Influence of aging methods on the volatile aroma compounds of tobacco leaves and the structure of surface microbial communities

doi:10.21203/rs.3.rs-8055318/v1

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Influence of aging methods on the volatile aroma compounds of tobacco leaves and the structure of surface microbial communities

https://doi.org/10.21203/rs.3.rs-8055318/v1

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Tobacco aging is a critical process for developing desirable flavor profiles, which is largely driven by microbial and enzymatic activities. This study systematically investigated the effects of bacterial inoculation (Bacillus clausii) and bacterial-enzyme co-treatment (with cellulase) on the surface microbial communities and flavor compounds of Yunyan 87 tobacco leaves during a 9-month aging period. High-throughput 16S rRNA sequencing revealed that the microbial-enzyme co-treatment (JM) significantly enhanced microbial diversity and community stability compared to the control (CK) and bacterial-only (FJ) treatments. PICRUSt functional prediction indicated a significant enrichment in metabolic pathways, particularly carbohydrate metabolism, amino acid metabolism, and metabolism of cofactors and vitamins. Gas chromatography–mass spectrometry (GC–MS) analysis identified 29 key aroma compounds, demonstrating that the JM treatment effectively promoted the accumulation of esters and ketones—such as ethyl palmitate, 4,7,9-megastigmatrien-3-one, and damascenone—which contribute desirable fruity, floral, and sweet notes. Correlation analysis further linked dominant bacterial genera (e.g., Pseudomonas, Bacillus, Acinetobacter) with the formation of these characteristic volatiles. These findings demonstrate that microbial-enzyme co-fermentation is a promising strategy to accelerate the aging process and improve the flavor quality of tobacco, offering significant potential for industrial application.

Tobacco aging

Microbial community

Volatile compounds

Microbial-enzyme co-fermentation

Tobacco is among the most extensively cultivated commercial crops worldwide. China ranks as one of the largest producers, with over 300 million smokers—accounting for nearly one-quarter of the global smoking population. The country contributes approximately 35% of global tobacco production and 32% of total sales (Shan et al. 2025). The quality of tobacco leaves is largely influenced by the climatic and environmental conditions in the growing regions (Hu et al. 2022). Moreover, post-harvest processes such as aging and fermentation, in conjunction with microbial activity, play essential roles in shaping the distinctive flavor profile of tobacco (Li et al. 2020).

During the aging process, intricate biochemical and microbial interactions occur, leading to the degradation of undesirable compounds and the formation of desirable aroma substances (Li et al. 2020). The transformation of macromolecules including starch, proteins, and cellulose, into low-molecular-weight flavor precursors is largely driven by microbial metabolism and enzymatic catalysis (Mai et al. 2025). Traditional natural aging relies on indigenous microorganisms and ambient environmental conditions, often resulting in extended processing times and inconsistent product quality (Liu et al. 2021; Zheng et al. 2022). In recent years, the exogenous addition of microorganisms and enzymes has emerged as an effective strategy to accelerate aging and improve flavor quality (Pei et al. 2025; Zhang et al. 2023). Among these, Bacillus species are widely recognized for their robust enzymatic capabilities, including the production of cellulase, amylase, and protease, which facilitate the breakdown of structural and storage compounds in tobacco leaves (Wei et al. 2024). Likewise, the use of commercial enzymes can target specific substrates, resulting in more efficient and controlled modification of tobacco composition (Hao et al. 2025). However, the synergistic effects of combined microbial-enzyme treatments on microbial community dynamics and associated metabolic functions during tobacco aging remain insufficiently.

The surface microbiota of tobacco leaves play a decisive role in determining the outcomes of the aging process and the final product quality (Wang et al. 2022; Wang et al. 2024b). Previous studies have demonstrated that microbial succession during fermentation is likely linked to the formation of key volatile compounds, such as esters, ketones, and aldehydes, which contribute to the characteristic aroma of aged tobacco (Pei et al. 2025). High-throughput sequencing technologies, particularly 16S rRNA gene amplicon sequencing, have enabled comprehensive profiling of microbial community structures and functional potentials (Di Bella et al. 2013). It has been reported that exogenous inoculation can significantly alter the abundance and diversity of dominant genera, including Bacillus, Acinetobacter, Staphylococcus, and Aspergillus, thereby modulating metabolic pathways related to starch and sucrose metabolism, amino sugar and nucleotide sugar metabolism, glycolysis/gluconeogenesis and mycolic acid biosynthesis (Yin et al. 2025). Moreover, microbial metabolism directly influences the conversion of flavor precursors, such as carotenoids, fatty acids, and amino acids, into odor-active compounds. For instance, Bacillus species are known to enhance the degradation of polysaccharides and proteins, leading to increased production of Maillard reaction products and Strecker aldehydes (Jiang et al. 2024; Zhu et al. 2025). Similarly, microbially mediated carotenoid degradation contributes to the generation of alcohols, ketones, and esters (Mai et al. 2024). Despite these advances, the temporal dynamics of microbial communities under different aging treatments, particularly those involving enzymatic supplementation, remain largely unexplored. The aroma profile of aged tobacco is a key determinant of product quality, consisting of a complex mixture of volatile organic compounds formed through diverse biochemical pathways. Gas chromatography–mass spectrometry (GC–MS) has been extensively used to characterize these volatiles and evaluate their contributions to sensory attributes. Among the major odor-active components are ketones, esters, and alcohols, each imparting distinct aromatic attributes. For example, ketones such as megastigmatrienones and damascones impart sweet and fruity notes, while esters including ethyl palmitate and ethyl linoleate contribute creamy and floral characteristics (Wang et al. 2024a). The evolution of these compounds during aging is influenced by microbial activity, enzymatic reactions, and environmental factors. However, the interactions between microbial community structure and volatile compound formation have not been quantitatively elucidated. Furthermore, although microbial and enzymatic treatments have individually demonstrated potential in enhancing tobacco quality, their combined effects remain underexplored.

Therefore, this study investigated the effects of bacterial and bacterial-enzyme co-fermentation on tobacco leaves. By integrating high-throughput sequencing with GC–MS, the effects of aging methods on the surface microbial community structure and the resulting flavor profile were analyzed. Furthermore, correlations between the microbial community and key aroma compounds were elucidated. This work will provide a theoretical insight and practical guidance for the industrial application of microbial-enzyme co-fermentation technology to achieve consistent and high-quality tobacco aging.

Materials and chemicals

Yunyan 87 tobacco leaves, utilized prior to the aging process, were provided by Guangxi Tobacco Industry Co., Ltd. B. clausii was isolated and screened by the Tobacco Biotechnology Research Laboratory of Zhengzhou University of Light Industry. The enzymatic agent, cellulase (50 U/mg, solid formulation), was sourced from Shanghai Yuanye Biotechnology Co., Ltd. Analytical grade dichloromethane was provided by Tianjin Fuyu Fine Chemical Co., Ltd. The internal standard phenyl ethyl acetate was purchased from Beijing Bailingwei Technology Co., Ltd. And all the chemicals used in this study were analytical grade.

Preparation of strain

The B. clausii strain, previously isolated and screened from the surface of tobacco leaves and preserved on LB agar slant medium, was inoculated onto LB solid medium using an inoculation loop under a laminar flow hood. The plates were subsequently incubated in a constant-temperature incubator at 32°C for a duration of 24 h.

Suspension preparation: Two loops of B. clausii from the solid medium were inoculated into LB liquid medium and cultured at 32°C and 180 r/min for 48 h. The resulting seed culture was centrifuged at 6000 r/min for 10 min, after which the supernatant was discarded. The pellet was resuspended in sterile water, and the optical density was measured using a UV spectrophotometer. The suspension was diluted to an OD₆₀₀ of 1.45, corresponding to a bacterial concentration of 10⁸ CFU/mL. This bacterial suspension was set aside for further use.

Bacterial-enzyme mixture preparation: 1 g of cellulase (with a dosage of 50 U per gram of tobacco leaves) was accurately weighed and dissolved in 80 mL of the prepared bacterial suspension. The mixture was stirred uniformly to form the bacterial-enzyme mixture.

Pre-treatment of tobacco leaves before aging

The prepared bacterial suspension and bacterial-enzyme mixture were evenly sprayed onto the surface of the tobacco leaves at a dosage of 80 mL per 1000 g of tobacco. Sterile water of the same volume was used for the control treatment. After spraying, the treated tobacco leaves were transferred to the aging warehouse and subjected to natural fermentation at 37°C and 50% relative humidity.

Tobacco samples sprayed with sterile water and aged for 3, 6, and 9 months were labeled as CK-3, CK-6, and CK-9, respectively. Samples treated with the bacterial suspension and aged for the same periods were designated as FJ-3, FJ-6, and FJ-9. Likewise, samples treated with the bacterial-enzyme mixture and aged for 3, 6, and 9 months were denoted as JM-3, JM-6, and JM-9. At each aging stage (3, 6, and 9 months), 500 g of tobacco leaves were uniformly collected from each treatment group for subsequent analyses.

DNA extraction, amplification and sequencing of microbiota

The nine samples subjected to different aging methods were aseptically cut into fine pieces. Total microbial DNA was extracted from the tobacco leaves following the instructions of the E.Z.N.A.™ Mag-Bind Soil DNA Kit according to a previously published method with minor modifications (Zhang et al. 2020). Using the total DNA of the aforementioned samples as the template, the first round of PCR amplification was performed. Bacterial 16S rRNA sequences were amplified using the primers 5′-CCT ACG GRR BGC ASC AGK VRV GAA/T3′ and 5′-GGA CTA CNV GGG TWT CTA ATC C-3′. The reaction mixture consisted of: 10 ng of DNA template, 15 µL of 2× Hieff® Robust PCR Master Mix, 1 µL of Bar-PCR primer F, 1 µL of Primer R, and ddH₂O added to a final volume of 30 µL. The amplification protocol was as follows: initial denaturation at 94°C for 3 min; 25 cycles of denaturation at 94°C for 30 s, annealing at 55°C for 20 s, and extension at 72°C for 30 s; followed by a final extension at 72°C for 5 min. A second round of PCR amplification was conducted using the products from the first PCR as templates, with the introduction of Illumina bridge-PCR compatible primers. The reaction mixture and thermal cycling conditions were identical to those used in the first round. The PCR products were examined by electrophoresis on a 2% (w/v) agarose gel. The targeted 16S rDNA gene fragments were purified using a multifunctional DNA purification and recovery kit. The purified PCR products were subsequently sent to Sangon Biotech (Shanghai) Co., Ltd. for bacterial 16S rDNA sequencing on the MiSeq high-throughput sequencing platform.

The raw sequences were initially processed using Cutadapt to remove primer and adapter sequences. Paired-end reads were then assembled into single sequences based on their overlap regions. Subsequently, the sequences were demultiplexed and assigned to corresponding samples according to their barcode tags. The processed sequences for each sample were further refined using Prinseq to obtain high-quality effective sequences. The sequences were clustered into operational taxonomic units (OTUs) using a 97% identity cutoff. Subsequently, chimeric sequences were identified and filtered out from the dataset. The representative sequences of OTUs were taxonomically annotated. Microbial community diversity indices for the 15 tobacco leaf samples were calculated using Mothur version 1.31.2. Principal coordinates analysis (PCoA) was performed to examine differences in OTU distribution among tobacco samples subjected to different aging methods. The relative abundances of microbial communities at the phylum and genus levels across all samples were visualized and analyzed using Origin 2022. To predict the functional profiles of the microbial communities in different tobacco samples, PICRUSt was employed. The accuracy of functional prediction was evaluated using the weighted nearest sequenced taxon index (Weighted NSTI), which represents the weighted average distance between the sequences in each sample and the reference genomes in the database.

Detection of volatile compounds

The content of aroma components in tobacco leaves was analyzed using GC-MS coupled with simultaneous distillation–extraction (SDE) for sample preparation as previously described method (Wu et al. 2025). Tobacco leaf samples were dried at 60°C and ground into a fine powder using a 60-mesh sieve. Exactly 25.00 g of the powdered sample was placed into a 1000 mL round-bottom flask, mixed with 20 g of NaCl and 400 mL of deionized water, and shaken thoroughly. The mixture was then connected to one end of the SDE apparatus and heated in a 60°C water bath for 150 min. The extraction solvent was dichloromethane. After the extraction was completed, 50 µL of 0.871 mg/mL phenyl ethyl acetate solution was added as the internal standard. After thorough shaking and mixing, anhydrous sodium sulfate was added for drying treatment. Subsequently, the sample was concentrated to approximately 1 mL, filtered through a 0.45 µm membrane, and subjected to GC-MS analysis.

GC-MS was performed using an Agilent 7890A-5975C system equipped with an HP-5MS capillary column (30 m × 0.25 mm × 0.25 µm; Agilent 19091S-433). The oven temperature program was set as follows: initial temperature 60°C, ramped at 2°C/min to 260°C and held for 10 min, then increased to 280°C at 5°C/min. The MS ion source and quadrupole temperatures were maintained at 230°C and 150°C, respectively, with a mass scan range of 30–550 m/z. Compound identification was carried out using the NIST20 mass spectral library, and data processing was performed with Agilent MassHunter Workstation software. The concentration of volatile compound was calculated using the following equation:

$$\:\text{Content:}\text{(}\text{μg}\text{/g)}\text{=}{C}_{i}\:\times\:\:{v}_{i}\:\times\:{\:A}_{s}/{m}_{0}\:\times\:\:{A}_{i}$$

Where$\:\:{C}_{i}$ is the concentration of internal standard, mg/mL; $\:{v}_{i}$ is the volume of internal standard, µL; $\:{A}_{s}$ and $\:{\:A}_{i}$ are peak area of volatile compound and the internal standard, respectively; $\:{m}_{0}$ refers to the quantity of the sample, g.

The odor activity value (OAV) is determined by dividing the concentration of volatile compound by its corresponding odor threshold (Wu et al. 2025).

Statistical analysis

All experiments were conducted in triplicate, and the results were expressed as mean ± standard deviation (SD). Statistical analysis was performed using a one-way analysis of variance (ANOVA), followed by Duncan’s significance test using SPSS 19.0, with statistical significance defined as p < 0.05. Bar charts were generated using Origin 2021 (OriginLab, Northampton, MA, USA). Partial least squares discriminant analysis (PLS-DA) was carried out with SIMCA 14.1 (Umetrics, Umea, Sweden). Principal coordinate analysis (PCoA) and Spearman correlation coefficients were performed using the Chiplot online tool (https://www.chiplot.online). Correlation networks were conducted with Cytoscape (v. 3.10.1).

Microbiological analysis in tobacco leaves with different aging treatments

Overview of the microbial community

The diversity of bacterial communities was evaluated by high-throughput sequencing of the 16S rRNA variable region. As shown in Fig. S1, rarefaction curves approached stabilization with increasing sequencing depth, indicating that the majority of microbial diversity within the samples had been captured. The coverage indices were all higher than 99.8%, further confirming that the sequencing effort was sufficient to represent the bacterial diversity and support reliable taxonomic classification. After filtering out singletons, a total of 81103–94247, 78697–92448, and 70182–87463 high-quality bacterial sequences in CK, FJ, and JM samples, respectively, were retained for subsequent analysis (Table S1). These sequences were clustered into operational taxonomic units (OTUs) at a 97% similarity threshold.

α- and β-diversity

To evaluate the richness and diversity of microbial communities during tobacco aging, α-diversity indices—including Ace, Chao, Shannon, and Simpson—were analyzed (Ruan et al. 2021). The Ace and Chao indices reflect species richness, whereas the Shannon and Simpson indices indicate species diversity and evenness. As shown in Fig. 1, the Ace and Chao indices revealed significantly higher bacterial richness in JM samples compared to FJ samples at 3–6 months of fermentation. However, this trend reversed at 9 months (Fig. 1A and B). The Shannon index was significantly higher in JM samples, while the Simpson index was higher in FJ samples, suggesting that the JM group supported a more diverse and evenly distributed microbial community (Fig. 1C and D). Throughout the fermentation process, the richness of FJ samples gradually increased, whereas JM samples showed minor change. Microbial diversity and evenness increased consistently in FJ samples but followed an initial increase followed by a decrease in JM samples. These results indicated that the richness and diversity of microbial communities varied with the aging time of tobacco leaves. Additionally, the application of a bacterial suspension and enzyme mixture significantly enhanced both richness and diversity compared to natural aging alone, with the exception of the three-month time point. These findings align with previous studies demonstrating that exogenous microbial inoculation can substantially reshape the microbial community structure in tobacco leaves (Shu et al. 2023).

β-diversity was assessed to characterize structural differences among microbial communities from different sample groups. Principal coordinate analysis (PCoA) employing the weighted Unifrac distance was employed to visualize the temporal dynamics of microbial composition during tobacco leaf aging (Fig. 1E–G). The first two principal coordinates (PCo1 and PCo2) accounted for 82.71%, 85.81%, and 91.83% of the total variance in CK, FJ, and JM treatments, respectively. Significant separation among aging stages along the y-axis indicated considerable temporal succession in community structure. Concurrently, a decline in Bray–Curtis dissimilarity suggested increasing community homogeneity and enhanced structural stability during mid to late fermentation. These findings were consistent with prior research on tobacco fermentation (Mai et al. 2025), which reported high initial community dispersion indicative of early ecological instability, followed by increased overlap in later stages, reflecting progressive stabilization. Together, these results demonstrated that the application of exogenous enzymes not only promotes microbial diversity but also contributes to the stabilization of the community structure.

Dynamic changes in microbial communities

The relative abundance of the microbial community in tobacco leaves with different aging treatments was analyzed at phylum and genus levels (Fig. 2). At the phylum level (Fig. 2A and C), Pseudomonadota consistently represented the most abundant phylum across all treatments (CK, FJ, JM), followed by Bacillota. Together, these two phyla consistently comprised over 80% of the total bacterial community. At identical aging stages, compared with the control group, the relative abundance of Pseudomonadota in the FJ and JM treatment groups decreased, while the relative abundance of Bacillota in the FJ treatment group increased, the relative abundances of Bacteroidota and Cyanobacteria in the JM treatment group significantly increased. These results indicated that the treatment of the bacterial enzyme mixture promoted the proliferation of Cyanobacteria and Bacteroidetes while maintaining substantial presence of Bacillus and Pseudomonadota, thereby significantly enhancing the diversity of the microbial community. During the aging of tobacco leaves treated with sterile water (CK), the relative abundance of Actinomycetota and Pseudomonadota gradually increased over time, whereas that of Cyanobacteriota and Bacillota declined. Across the 3-, 6-, and 9-month aging intervals, the combined relative abundance of Pseudomonadota and Bacillota reached 90.80%, 96.37%, and 96.98%, respectively, indicating their dominance within the microbiota. Other bacterial phyla were present at comparatively low abundances. For FJ treatment, extended aging led to a progressive increase in Bacteroidota and a reduction in Bacillota. Pseudomonadota and Bacillota together constituted 92.28%, 95.19%, and 90.79% of the community at 3, 6, and 9 months, respectively, confirming their continued predominance, with minor phyla representing only a small fraction. For JM treatment, the relative abundance of Pseudomonadota and Actinomycetota rose with aging time, while Bacillota decreased. The combined share of Pseudomonadota and Bacillota accounted for 83.91%, 90.36%, and 88.49% at each respective time point, reinforcing their role as the two most dominant phyla, with other taxa remaining relatively scarce.

At the genus level (Fig. 2B and D), the predominant bacterial communities in tobacco leaves varied significantly among treatments. In the CK treatment, the dominant genera were Franconibacter, Pseudomonas, and Enterobacter, with relative abundances of 29.11–39.29%, 22.50–31.88%, and 5.40–14.16%, respectively. In contrast, the FJ treatment was dominated by Franconibacter, Bacillus, Enterobacter, and Pseudomonas (relative abundances: 13.29–37.63%, 19.78–37.41%, 10.09–18.13%, and 4.61–19.22%), while the JM treatment exhibited a distinct profile dominated by Bacillus, Pseudomonas, Enterobacter, and Franconibacter (relative abundances: 24.25–27.30%, 15.00–18.16%, 9.46–13.12%, and 4.33–10.99%). These compositional differences indicated that the application of bacterial/enzymatic treatments significantly altered the microbial community structure and enhanced microbial diversity. Over the aging period, temporal shifts in genus abundance were also observed. In the CK group, the relative abundance of Pseudomonas and Sphingomonas gradually increased, while that of Enterobacter decreased. Notably, the combined abundance of Pseudomonas and Franconibacter accounted for 61.79%, 70.78%, and 60.99% of the microbiota at 3, 6, and 9 months, respectively, indicating their dominance throughout the aging process under control conditions. In the FJ treatment, genera such as Sphingomonas, Acinetobacter, Stenotrophomonas, Methylobacterium, Sphingobium, and Novosphingobium showed increasing trends over time, while Franconibacter declined. The sum of the relative abundances of Pseudomonas, Franconibacter, and Bacillus reached 69.17%, 73.26%, and 53.48% at 3, 6, and 9 months, respectively, highlighting their roles as the core dominant taxa in this treatment. Similarly, in the JM-treated leaves, the relative abundance of Acinetobacter and Novosphingobium increased with aging time, whereas Franconibacter decreased. The combined relative abundance of Pseudomonas and Bacillus constituted 45.46%, 39.25%, and 42.05% of the community at 3, 6, and 9 months, confirming their dominance among the microbial population in this treatment group.

Biomarker microorganisms in different aging treatments

Linear discriminant analysis (LDA) effect size (LEfSe) is an effective approach for identifying taxa that exhibit statistically significant and biologically meaningful differences across groups, particularly during dynamic process such as fermentation (Zhang et al. 2025c). This approach provides valuable insights into microbial succession and their potential associations with final product quality. In this study, LEfSe was employed to identify differentially abundant bacterial taxa in the CK, FJ, and JM groups across three aging time points (3, 6, and 9 months). The evolutionary cladograms (Fig. 3A–C) illustrate the phylogenetic distribution of identified biomarkers from phylum to species level, radiating from the inner to outer circles. Complementary biomarker (LDA > 4.0, p < 0.05) distributions for each treatment and aging points were shown in supplementary Fig. S2A–C. Specifically, in the CK treatments (Fig. 3A), the microbial composition at 3 months was characterized by biomarkers such as Franconibacter and Enterobacter. By 6 months, the community shifted towards taxa within Enterobacterales and Priestia. At 9 months, a further succession was observed, with significant enrichment of Pseudomonas, Pantoea, Acinetobacter, and Stenotrophomonas. In the FJ treatments (Fig. 3B), only Enterobacterales were significantly enriched at 3 months. By 9 months, biomarker diversity increased substantially, encompassing Pseudomonas, Enterobacter, Acinetobacter, Sphingomonas, and Stenotrophomonas. Compared to the FJ treatments, the JM treatments exhibited a more balanced successional pattern across aging periods (Fig. 3C). At 3 months, key biomarkers included Enterobacterales, Bacillus, and Pantoea. By 6 months, the community simplified, with only Pseudomonas and Enterobacter serving as predominant biomarkers. By 9 months, the biomarker profile broadened again to include Cyanobacteriia, Sphingomonas, and Stenotrophomonas. These results indicated that both aging treatment method and duration collectively shape the successional dynamics of microbial communities. This temporal progression of characteristic microorganisms is closely correlated with divergence in the flavor quality of aged tobacco leaves.

Microbial function prediction analysis

High-throughput sequencing data were aligned against the KEGG database, and functional potentials of the microbial communities in tobacco leaves were predicted using PICRUSt. As shown in Fig. 4A, six major categories were identified at KEGG Level 1 across all aging treatments, including Cellular Processes, Environmental Information Processing, Genetic Information Processing, Metabolism, Organismal Systems, and Human Diseases. Among these, metabolic pathways were predominant, accounting for 78.22% to 79.57% of the predicted functional profiles. Significant temporal shifts in metabolic functions were observed among the microbial communities. Under the same aging duration, the JM treatment exhibited higher metabolic activity compared to the CK and FJ treatments. Furthermore, within the JM treatment, the relative abundance of genes associated with metabolic functions progressively increased with extended aging time. These results indicated that metabolic function played significant role in tobacco leaves during aging.

Further classification within metabolism-related pathways identified a total of 11 metabolic pathways at KEGG Level 2 (Fig. 4B). Among these, carbohydrate metabolism pathway was the most abundant, accounting for 16.72–20.60%, followed by metabolism of cofactors and vitamins and amino acid metabolism, which accounting for 16.49–17.69% and 14.48%–16.67%, respectively. These results suggested that carbohydrate metabolism, metabolism of cofactors and vitamins, and amino acid and metabolism pathways were dominant during aging and likely contribute substantially to flavor compound formation of tobacco leaves. These findings align with previous studies on cigar, which also reported high relative abundances of carbohydrate metabolism, amino acid metabolism, energy metabolism, cofactor and vitamin metabolism, and membrane transport among the most active functional pathways (Pan et al. 2025). In carbohydrate metabolism (Fig. 4C), 15 pathways were detected. Branched dibasic acid metabolism accounted for the highest proportion (11.35–13.14%), followed by pentose phosphate pathway (9.02–10.96%) and pyruvate metabolism pathway (8.65–10.20%). In metabolism of cofactors and vitamins (Fig. 4D), a total of 12 metabolic pathways were identified. Lipoic acid metabolism was the most prominent (10.50–13.67%), followed by Pantothenate and CoA biosynthesis (10.77–11.84%) and biotin metabolism (8.74–11.24%).

Changes of volatile compounds in tobacco leaves with different aging treatments

A total of 326 volatile compounds were detected by HS-SPME-GC-MS, among which 29 substances were the main aromatic compounds of aged tobacco leaves, including 10 esters, 4 alcohols, 5 aldehydes, 7 ketones, and 3 others (Table 1). As shown in Fig. 5A, ketones consistently constituted the most abundant class of volatiles in CK and FJ treatments, followed by esters and alcohols. While in JM treatment, esters were the most abundant flavor compounds, followed by ketones and alcohols. Among all the treatments, aldehydes accounted for less than 7.5% of the total. As aging progressed, the relative abundance of ketones and esters continued to rise, while that of alcohols declined. This compositional shift implied that microbial-enzyme co-treatment facilitated the gradual conversion of long-chain alcohols into ketones and esters compounds, which were known for their low odor thresholds and desirable aromatic properties, thus improving the quality of tobacco. Consequently, these transformations likely contributed to the development of a more intense and persistent fruity and sweet aroma in the tobacco leaves (Zhang et al. 2025a). Figure 5B showed that the total volatile content in tobacco leaves varied considerably among the three aging treatments (p < 0.05). The control group (CK) exhibited volatile compound levels ranging from 124.74 to 142.95 µg/g. In contrast, the FJ treatmets showed a notable increase, with total volatile contents reaching 283.21–300.78 µg/g, while the bacterial-enzyme co-treatment (JM) further enhanced accumulation, yielding concentrations between 317.06–388.80 µg/g. These results suggested that exogenous microbial inoculation promotes the conversion of flavor precursors, and that the microbial-enzyme co-treatment exerted a compounded effect on the synthesis of volatile compounds.

Table 1
Contents and odor activity values of the main aromatic compounds in tobacco leaves with different aging treatments
	Compound	Flavor ^a	Content(µg/g)									Odor threshold (µg/g)	Odor activity value
	Compound	Flavor ^a	CK-3	CK-6	CK-9	FJ-3	FJ-6	FJ-9	JM-3	JM-6	JM-9	Odor threshold (µg/g)	CK-3	CK-6	CK-9	FJ-3	FJ-6	FJ-9	JM-3	JM-6	JM-9
Esters	Dihydroactinidiolide	Sweet, creamy	2.18 ± 0.06d	0.02 ± 0.01i	0.3 ± 0.01h	1.97 ± 0.04e	1.36 ± 0.02f	2.86 ± 0.04b	2.61 ± 0.06c	0.91 ± 0.07g	5.42 ± 0.19a	0.5	4.36	<1	0.60	3.94	2.72	5.72	5.22	1.82	10.84
	Ethyl myristate	Ester aroma, fruity	-	0.57 ± 0.04g	0.47 ± 0.04g	6.08 ± 0.03b	2.68 ± 0.13f	3.24 ± 0.08e	8.96 ± 0.08a	5.74 ± 0.1c	4.4 ± 0.11d	20	-	<1	<1	<1	<1	<1	<1	<1	<1
	Methyl palmitate	Iris scent	-	-	-	5.06 ± 0.04d	-	14.61 ± 0.1b	9.38 ± 0.33c	-	22.09 ± 0.4a	4000	-	-	-	<1	-	<1	<1	-	<1
	Ethyl palmitate	Buttery	-	12.81 ± 0.12h	15.69 ± 0.15g	35.32 ± 0.92f	45.47 ± 0.65e	49.02 ± 0.33d	54.37 ± 1.28c	89.81 ± 0.96a	73.45 ± 0.72b	1.5	-	8.54	10.46	23.55	30.31	32.68	36.25	59.87	48.97
	Methyl linolenate	Melon flavor	-	2.91 ± 0.07f	6.83 ± 0.05d	-	7.27 ± 0.06d	8.65 ± 0.14c	-	18.03 ± 0.17a	9.47 ± 0.16b	/	-	-	-	-	-	-	-	-	-
	Ethyl linoleate	Fruity, floral	-	2.94 ± 0.08g	5.55 ± 0.08f	14.62 ± 0.3e	15.97 ± 0.47d	14.27 ± 0.32e	27.23 ± 0.29b	36.65 ± 0.77a	21.41 ± 0.36c	2.8	-	1.05	1.98	5.22	5.70	5.10	9.73	13.09	7.65
	Ethyl linolenate	Waxy	-	5.14 ± 0.07g	9.04 ± 0.13f	21.18 ± 0.45e	27.43 ± 0.41d	28.03 ± 0.17d	34.49 ± 0.66c	51.91 ± 0.81a	35.92 ± 0.76b	/	-	-	-	-	-	-	-	-	-
	Ethyl stearate	Wax aroma	-	0.41 ± 0.02h	0.76 ± 0.04g	5.29 ± 0.06c	2.96 ± 0.07f	4.47 ± 0.09e	9.46 ± 0.05a	6.59 ± 0.06b	5.07 ± 0.03d	18	-	<1	<1	<1	<1	<1	<1	<1	<1
	Dioctyl phthalate	Apricot flavor	-	-	3.07 ± 0.04d	5.4 ± 0.03c	0.17 ± 0.03e	0.52 ± 0.03e	13.69 ± 0.21b	-	18.42 ± 0.46a	/	-	-	-	-	-	-	-	-	-
	Methyl linoleate	Milky	-	0.85 ± 0.02e	-	16.13 ± 0.2b	21.93 ± 0.88a	-	9.14 ± 0.08c	7.43 ± 0.15d	-	/	-	-	-	-	-	-	-	-	-
Alcohols	Benzyl alcohol	Floral and fruity	12.44 ± 0.09d	7.38 ± 0.04f	2.92 ± 0.06g	15.12 ± 0.4b	13.45 ± 0.09c	10.17 ± 0.29e	17.64 ± 0.18a	2.55 ± 0.11g	12.82 ± 0.18d	10	1.24	<1	<1	1.51	1.35	1.02	1.76	0.26	1.28
	Linalool	Floral, sweet	2.54 ± 0.08d	4.19 ± 0.06b	0.32 ± 0.02f	3.38 ± 0.07c	3.23 ± 0.23c	2.59 ± 0.05d	3.93 ± 0.06b	5.58 ± 0.28a	0.62 ± 0.06e	0.037	68.65	113.24	8.65	91.35	87.30	70.00	106.22	150.81	16.76
	Phenethyl alcohol	Rose fragrance	9.35 ± 0.05e	-	3.89 ± 0.05f	20.27 ± 0.51b	20.03 ± 0.58b	-	25.1 ± 0.54a	17.94 ± 0.33c	14.96 ± 0.1d	31	<1	-	<1	<1	<1	-	<1	<1	<1
	Furfuryl alcohol	Sweet aroma and caramel	1.14 ± 0.06c	-	0.26 ± 0.02d	3.39 ± 0.02b	-	-	4.3 ± 0.07a	-	-	0.07	16.29	-	3.71	48.43	-	-	61.43	-	-
Aldehydes	3-Furfural	Almond-like aroma	-	-	-	-	-	8.53 ± 0.26b	-	-	14.71 ± 0.13a	/	-	-	-	-	-	-	-	-	-
	Benzaldehyde	Almond flavor	0.93 ± 0.02e	-	0.11 ± 0.01f	2.34 ± 0.07b	0.05 ± 0.03f	1.37 ± 0.04d	2.14 ± 0.06c	0.01 ± 0f	3.25 ± 0.11a	0.35	2.66	-	<1	6.69	<1	3.91	6.11	<1	9.29
	Phenylacetaldehyde	Fruity, nutty	6.87 ± 0.04b	4.35 ± 0.07d	-	-	-	4.76 ± 0.07c	-	-	8.44 ± 0.18a	1.8	3.82	2.42	-	-	-	2.64	-	-	4.69
	β-Cyclocitral	Floral and fruity fragrance	0.82 ± 0.02c	0.42 ± 0.02e	-	0.67 ± 0.06d	0.3 ± 0.02f	1.03 ± 0.03b	0.97 ± 0.08b	0.47 ± 0.03e	1.26 ± 0.04a	0.5	1.64	0.84	-	1.34	<1	2.06	1.94	<1	2.52
	Furfural	Caramel aroma	-	-	-	-	5.11 ± 0.08b	-	-	11.87 ± 0.04a	-	11.36	-	-	-	-	<1	-	-	1.04	-
Ketones	Damascenone	Honey and floral notes	18.68 ± 0.12d	21.42 ± 0.03c	22.78 ± 0.25b	18.36 ± 0.68d	22.32 ± 0.56b	22.63 ± 0.19b	-	13.41 ± 0.18e	25.39 ± 0.63a	50	<1	<1	<1	<1	<1	<1	-	<1	<1
	Irisone	Floral and sweet	-	-	-	-	-	3.18 ± 0.06c	5.16 ± 0.06b	7.49 ± 0.19a	5.1 ± 0.02b	7	-	-	-	-	-	<1	<1	1.07	<1
	4,7,9-Megastigmatrien-3-one	Sweet aroma of licorice	55.09 ± 0.26f	50.73 ± 0.19g	60.82 ± 0.35e	87.88 ± 1.05b	87.4 ± 0.83b	86.53 ± 0.21b	65.71 ± 0.81d	68.21 ± 0.49c	92.42 ± 0.66a	/	-	-	-	-	-	-	-	-	-
	Phytone	Herbal scent	-	3.33 ± 0.09d	2.52 ± 0.1e	2.49 ± 0.04e	2.61 ± 0.08e	3.95 ± 0.1c	-	5.62 ± 0.26b	6.36 ± 0.12a	/	-	-	-	-	-	-	-	-	-
	Geranylacetone	Floral and fruity aroma	5.09 ± 0.06e	7.48 ± 0.04a	5.63 ± 0.08d	4.99 ± 0.04e	6.29 ± 0.17c	7.42 ± 0.11a	6.53 ± 0.22b	6.13 ± 0.09c	-	0.06	84.83	124.67	93.83	83.17	104.83	123.67	108.83	102.17	-
	α-Damascenone	Rose fragrance	-	3.58 ± 0.02c	0.41 ± 0.02d	-	7.44 ± 0.31a	0.55 ± 0.02d	-	4.96 ± 0.08b	-	0.013	-	275.38	31.54	-	572.31	42.31	-	381.54	-
	β-Ionone	Violet scent	-	3.04 ± 0.05c	0.69 ± 0.02d	3.95 ± 0.07b	4.6 ± 0.06a	-	-	-	-	0.0009	-	3377.78	766.67	4388.89	5111.11	-	-	-	-
Others	2-Methoxy-4-vinylphenol	Cedar and roasted peanut notes	8.38 ± 0.06c	-	0.89 ± 0.05e	10.26 ± 0.23b	2.71 ± 0.05d	-	15.38 ± 0.58a	8.45 ± 0.1c	-	/	-	-	-	-	-	-	-	-	-
	2-Acetylpyrrole	Nutty aroma	-	-	-	-	-	3.48 ± 0.03b	-	-	7.82 ± 0.16a	0.002	-	-	-	-	-	1740.00	-	-	3910.00
	2-Pentylfuran	Green bean and fruity notes	1.23 ± 0.09b	-	-	0.64 ± 0.04d	-	1.35 ± 0.02a	0.87 ± 0.02c	-	-	0.002	615.00	-	-	320.00	-	675.00	435.00	-	-
^a Fragrance descriptions were obtained from the publicly available flavor database www.vcf-online.nl/VcfHome.cfm.
^b Odor thresholds were referenced from the online database www.vcf-online.nl/VcfHome.cfm and the compilation by (Van Gemert 2011).

Figure 5C showed the heat map generated by studying the relative content of identified main aromatic compounds and their relationship with the properties of different aging methods of tobacco leaves. A total of 29 aromatic compounds were found in all the tobacco leaves, but they varied depending on the aging methods and aging time-point. The difference of flavor substances in different aging stages of tobacco leaves might be caused by the flavor precursor catabolic reactions (Zhu et al. 2025).

Volatile ketones, which arise from pathways such as β-oxidation and degradation of fatty acids, oxidative cleavage of carotenoids, and Maillard reactions, represented the most abundant class of volatile compounds in the analyzed samples and were characterized by their relatively low sensory thresholds (Wang et al. 2024b; Wu et al. 2023). The total ketone content in FJ-treated samples exceeded that of both CK and JM groups across all fermentation periods. Among the ketones identified, damascenone, 4,7,9-megastigmatrien-3-one, geranylacetone were the most abundant in all three tobacco types. Their concentrations increased progressively with aging in both FJ and JM samples, promoting the development of an ideal floral and fruity aroma in the tobacco leaves. These findings are consistent with previous studies reporting similar trends in other tobacco varieties (Wu et al. 2022). Shan et al reported that the content of megastigmatrienone significantly increased in Bacillus velezensis TB-1 fermented low-grade tobacco leaves (Shan et al. 2025).

Esters in tobacco leaves were primarily formed through esterification reactions between short-chain acids and alcohols (Zhang et al. 2024b). The predominant volatile esters identified in aged tobacco leaves include methyl palmitate (35.32–89.81 µg/g), ethyl linoleate (21.18–51.91 µg/g), and ethyl linoleate (14.62–36.65 µg/g), which contribute pleasant fruity and floral notes and significantly enhance the overall aroma and flavor profile of tobacco (Wang et al. 2024a). Compared to the CK treatment, both the diversity and concentration of esters were elevated in the FJ and JM treatments. This increase may be attributed to the action of cellulase, which degraded tobacco cell walls and released additional precursors such as sugars and phenolic compounds. Concurrently, B. clausii likely facilitated the conversion of these precursors into ester compounds via its oxidative metabolic pathways.

Alcohols in tobacco leaves originate primarily from the enzymatic oxidation and breakdown of polyunsaturated fatty acids, as well as from the secondary degradation of fatty acid hydroperoxides or microbial fermentation of carbohydrates (Weng et al. 2024). Under all three aging methods, the total alcohol concentration exhibited a gradual decline as fermentation progressed. Although alcohols generally contributed pleasant aromas and are known to mellow the smoke and reduce irritation (Zhang et al. 2025b), their overall abundance decreased over time. Similar result was also found in tobacco leaves fermented by Bacillus velezensis TB-1, which reported that the content of 1,3-dioxolane-2-methanol and benzyl alcohol decreased after Bacillus velezensis TB-1 (Shan et al. 2025). These findings align with previously reported trends in fermented tobacco leaves and further demonstrated that exogenous microbial-enzyme co-fermentation enhances ester formation, thereby enriching the desirable floral and fruity aroma notes in tobacco leaves (Zhang et al. 2025b). Notably, the concentrations of specific key alcohols varied significantly among treatments. The content of benzyl alcohol increased from 12.44 µg/g (CK) to 15.12 µg/g (FJ) and 17.64 µg/g (JM) for aging 3 months. A more pronounced increase was observed for furfuryl alcohol, which rose from 1.14 µg/g (CK) to 3.39 µg/g (FJ) and 4.30 µg/g (JM). Benzyl alcohol, derived from the conversion of phenylalanine, serves as an important odor-active compound in tobacco, contributing distinct aromatic characteristics to cigarette smoke (Weng et al. 2024). Furfuryl alcohol imparted a sweet, caramel-like scent that significantly enriched the overall aroma (Liu et al. 2024). Furthermore, it played an effective role in reducing smoke irritation and masking undesirable odors in the tobacco leaves.

A total of only five volatile aldehydes were identified across the three types of aged tobacco leaves. Aldehydes—mainly derived from lipid oxidation—are known for their low odor thresholds and play a significant role in shaping the aroma profile of tobacco leaves (Fang et al. 2024). As shown in Fig. 5B and 5C, the total aldehyde content under the JM treatment was significantly higher than that in both the CK and FJ treatments at each aging time point. Moreover, aldehyde levels increased progressively throughout the fermentation period, suggesting that microbial-enzyme co-fermentation promoted the degradation and conversion of lipid, leading to increased aldehyde formation (Li et al. 2024). The five aldehydes detected during aging included 3-furaldehyde, benzaldehyde, phenylacetaldehyde, β-cyclocitral, and furfural. In all treatments, aldehydes collectively accounted for less than 7.5% of the total quantified aroma compounds. Their concentrations increased linearly over fermentation time in both FJ and JM samples. After nine months of aging, three aldehydes—3-furfural, phenylacetaldehyde, and β-cyclocitral—were uniquely generated in the FJ and JM treatments, with concentrations consistently higher in the JM-9 group than in FJ-9. In contrast, benzaldehyde was detected in all three treatments; its content was highest in JM (3.25 µg/g), followed by FJ (1.37 µg/g) and CK (0.11 µg/g). These aldehydes contribute floral, fruity, and nutty notes, substantially enriching the complexity and layering of the overall tobacco aroma.

The odor activity values (OAVs) of key aroma components in tobacco leaves subjected to different treatments are summarized in Table 1. The results indicated that the JM treatment exhibited significantly higher OAVs for dihydroactinidiolide and ethyl palmitate—both associated with milky notes—compared to the CK and FJ treatments. With OAVs exceeding 1, these compounds contributed to a more pronounced milky aroma in the JM group. Similarly, the JM treatment showed elevated OAVs for several floral- and fruity-scented compounds, including ethyl linoleate, linalool, benzaldehyde, β-ionone, benzyl alcohol, and β-cyclocitral. Among these, the OAVs of ethyl linoleate, linalool, and benzaldehyde were greater than 1, enhancing the fruity aroma profile of the JM treatment. Additionally, furfuryl alcohol, which imparted a caramel-like sweetness, displayed an OAV above 1 in the JM treatment, underscoring its role in enriching the sweet aromatic notes. Notably, benzaldehyde and 2-acetylpyrrole (nutty aromas) also exhibited higher OAVs in the JM treatment. Owing to its particularly low odor threshold, 2-acetylpyrrole yielded the highest OAV among all detected aroma compounds. Collectively, these substances, including ethyl linoleate, benzyl alcohol, linalool, benzaldehyde, phenylacetaldehyde, β-cyclocitral, β-ionone, and 2-acetylpyrrole, contributed significantly to the overall aroma profile of the JM treatment. With the exception of benzaldehyde, β-cyclocitral, and β-ionone, all exhibited OAVs greater than 1, identifying them as key aroma-active compounds following tobacco fermentation.

Characteristic volatile compounds in tobacco leaves with different aging treatments

A partial least squares-discriminant analysis (PLS-DA) model was applied to analyze the volatile compounds in tobacco leaves subjected to different aging treatments, as depicted in Fig. 6. The model demonstrated high reliability, with both the variable fit index (R²) and the predictive ability index (Q²) exceeding 0.5. Variables with a variable importance in projection (VIP) > 1 and p < 0.05 were considered as differential volatile flavor compounds (Wang et al. 2024a). When comparing the FJ group to the CK group, key differential compounds included ethyl palmitate, 4,7,9-megastigmatrien-3-one, phenethyl alcohol, methyl linoleate, ethyl linolenate, benzyl alcohol, and methyl palmitate. In the JM group versus CK, the main differential volatiles were ethyl palmitate, ethyl linolenate, 4,7,9-megastigmatrien-3-one, ethyl linoleate, dioctyl phthalate, and damascenone. The results indicated that these markers primarily belonged to esters, alcohols, and ketones. Most of these compounds contribute pleasant aromatic attributes and play essential roles in harmonizing, refining, and distinguishing the overall aroma profile of tobacco leaves under different aging conditions.

Correlation analysis between dominant microorganisms and characteristic volatile compounds

To elucidate the role of dominant microbiota in flavor formation, which is likely mediated by their high metabolic potential, this study investigated the correlations between the top ten bacterial genera and characteristic aroma components using Spearman correlation analysis and visualized using correlation networks (Fig. 7). Results showed that Pseudomonas exhibited correlations with three aroma compounds, showing positive correlations with furfuryl alcohol and 2-methoxy-4-vinylphenol, but a negative correlation with α-damascenone, while Stenotrophomonas was positively correlated with 3-furfural, 2-acetylpyrrole, and methyl linolenate. Previous study has implicated Pseudomonas and Stenotrophomonas in the formation of tobacco flavor, showing a significant positive correlation with compounds such as acetophenone, decyl aldehyde, and β-cyclonitroaldehyde during industrial fermentation, thereby highlighting their potential contribution to the sensory profile (Zhang et al. 2024a).

Franconibacter was exclusively negatively correlated with methyl linolenate, 3-furfural, and 2-acetylpyrrole. In contrast, Acinetobacter demonstrated the broadest influence, displaying positive correlations with five components, including methyl linolenate, ethyl palmitate, ethyl linolenate, phytone, and β-ionone. Enterobacter was strongly negatively correlated solely with dioctyl phthalate, Bacillus showed a single positive correlation with methyl linolenate. Similarly, Solibacillus was identified as a characteristic microorganism during the air-curing process, with correlation analysis indicating a significant positive relationship between its abundance and the formation of carbonyl compounds such as 3,5-octadien-2-one, geranyl acetone, and 2,3-pentanedione (Zhang et al. 2025a). These results preliminarily elucidate the potential contribution of bacterial communities to flavor compound formation during tobacco leaf aging. However, this study has certain limitations. However, due to the amplification preference of the primers selected for amplicon sequencing for different species, this method is difficult to accurately reflect the true composition and structure of the microbial community, which may affect the accurate inference of community functions (Cocolin et al. 2013). Moreover, the lack of full genomic information limits an in-depth exploration of the metabolic pathways involved. To further systematically explain the specific mechanisms of microorganisms in the flavor formation of tobacco leaves, future studies should employ metagenomic or metatranscriptomic technologies to enable direct analysis based on functional genes. Furthermore, isolating key microbial strains and validating their functions through laboratory and industrial-scale fermentation experiments will be essential to confirm their roles.

This study systematically evaluated the impact of bacterial and bacterial-enzyme co-treatments on the microbial community structure and flavor profile of tobacco leaves during aging. High-throughput sequencing demonstrated that the application of B. clausii combined with cellulase (JM treatment) significantly enhanced microbial diversity and evenness, promoted the proliferation of beneficial phyla such as Bacteroidota and Cyanobacteria, and stabilized community structure over time. Functional prediction via PICRUSt highlighted the dominance of metabolic pathways—especially carbohydrate metabolism, amino acid metabolism, and metabolism of cofactors and vitamins—which are closely associated with the formation of key flavor compounds. Volatile compound analysis revealed that the JM treatment notably increased the total content of aroma substances, particularly esters and ketones, while facilitating the conversion of alcohols into more desirable aromatic compounds. Key differential volatiles, including ethyl palmitate, ethyl linolenate, megastigmatrienone, and damascenone, were identified as characteristic markers of the JM treatment, contributing to enhanced fruity, floral, and milky notes. Odor activity value (OAV) analysis further confirmed the sensory significance of compounds such as dihydroactinidiolide, ethyl linoleate, linalool, and 2-acetylpyrrole in the JM group. Correlation network analysis elucidated the relationships between dominant microbial genera and aroma components. Acinetobacter, Pseudomonas, and Bacillus were positively correlated with multiple key volatiles, underscoring their roles in flavor formation. However, the limitations of 16S amplicon sequencing and the lack of genomic data necessitate further validation through metagenomic or metatranscriptomic approaches, coupled with isolation and functional characterization of key strains. In conclusion, microbial-enzyme co-fermentation not only enhances the microbial community structure, but also facilitates the production of desirable aromatics, leading to an enhancement of aged tobacco quality. This study establishes a theoretical foundation and offers a practical framework for the industrial application of directed fermentation, paving the way for standardized production of high-quality aged tobacco.

Conflict of 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.

Ethical Approval

This article does not contain any studies involving human participants or animals conducted by the authors.

Consent for Publication

All authors consent to publication in the current microbiology.

Funding

This work is funded by Major Science and Technology Project of China National Tobacco Corporation [110202201005(JY-05)] and Major Science and Technology Project of Henan Province (231100310200).

Acknowledgments

The authors are grateful to the School of Tobacco Science and Engineering, Zhengzhou University of Light Industry for providing an excellent platform that facilitated the experiments and the writing of this article.

Data availability

Data will be made available on request.

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Influence of aging methods on the volatile aroma compounds of tobacco leaves and the structure of surface microbial communities

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Abstract

Figures

Introduction

Materials and methods

Materials and chemicals

Preparation of strain

Pre-treatment of tobacco leaves before aging

DNA extraction, amplification and sequencing of microbiota

Detection of volatile compounds

Statistical analysis

Results and discussion

Microbiological analysis in tobacco leaves with different aging treatments

Microbial function prediction analysis

Changes of volatile compounds in tobacco leaves with different aging treatments

Characteristic volatile compounds in tobacco leaves with different aging treatments

Correlation analysis between dominant microorganisms and characteristic volatile compounds

Conclusion

Declarations

Funding

Acknowledgments

Data availability

References

Supplementary Files

Status:

Version 1