The organic fraction of municipal solid waste (OFMSW) must be stored for hours or days before being fed to the anaerobic digestion reactors. This storage leads to spontaneous lactic acid fermentation, and volatile fatty acids (VFAs) and ethanol are produced by naturally occurring microorganisms. This research deals with fermentation and hydrolysis by controlling the OFMSW storage (silage) conditions. Using only naturally occurring microorganisms as inoculum, OFMSW fermentation in a semi-continuous reactor at pH values of 4, 5, and 6 was performed. During 6 days, samples were collected and analyzed daily for VFAs, ethanol, and lactic acid. At pH 4, the main products were ethanol, lactic acid, and acetic acid; at pH 5, lactic acid predominated, decreasing after day 4; at pH 6, acetic acid formed rapidly and after day 1, the concentration remained constant. At pH 6, butyric acid reached the highest concentration of all VFAs. The microbial diversity increased with pH. Metataxonomic analysis supports the possibility that the fungus of the Pichia genus is responsible for ethanol production and that various bacteria are responsible for VFAs, lactic acid production, and acetogenesis. Acetogenesis was the main pathway for the decrease in lactic acid and ethanol over time.

  • Organic fraction of municipal solid waste (OFMSW) fermentation produces ethanol using naturally occurring microorganisms.

  • Fermentation allows the hydrolysis of simple carbohydrates.

  • Carbohydrates in OFMSW allow efficient production of low-chain acids.

  • Microbial diversity decreases with pH during OFMSW fermentation.

  • The fermentation rate increases with pH during fermentation.

Fermentation of the organic fraction of municipal solid waste (OFMSW) is essential when considering a waste biorefinery. Some products of this fermentation include volatile fatty acids (VFAs), ethanol, and lactic acid (Strazzera et al. 2018). VFAs can be used as precursors in the chemical industry, and ethanol has become an important biofuel. Ethanol is also a suitable substrate for methane production as it does not decrease the pH and consumes alkalinity during the process (Jojoa-Unigarro & González-Martínez 2021). Lactic acid is vital to the chemical and food industries (Kamm & Kamm 2004).

The metabolic pathways for ethanolic, acid, and lactic acid fermentation are well described in the literature, and the responsible microorganisms can also be found elsewhere (Strazzera et al. 2018). Contrary to industrial fermentation, in the food industry, the variety and complexity of the microbial communities found in OFMSW make fermentations produce various products simultaneously, and different types of microorganisms compete for the substrates. The substrate also requires careful consideration because of its complexity: OFMSW contains low-molecular-weight carbohydrates from fruits and vegetables, as well as complex carbohydrates such as cellulose and hemicellulose, lipids, and proteins (Sawatdeenarunat et al. 2015; Figueroa-Escamilla et al. 2021).

According to Jojoa-Unigarro & González-Martínez (2021) and Castellón-Zelaya & González-Martínez (2021), the naturally occurring microorganisms in OFMSW are best adapted for the (anaerobic) hydrolysis of complex molecules proving that ethanol and lactic acid can be produced from OFMSW without sanitizing the substrate and pH control. They also concluded that, under the conditions tested, the amounts produced did not justify refining the fermentation products. Not enough information can be found on manipulating the fermentation conditions using complex substrates to obtain higher ethanol and lactic acid yields using only naturally occurring microorganisms as inoculum. There is no consensus on which microorganisms are responsible for ethanol production. Wu et al. (2017), Wang et al. (2020), and Ebrahimian et al. (2022) report that ethanolic fermentation is caused by Zymomonas mobilis or any other yeast and, for a heterolactic fermentation, is a product of Lactobacillus. Acid fermentation can be a fermentation product of any of the lipids, proteins, and carbohydrates from OFMSW; however, ethanol can only be produced from sugars, and it stops only when sugars are depleted (Bouallagui et al. 2008; Alcántara-Hernández et al. 2017).

The fermentation time of municipal organic waste plays a key role in metabolite production. Studies have reported that fermentation periods between 3 and 10 days maximize the conversion of organic matter into valuable products such as ethanol, lactic acid, and/or VFAs (Strazzera et al. 2018; Swiatkiewicz et al. 2021). At fermentation times of 6 days, acetic accumulation is favored since direct acetic fermentation can take place as acetogenesis (Zhang et al. 2020; Battista et al. 2022; Papa et al. 2022). Fermentation times shorter than 3 days result in a high concentration of ethanol and lactate (Demiray et al. 2018; Darwin et al. 2019). In processes with 2–4 days of fermentation, carbohydrates are significantly consumed, favoring lactic and ethanolic pathways. In contrast, fermentation periods longer than 4 days may lead to protein hydrolysis, resulting in higher amounts of acetic and butyric acid (Zhang et al. 2020).

pH is the most significant parameter affecting metabolic pathways. Raw OFMSW typically has a pH between 6 and 6.5 pH units. In an acid fermentation process with naturally occurring microorganisms, the pH can decrease to 3.8–4 (Zheng et al. 2015; Wu et al. 2017). Due to acid production, the fermentation processes tend to reduce the pH. Zhang et al. (2005) report VFAs presence at pH 6 and higher with little ethanol and lactic acid production; above pH 8, no ethanol could be detected (Wang et al. 2011). Under a pH of 6 and shorter reaction times, ethanolic fermentation can be expected, and lactic acid can be produced (Zheng et al. 2015; Wu et al. 2017). pH values between 5.0 and 6.0 are optimal for lactic and acetic acid production, promoting the growth of Lactobacillus and Bifidobacterium, key bacteria during heterofermentative fermentation (Ali et al. 2019). In contrast, pH values below 4.5 favor the accumulation of VFAs but can inhibit acidogenic bacteria, reducing the efficiency of the process (Cheah et al. 2018).

The primary purpose of this research was to evaluate OFMSW fermentation under different pH values using naturally occurring microorganisms as inoculum. This could be performed in a semi-continuous reactor with automatic pH control at 4, 5, and 6. Analyzing the fermentation products and the corresponding microbial populations was essential to achieving the desired results. This study investigated OFMSW acid fermentations at three different pH values (4, 5, and 6) to determine the fermentation products and shifts in microbial communities under specific environmental conditions. This research helps plan a biorefinery for OFMSW where metabolites are identified, in which amounts, and under which conditions.

OFMSW sampling and characterization

This experiment used source-separated OFMSW from the transfer station Coyoacán in Mexico City as substrate. Sampling was performed using the ASTM sampling and quartering methods (ASTM 2016). Approximately 100 kg from 11 randomly selected trucks were separated, and 1 ton (1000 kg) was thoroughly mixed using a skid-steer loader and shovels. The quartering method was applied twice to reduce the amount to approximately 100 kg. Plastic bags, stones, and wood were hand-separated, and the clean OFMSW was distributed in 2-L freezing bags and frozen at −20 °C.

When needed, OFMSW was thawed overnight at room temperature. To achieve homogeneity, OFMSW was allowed through an extruder and a grinder to achieve particles under 5 mm in size (Campuzano & González-Martínez 2016). No further classification of the particles was made, and the average particle size was visually determined using a stereoscopic microscope. OFMSW was then characterized for pH, total solids (TS), volatile solids (VS), chemical oxygen demand (COD), Kjeldahl (KN), and ammonia nitrogen (NH4-N) were used to determine organic nitrogen. These determinations were performed according to Standard Methods (APHA 2017). OFMSW from Mexico City can be compared in general to several other cities worldwide, especially European ones (Campuzano & González-Martínez 2016).

Fermentation reactor with pH automatic control

A 4.5-L glass, jacketed reactor with a reaction volume of 3 L was operated at 35 °C as a batch. The reactor had a twin-bladed mechanical mixing (BPC Instruments, Sweden) and automatic pH control consisting of an electrode, a control device, and two separately operating peristaltic pumps for solutions of NaOH 2 M and phosphoric acid 2 M. pH was adjusted continuously with an error of 0.1 pH units.

According to the recommendations of Wu et al. (2017), the reactor was operated at a solids concentration of 4% to avoid product inhibition. Because lactic and ethanolic fermentations with OFMSW are relatively short processes, less than 3 days (Demiray et al. 2018), the experiment duration was set to 6 days. Sampling was performed by extracting 1 L and replacing the same volume with freshly processed OFMSW. One part of the sample was centrifuged to separate dissolved substances from solids, and the two fractions were analyzed for COD, total and volatile solids, VFAs, lactic acid, and alcohol.

Analytical methods

Dissolved COD was determined after filtering the sample through a 0.45 μm membrane, and pH was determined according to Standard Methods (APHA 2017). Lactic acid was determined using the spectrophotometric method proposed by Borshchevskaya et al. (2016). The carbohydrate technique used was that of Dubois et al. (1956), which allows quantification of total carbohydrates (simple carbohydrates). Methanol, ethanol, and VFAs (acetic, propionic, isobutyric, butyric, isovaleric, valeric, and hexanoic acids) were determined using a gas chromatograph (HP 5890 GC System) equipped with a flame ionization detector and Stabilwax column (DA), with hydrogen as a carrier at a flow rate of 2 mL/min. The injector and detector temperatures were maintained at 220 °C. The oven temperature ramp was programmed to increase from 40 to 220 °C at 10 °C/min. The sample was previously filtered using 0.22 μm cellulose filters, and the injected volume was 0.1 mL.

The degree of fermentation was determined for each pH condition and reaction time using the following equation:
(1)

Bioinformatic analysis

The extracted deoxyribonucleic acid (DNA) was temporarily stored at −20 °C and then sent to Macrogen Inc. (South Korea) for sequencing. The 16S ribosomal ribonucleic acid (rRNA) and internal transcribed spacers (ITS) regions were used to characterize bacteria and eukaryotes. The amplicon libraries for 16S rRNA were created and sequenced using Illumina MiSeq (Illumina). The primers for bacteria were 16S (V3-V4) for the Bakt_341F (CCTACGGGNGGCWGCAG) and Bakt_805R (GACTACHVGGGTATCTAATCC) regions. The eukaryotic primers were ITS1 (CTTGGTCATTTAGAGGAAGTAA) and ITS2 (GCTGCGTTCTTCATCGATGC) (Alcántara-Hernández et al. 2017; Jiménez-Ocampo et al. 2021; Serrano-Meza et al. 2022). Macrogen Inc. sent the raw sequencing information to be processed for the bioinformatic analysis, which was performed using Qiime2, version 2021.8 (Bolyen et al. 2019). The libraries for the 16S rRNA bacterial genes and ITS were processed separately. For the alignment of the forward, reverse, and contig generation sequences, DADA2 in Qiime 2021.8 was used. Chimeric sequences were removed using the same method (Callahan et al. 2016). Taxonomical assignment of amplicon sequencing variants was performed (Amplicon Sequence Variants), which resulted from the reference database Silva 138 (Quast et al. 2013). Predetermined parameters in Qiime2 2021.8 were used for final purging and figures processing.

OFMSW fermentation and hydrolysis

Figure 1 shows the metabolites produced during OFMSW fermentation along 6 days reaction time. The upper figure shows the metabolites in the liquid fraction and other metabolites in the solid fraction. The liquid fraction was separated using a 0.45 μm membrane filter. Carbohydrates, VSs, fat, oil, and KN were determined. On day zero, the sample from the reactor was taken 5 minutes after starting the operation.
Figure 1

Liquid and solid fractions of the fermentation products. The bars indicate the mean value of four replicas with the standard deviation.

Figure 1

Liquid and solid fractions of the fermentation products. The bars indicate the mean value of four replicas with the standard deviation.

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Carbohydrates

Independent of the adjusted pH, carbohydrates were consumed rapidly during the first operation day, from 12 g/L to approximately 2 g/L on the first day for the liquid fraction (Figure 1). For the liquid fraction, the carbohydrate concentration continued to decrease with time; the higher the pH, the lower the carbohydrate final concentration. Approximately 95% of carbohydrates in the liquid fraction were consumed in 3 days. For the solid fraction, the carbohydrate concentration increased from 260 to approximately 290 g/kgVS during the first day and decreased with time. As OFMSW contains complex substances, increasing carbohydrates indicates the hydrolysis of substances that can later be detected as carbohydrates. As solubilization continued, consumption increased more rapidly for the fermenter under pH 4, reaching 68% removal. For the fermenters at pH 5 and 6, carbohydrate removal was similar, reaching 53 and 54%, respectively.

Research suggests that working at a pH in the range of 4–5.5 increases the solubility of carbohydrates because the acidic medium increases solubility and allows acid to attack more complex carbohydrates (Sawatdeenarunat et al. 2015; Cheng & Brewer 2021; Naik et al. 2021). Furthermore, in research on fermentations from biomass mainly from food waste, OFMSW, vegetables, and fruits, which are made up of the three groups of macromolecules (carbohydrates, proteins, and lipids), several authors affirm that the first group to be degraded and in more significant proportion is carbohydrates, achieving percentages between 60 and 90%, then followed by fats and oils, where removals of between 45 and 70% can be observed (Sawatdeenarunat et al. 2015; Naik et al. 2021).

Chemical oxygen demand

For the liquid fraction, the COD values increased during the first days due to the carbohydrate solubilization in the solid fraction (Figure 1). After increasing the first reaction day from 25 to 29 g/L, for pH of 4 and 5, COD in the liquid fraction decreased slightly until the end of the experiment. At pH 6, COD increased during the first 3 days to decrease for another 3 days. Independent of pH, the COD values in the liquid fraction ended with 27 g/L for total COD and 25 or 26 g/L for dissolved COD. As can be observed in Figure 1, except for pH 4 during the first 3 days, the difference between total and dissolved COD remained constant over time.

Fats and oils

For the three pH cases, fats and oils decreased with reaction time, and their removal also increased with pH (Figure 1). In terms of fat and oil at pH 6 and a reaction time of 1 day, 17% removal was achieved; at 3 days, it increased to 62%, and at 6 days, it increased to 68%. For a pH of 5, a 16% removal was achieved. This increased to 45% with 3 days reaction time and 65% removal on day 6. At pH 4, 42% was achieved on day 1, and 58% was achieved on day 6. At pH values of 5 and 6, fats and oils removal increased as time increased. Noteworthy is that the degradation of fats and oils began on day 3.

Kjeldahl nitrogen

The removal of proteins (Kjeldahl nitrogen) for all pH values presented a similar behavior with no significant differences. Theoretically, there is no reason for the removal of total nitrogen, as no metabolite detected in Figure 2 contains nitrogen.
Figure 2

Fermentation products at different pH values and reaction times.

Figure 2

Fermentation products at different pH values and reaction times.

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The last group to be used is proteins, which are very important; they are mainly used for the formation of new microorganisms, that is, anabolic processes (Rajeshwari et al. 2001; Bouallagui et al. 2004; Ganesh et al. 2014; Alibardi & Cossu 2016; Cheng & Brewer 2021).

Metabolites production

Table 1 shows multiple reactions that can occur in a fermentation system, along with their respective ΔG values. Negative values indicate that the reaction is spontaneous, while positive values mean that microorganisms need to expend energy for the reaction to proceed. The reactions begin with glucose because, as shown in Figure 1, a high consumption of carbohydrates was observed during the fermentation process.

Table 1

Reactions in a fermentation process with naturally occurring microorganisms (Azbar et al. 2001; Cavalcante et al. 2017)

ReactionPathwayStoichiometryΔG (kJ/mol)
Fermentation    
Glucose to acetic acid Glucose + 4H2O → 2 acetic acid + 2CO2 + 2H2 −206 
Glucose to ethanol Glucose + 2H2O → 2 ethanol + 2CO2 −226 
Glucose to lactic acid Glucose → lactic acid −198.3 
Glucose butyric acid Glucose + 2H2O → Butyric acid + 2CO2 + 2H2 −255 
Acetogenesis    
Ethanol to acetic acid Ethanol + H2O → acetic acid + H+ + 2H2 + 19 
Lactic acid to acetic acid Lactic acid + H2O → acetic acid + 2H2 + CO2 −8.4 
Butyric acid to acetic acid Butyric acid + 2H2O → 2 acetic acid + 2CO2 + H2 + 48 
Chain elongation reaction    
Ethanol to n-butyric acid elongation Ethanol + acetic acid → n-butyric acid + H2−38.62 
Ethanol to n-caproic acid elongation Ethanol + n-butyric acid → n-caproic acid + H2−38.74 
10 Overall n-caproic acid generation 12 ethanol + 3 acetic acid −→ 5n-caproic acid + 4H2 + 8H2−30.55 
11 Lactic acid to n-butyric acid elongation Lactic acid + acetic acid → n-butyric acid + H2O + CO2 −57.65 
12  2 lactic acid + H2O → n-butyric acid + CO2 −56 
13 Lactic acid to n-caproic acid elongation Lactic acid + n-butyric acid + H +→ n-caproic acid + H2O + CO2 −57.65 
14 Overall n-caproic acid generation 15 lactic acid → 5n-caproic acid + 5CO2 + 10H2 + 5H2−41.32 
ReactionPathwayStoichiometryΔG (kJ/mol)
Fermentation    
Glucose to acetic acid Glucose + 4H2O → 2 acetic acid + 2CO2 + 2H2 −206 
Glucose to ethanol Glucose + 2H2O → 2 ethanol + 2CO2 −226 
Glucose to lactic acid Glucose → lactic acid −198.3 
Glucose butyric acid Glucose + 2H2O → Butyric acid + 2CO2 + 2H2 −255 
Acetogenesis    
Ethanol to acetic acid Ethanol + H2O → acetic acid + H+ + 2H2 + 19 
Lactic acid to acetic acid Lactic acid + H2O → acetic acid + 2H2 + CO2 −8.4 
Butyric acid to acetic acid Butyric acid + 2H2O → 2 acetic acid + 2CO2 + H2 + 48 
Chain elongation reaction    
Ethanol to n-butyric acid elongation Ethanol + acetic acid → n-butyric acid + H2−38.62 
Ethanol to n-caproic acid elongation Ethanol + n-butyric acid → n-caproic acid + H2−38.74 
10 Overall n-caproic acid generation 12 ethanol + 3 acetic acid −→ 5n-caproic acid + 4H2 + 8H2−30.55 
11 Lactic acid to n-butyric acid elongation Lactic acid + acetic acid → n-butyric acid + H2O + CO2 −57.65 
12  2 lactic acid + H2O → n-butyric acid + CO2 −56 
13 Lactic acid to n-caproic acid elongation Lactic acid + n-butyric acid + H +→ n-caproic acid + H2O + CO2 −57.65 
14 Overall n-caproic acid generation 15 lactic acid → 5n-caproic acid + 5CO2 + 10H2 + 5H2−41.32 

Figure 2 shows the metabolite production for the three tested pH values. This also indicates dissolved COD and the sum of all metabolites. The bars compare the differences between soluble COD and the sum of the concentrations of all detected metabolites expressed as COD. The initial COD was similar for every pH run, and the maximum value was reached on day three for pH 4 and 6 and on day 2 for pH 5.

OFMSW fermentation at pH 4

Raw OFMSW shows lower VFAs and alcohol concentrations (day zero). At pH 4, the production of metabolites increased with time, reaching values of approximately 70% (fermentation rate) of dissolved COD. After day 3, the ratio of metabolites to COD did not change. OFMSW fermentation at pH 4 was selective to ethanol and acetic acid during the first reaction days, and acetic fermentation predominated (reactions 1, 2, and 3 in Table 1). Lactic acid concentration was maximal on day 3, decreasing with reaction time. The highest ethanol concentration was recorded on day 1 and then decreased from 6 to 2 g/L until day 6; acetogenesis consumes ethanol as the reaction time increases. On reaction day 1, the ethanol concentration is like the one at pH 5. Acetic acid increased steadily until day 6 as ethanol and lactic acid underwent acetogenesis. Acetogenesis of ethanol and lactic acid is evident as acetic acid rapidly increases (reactions 5 and 6 in Table 1). Although there is also another pathway for lactic acid consumption, the elongation reaction for the production of caproic acid from lactic acid as the only reactant (reaction 14), this reaction presents a negative Gibbs energy value. In Figure 2, the blue bars represent the total sum of all identified metabolites, while the yellow bars indicate the soluble COD. There is a difference between them, especially on day 1.

Increasing soluble COD shows the hydrolysis and solubilization of the substrate, and the difference between them indicates the presence of unidentified substances in the media. After 3 days, the fermentation rate was 67% of the 33% of unidentified substances.

On day 1, ethanol presented a similar concentration at the three tested pH values. The acetogenesis of ethanol at pH 4 and 6 was observed from day 3 and decreased 25–30% compared to day 1.

OFMSW fermentation at pH 5

At this pH, the fermentation was selective to ethanol and lactic acid. Higher lactic acid concentrations were observed on the first day (Figure 2), increasing until day 3 and then decreasing slowly to day 6 due to acetogenesis: As lactic acid concentration decreased, acetic acid increased significantly. Jojoa-Unigarro & González-Martínez (2021) reported the same behavior when fermenting OFMSW under different conditions. Another pathway could be the elongation reaction in which butyric acid is generated from lactic acid (reaction 12 in Table 1). From days 3–6, butyric acid increased from zero to 8 g/L, coinciding with decreasing lactic acid concentration (Figure 2). Ethanol concentration increased until day 3 and remained unchanged until day 6, indicating that the acetogenesis of ethanol was minimal at pH 5, which is contrary to the behavior of the ethanol concentration at pH 4, where acetogenesis of ethanol is evident. Traces of methanol could be detected on day 2.

The difference between the sum of the identified metabolites and soluble COD is less than for the fermentation under pH 4. The degree of fermentation increased with time, achieving a degree of fermentation of 97% at day 6.

OFMSW fermentation at pH 6

OFMSW fermentation at pH 6 resulted in a high formation of acetic acid on day 1 (reaction 1 in Table 1), decreased on day 3, and remained unchanged. Ethanol was produced on day 1 (reaction 2 in Table 1), and then the concentration decreased slowly until day 6 due to acetogenesis and/or the preferential production of other metabolites, such as butyric acid (reaction 8 in Table 1). Very small amounts of lactic acid were produced under pH 6. Notorious is the sudden production of butyric acid under pH 6: It increased rapidly until day 3, then decreased from 17.5 to 15 gCOD/L on day 6. This production was caused by direct fermentation (reaction 4 in Table 1) or an elongation reaction from ethanol and acetic acid (reaction 8 in Table 1).

As shown in Figure 2, the sum of metabolites is similar to the soluble COD, indicating a reasonable fermentation rate of almost 100% on day 6 at pH 6. Wu et al. (2015) worked with food waste and evaluated the selectivity in the fermentation when switching a semi-continuous fermenter from pH 5 to pH 7; at pH 5 the selectivity is mainly acetic with traces of propionic and butyric acids. When the pH was changed to 6, a butyric acid peak was observed, reaching 60% of the total metabolites; the remaining were mainly acetic acid. When the pH was adjusted to 7, acetic and butyric acids continued to dominate, and 15% was propionic acid. Wu et al. (2015) did not consider lactic acid or ethanol. Wu et al. (2017) fermented food waste at pH 6 for 24 days in a batch reactor; ethanol was produced on the first day and disappeared on day 5 as butyric acid decreased. Acetic increased together with propionic and valeric acids.

Zheng et al. (2015) analyzed waste fermentation from fruits and vegetables at pH 4, 5, and 6 for 48 h at 35 °C. They did not analyze for lactic acid and concluded that at pH 4, 95% of the fermentation formed ethanol and the rest, acetic acid. At pH 5, the fermentation started as ethanolic, and then, because of acetogenesis from ethanol, acetic acid increased, as did propionic and butyric acids. At pH 6, ethanolic and acetic fermentations predominated during the first 24 h, allowing further butyric acid fermentation. This research shows predominant butyric acid fermentation at pH 6 and after 3 days of fermentation. Jojoa-Unigarro & González-Martínez (2021) conclude that decreasing ethanol and lactic acid concentrations at lower pH is caused by acetogenesis and that, at pH values between 5 and 6, acetogenesis contributes to butyric acid accumulation.

Tang et al. (2017) evaluated the effect of pH (4, 5, and 6) on the fermentation at mesophilic conditions of food waste for 12 days. They did not consider ethanol and concluded that butyric fermentation dominates over lactic fermentation at pH 6. The highest lactic acid production occurred at pH 5 and remains constant. However, under the conditions of this research, at pH 5, lactic acid is consumed. Tang et al. (2017) conclude that at pH 4, lactic fermentation is not fast, but from day 4 onwards, lactic acid production increases, surpassing acetic fermentations from day 8 onwards. They also concluded that the most adequate pH value to increase lactic acid generation is 5.

Studies by Hafid et al. (2016), Yan et al. (2011), and Zhang et al. (2005) on vegetable, fruit, and kitchen waste for ethanol and VFAs production, using VS concentrations between 5 and 8% in fermentation reactors and evaluating pH as a primary variable, found that the highest removal of TSs and carbohydrates takes place within a pH range of 5.5–6.5. Hafid et al. (2016) observed that at pH levels close to 4, carbohydrate consumption was low during the 8 days of fermentation compared to higher pH levels.

Analysis of microbial communities

Figure 3 shows the relative abundance of predominant bacteria in raw OFMSW, unfermented OFMSW, and the digestate product of fermentation. The samples were collected on day 6. Four important phyla, Firmicutes, Proteobacteria, Actinobacteriota, and Bacteroidota, are observed for raw OFMSW and the different digestates. In general, it is observed for both phyla, classes, and genera that by decreasing the pH in fermentation from 6 to 5 and to 4, there is a decrease in taxonomic diversity, reaching pH 4, only two dominant genera remain belonging to two different phyla, Firmicutes and Proteobacteria.
Figure 3

Relative abundances of bacteria in raw and fermented OFMSW at different pH values. Samples were collected at the end of every test on the 6th day.

Figure 3

Relative abundances of bacteria in raw and fermented OFMSW at different pH values. Samples were collected at the end of every test on the 6th day.

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Phylum

Firmicutes achieved the highest relative sequence abundances (RSA) in OFMSW when fermented at pH 6 and 5, achieving 85% at pH 5 and presenting a significant drop at pH 4 with 32% RSA. Proteobacteria showed a decrease in its RSA when fermenting OFMSW at pH of 6 and 5, but at pH 4 it became the dominant phylum with an RSA of 68%. Actinobacteriota was present at pH 6 (14%) and pH 5 (5%) while Bacteroidota was the only phylum present in OFMSW (4%) and at pH 6 (17%).

Class

The Alphaproteobacteria class associated with Acetobacter had less than 1% presence of RSA at pH 6 and 5 and in OFMSW. However, at pH 4, the abundance of skyrockets was 48%. For pH 5 and 6, the microorganisms responsible for acetogenesis can be associated with the classes Clostridia with 42% of RSA at pH 5 and Gammaproteobacteria, Actinobacteria, and Bacteroidia with RSA of 21, 14, and 19%, respectively. At pH 6, it can be inferred from the metabolism of each of these classes that they may participate in acidogenesis and acetogenesis (Bergey's Manual® 2010).

Genus

Lactobacillus, which belongs to the Bacilli class, in terms of RSA from highest to lowest, was present in the digestates fermented at pH 5 with 43%, at pH 4 at 30%, at pH 6 at 19%, and finally with 7% in OFMSW.

The same behavior can be observed with the production of lactic acid (see Figure 2): The highest metabolite production was observed with the digestate fermented at pH 5, then at pH 4, and finally at pH 6. De Groof et al. (2021) worked with selective fermentation from food waste, varying the organic load and hydraulic retention time; all the experimentation was carried out at pH 4. The sample was taken after 60 days of operation, and the RSA for Lactobacillus was between 20 and 45%. However, Wu et al. (2016) analyzed anaerobic digestion in two stages; the first fermentative stage worked in the pH range of 4.6–4.8, and after 38 days of operation, the fermenter reached an RSA of 90%. In research using Lactobacillus species as inoculum to produce lactic acid from food waste, Dreschke et al. (2015) and Ohkouchi & Inoue (2006) determined that the optimal pH range for the greatest production of lactic acid is between 4.5 and 5.5.

Facultative Acetobacter is mainly associated with the transformation of ethanol to acetic acid under aerobic and anaerobic conditions; in the presence of fruit, vegetable, or food waste, low abundances have been observed (RSA lower than 2%). Other authors have observed RSA in the 20–45% range when fermenting fruits, vegetables, or food waste (Papalexandratou et al. 2013; Higashiura et al. 2014; Nguyen et al. 2022). A relationship was found between the production of metabolites in fermentation at pH 4 (Figure 2) and the presence of Acetobacter. At pH 4 the highest concentration of acetic acid occurred after a reaction time of 1 day, which implies that Acetobacter is responsible for the acetogenesis of ethanol and lactic acid generated on the first day of fermentation.

Raw OFMSW

The origin and heterogeneity of the substances found in OFMSW make comparing the composition of communities and microbial structure among authors difficult. When comparing phyla, similarities were found, as well as differences in RSA and genera.

Tonanzi et al. (2018) worked with solid wastes from food on a Roman university campus in Italy, and they found phyla such as Actinobacteriota, Firmicutes, Bacteroidetes, and Proteobacteria (from higher to lower abundance). Jiménez-Ocampo et al. (2021) worked with solid waste from a municipal market in the city of Querétaro, Mexico. The most significant phyla were Firmicutes, Proteobacteria, Bacteroidetes, and Sinergistetes (from higher to lower abundance). Figure 4 shows the microbial communities of Eukarya in the raw OFMSW from Mexico City. At 98%, Ascomycota was the predominant phylum in Eukarya in fresh unfermented OFMSW. Saccharomycetes is the most abundant class (74%), and the most abundant genus is Pichia, which produces ethanol under anaerobic conditions. Some Pichia species can also produce methanol. Pichia strains are widely used to produce ethanol from substrates rich in lignocellulosic compounds (Agbogbo & Coward-Kelly 2008; Dubey et al. 2012; Demiray et al. 2018). The second most abundant genus is Candida (9%), which is known for its high hydrolytic capacity and its ability to produce alcohols, acetate, and pyruvate (Klinke et al. 2009).
Figure 4

Composition and structure of fungal community in raw, unfermented OFMSW.

Figure 4

Composition and structure of fungal community in raw, unfermented OFMSW.

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Thirteen percent of the sequences could not be classified to levels higher than the phylum level; however, the results showed they belonged to the Ascomycota (see unclassified and Ascomycota, Figure 4).

The 14% remaining unclassified genera were distributed in classes such as Sordariomycetes (4%), Dothideomycetes (4%), and Leotiomycetes (3%). In the characterized raw OFMSW, these classes contained the genera Myrothecium (2%), Ascochyta (3%), and Scleromitrula (3%). The last ones can use substrates different from sugars, and some can be plant pathogens, indicating their ability to hydrolyze and use lignocellulosic compounds as substrates (Baite & Dubey 2018; Fujinawa et al. 2020).

When analyzing the relative abundance of Bacteria and Eukarya in raw, unfermented OFMSW, it can be concluded that OFMSW is rich in microorganisms with efficient hydrolytic capacity (Figures 3 and 4). The microbial community characterization showed that no bacteria were able to produce ethanol during the different pH experiments, indicating that the microorganisms responsible for producing ethanol were yeasts naturally found in the substrate. A significant relative abundance of Pichia was observed in the raw unfermented OFMSW. De Groof et al. (2021) also worked with selective fermentation from food waste, varying the organic load and solids retention time (SRT) at pH 4. They concluded that Lactobacillus was present in all samples, with abundances ranging between 20 and 45%. These results differ from those in the review by Hafid et al. (2016), which indicated that organisms from the genus Zymomonas cause ethanol production. Zymomonas was not detected in this research (Figure 3).

  • At pH 4, substrate solubilization (fermentation rate) was lower than at higher pH values. At pH 6, the total metabolites represented 97% of the soluble COD.

  • Ethanol and lactic acid were preferentially produced at pH 5. Butyric acid was produced only at pH 6.

  • As pH increased to 6, the diversity of the microbial community increased. The taxonomic diversity of bacteria decreased drastically at lower pH values

  • Bacteria genera capable of producing VFAs and lactic acid and of performing acetogenesis were detected,

  • Bacteria responsible for VFAs and lactic acid production could be found under all three tested pH values, and they could also perform acetogenesis.

  • Bacteria capable of acetogenesis are responsible for decreasing lactic acid and ethanol production as the reaction time increases.

  • Relative abundance of Lactobacillus decreased at pH 6, where lactic acid production is almost absent.

Financing was provided by the General Directorate for Academic Affairs (DGAPA) at the National University of Mexico (UNAM), PAPIIT IT101320. The authors thank CONAHCYT for the doctoral scholarship. The authors acknowledge Dr Francisco Rojo-Calleja for the analytical support at the Chemistry School at UNAM.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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