7.8 g VS/L, increased with substrate concentration, up to 31.8 % at 50 g VS/L, and 17.6–20.7 % at 100 g VS/L. *Bacillales* was below 1 % at 50 g VS/L but reached 28.9 % in one replicate at 100 g VS/L. *Enterobacteriales*, represented solely by the *Enterobacteriaceae* family, appeared only at low concentrations up to 8.2 %. At the family level, low concentrations exhibited a greater diversity of families within the *Clostridiales* order, including *Lachnospiraceae*, *Ruminococcaceae*, *Family XI*, *Clostridiaceae* 1, *Peptostreptococcaceae*. As the substrate concentration increased, *Clostridiaceae* 1 became the most dominant family. *Lactobacillales* abundance varied with the substrate concentration, with *Enterococcaceae* being abundant at 25 and 50 g VS/L and *Lactobacillaceae* increasing to 15 % at 100 g VS/L (not displayed). *Family XII* from *Bacillales* dropped from 13.5 % in the control down to 9.7 % at 25 g VS/L, almost disappeared at 50 g VS/L and re-emerged at 100 g VS/L (8.8–28.9 %). *Exiguobacterium* was the sole genus found in this cluster.

At the highest initial substrate concentration of 100 g VS/L, the increase in relative abundance of the *Lactobacillaceae* family to 9–13 % was likely caused by a pH drop below 5, which favoured the growth of LAB, more resistant to low pH than acidogenic bacteria, as discussed previously. The disappearance of families such as *Enterobacteriaceae*, *Ruminococcaceae*, *Lachnospiraceae*, and *Family XI* also
supports the hypothesis of such inhibition. On the contrary, the *Clostridiaceae* 1 family remained abundant at 100 g VS/L, probably indicating that these bacteria are more resistant to low pH and high concentrations. Acidogenic bacteria were previously identified to resist a wide range of pHs (Yuan et al., 2019), but the change in the composition of the microbial community demonstrated here suggests that not all acidogenic bacteria have the same tolerance to this drop in pH. The high relative abundance of *Enterococcaceae* in the fermentation at 50 g VS/L was attributed to the final pH of 5.75 and the possible inhibition of VFA producers in favour of lactic acid producers. However, no lactic acid was detected in the final fermentation broth, suggesting later consumption. At the lowest concentration (7.8 g VS/L), the composition of the microbial community was more diverse, while some key families, such as *Clostridiaceae* 1, *Enterococcaceae*, and *Family XII Bacillales*, clearly dominated at higher concentrations. This can be linked to the differences in metabolic pathways observed, confirming the influence of a high organic load on the selection of microorganisms in the community, impacting metabolite production.

### 3.4.4. Redundancy analysis

A redundancy analysis (RDA) was conducted using the overall data collected in this study to examine potential correlations between microbial communities and metabolite production. This analysis is based on a similar principle to PCA (principal component analysis), by assuming linear correlations between parameters, but allows for relating two linked datasets by finding redundancies among the data. Significantly robust, this analysis explained 72.4 % of the variance in the dataset through its two main axes, as represented in Fig. 4. pH and food waste concentrations exhibited opposite trends on the first axis, while temperature was related to the second axis. Even-carbon VFAs, isovaleric acid, lactic acid, and food waste concentration were related to *Clostridiaceae* 1, *Lactobacillaceae*, and to a lesser extent to *Enterococcaceae*. On the opposite, pH was related to *Bacillaceae*, *Clostridiaceae* 2 and 3, *Ruminococcaceae*, and *unclassified Bacillales* and *Clostridiales* families. Propionic and valeric acids, along with propionic acid selectivity, were correlated with *Family XII* (*Bacillales*), *Enterobacteriaceae*, and *Lachnospiraceae* families.

Even though this type of analysis is not a functional assignment of microbial families, it could help to relate observations made

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Redundancy analysis conducted on microbial data at the family level and environmental data (initial pH, temperature, food waste concentration, metabolite production, and propionic acid selectivity) for all samples.</figcaption></figure>
regarding the operating parameters, the metabolite products and the relative abundance of microbial families. Some groups of metabolites appear to be correlated, suggesting close metabolic pathways (acetate and butyrate) linked to operating parameters such as a higher concentration of food waste. On the other hand, the group of metabolites formed by propionic acid and valeric acid do not appear to be linked to the previous ones (orthogonality), suggesting independent metabolic pathways. Furthermore, the concentration of these metabolites in the final effluent is linked to the relative abundance of some microbial families: there is a relationship between lactic acid and the *Lactobacillaceae* family, which was already reported in previous studies (Detman et al., 2021; Feng et al., 2018). Meanwhile, *Clostridiaceae* 1 family correlated with acetic, butyric, isovaleric and caproic acids, as they are identified as main contributors of dark fermentation for H₂ production (Perat et al., 2024). This suggests a correlation between the H₂ type fermentation and the lactic type fermentation, while they have no link to propionate fermentation due to their orthogonality in the representation. Furthermore, propionic and valeric acids, along with propionic acid selectivity, correlated to families of *Enterobacteriaceae*, *Lachnospiraceae*, and *Family XII* from the *Bacillales* order. Propionic and valeric acids are often correlated as odd-carbon VFA and related pathways (Huang et al., 2018), as propionate can be elongated into valerate (Agler et al., 2011). However, the microorganisms responsible for their synthesis differ according to the fermentation conditions. In mixed-acid fermentation at pH 5–6, *Megasphaera* and *Prevotella* are commonly associated with the production of various VFA, including propionic acid (Cibis et al., 2016; Feng et al., 2018). Nonetheless, they were not found in this study as the fermentation started under alkaline conditions. In alkaline fermentation of waste-activated sludge, Maspolim et al. (2015) identified genera like *Tissierella*, *Petrimonas*, *Proteiniphilum*, *Levilinea*, *Proteiniborus* and *Sedimentibacter* at pH 8, and some of them at pH 9, associated with the production of acetic and propionic acids. In the present study, bacteria correlated with propionic and valeric acids were *Enterobacteriaceae*, *Lachnospiraceae* and *Exiguobacterium* genus (*Family XII* of *Bacillales*), highlighting other acidogenic bacteria in alkaline fermentation. *Exiguobacterium* genus included highly versatile species capable of degrading complex organic matter through the production of resistant hydrolytic enzymes that can break down a wide range of compounds, including lignocellulosic materials (Harirchi et al., 2022b), likely indicating a hydrolytic role. In particular, glycosidase and glycoside hydrolase, that could function across a wide range of pH and temperature conditions, are produced by species belonging to this genus (Vishnivetskaya et al., 2011). However, complete genome annotations of these bacteria do not show any genes associated to the Wood Werkman or acrylate pathways, which are known to lead to propionic acid production. The degradation products generated by enzymatic activity of *Exiguobacterium* could include precursors of propionic and valeric acids (odd-carbon molecule), which may explain the observed link between the enrichment of this bacterium in the microbial community and the production of these metabolites. The *Enterobacteriaceae* family, correlating also with the same metabolites, is highly diverse, making functional assignment challenging, especially as the majority of microorganisms in this experiment remain unclassified. Finally, the *Lachnospiraceae* family, commonly found in the human gut, is known for the synthesis of short-chain fatty acids such as propionic, acetic, and butyric acids (Vacca et al., 2020). Members of this family likely have fermentative activities related to propionic acid production, supporting the link between these two variables in the redundancy analysis. Similar studies examining how operating parameters influence microbial community composition can provide valuable comparisons. For instance, Huang et al. (2018) demonstrated that pH significantly impacts VFA composition during waste-activated sludge fermentation with glycerol as a carbon source, identifying pH 8 as optimal for propionic acid production, with key producers from the *Clostridiales* order like *Proteiniborus* and *Petrimonas*. The differences in microbial communities between their study and this one likely resulted from the different substrates used (food waste vs. sludge and glycerol). Detman et al. (2021) conducted a similar analysis for lactate and butyrate production under acidic conditions, linking lactate to *Lactobacillus*, *Leuconostoc*, *Fructobacillus*, and *Bifidobacterium*, and butyrate to *Clostridium*. At the family level, our findings align with these observations, with *Lactobacillaceae* associated with lactate and *Clostridiaceae* with acetate and butyrate. However, the specific focus on propionic acid enrichment and its potential link with *Exiguobacterium*, *Enterobacteriaceae* and *Lachnospiraceae* is a new observation of this study. It was therefore demonstrated that even after an 8-day batch, the microbial communities were not the same depending on the parameters used for the fermentation, and that these differences could relate to metabolite production. Running a continuous fermentation process with parameters similar to those tested in batch could enable the above observations to be consolidated and the development of the microbial community to be observed.

## 4. Conclusion

In this study, a screening of the operating parameters on fermentation end products from food waste was investigated. Significant metabolic changes were observed depending on initial pH, temperature and food waste concentration, allowing the identification of the most favourable conditions for VFA production with higher propionic acid selectivity (initial pH of 9, temperature of 35°C and FW concentration of 7.8 g VS/L). Under these conditions, propionic acid reached 1.71 ± 0.13 g/L and represented up to 31.89 ± 5.39 % of the total metabolites, although acetic acid remained the dominant product. Microbial communities were affected by the change in operating parameters, in relation to the different metabolite productions observed. Hydrolytic and acidogenic bacteria were predominant, particularly those belonging to *Clostridiales*, *Bacillales*, *Enterobacteriales* and *Lactobacillales* orders. Correlations between microbial families, metabolite production and operating parameters highlighted the potential implication of *Enterobacteriaceae*, *Lachnospiraceae* families and *Exiguobacterium* genus in the production mechanism of propionate and valerate. Overall, this study gives new insights into the selective VFA production from food waste in relation to microbial community structure, contributing to a better understanding and potential control of the fermentation process.

### CRediT authorship contribution statement

Mathilde Bourgeois: Writing – original draft, Visualization, Investigation, Formal analysis, Data curation, Conceptualization.
**Sabine Taussac:** Investigation. **Nicolas Bernet:** Writing – review & editing, Validation, Supervision, Resources, Conceptualization.

**Renaud Escudié:** Writing – review & editing, Validation, Supervision, Conceptualization. **Eric Trably:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization.

Declaration of Competing Interest

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

Acknowledgements

The authors acknowledge the financial support of BIOCTANE project with Grant N° 101084336. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure and Environment Agency (CINEA). Neither the European Union nor CINEA can be held responsible for them. Data are available on the depository Zenodo under the denomination 10.5281/zenodo.13933076. The authors would like to acknowledge INRAE Bio2E Facility (Bio2E, INRAE, 2018. Environmental Biotechnology and Biorefinery Facility (https://doi.org/10.15454/1.557234103446854E12) where part of the experiments was conducted.

Data availability

Data will be made available on request.

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