www.nature.com/scientificreports SCIENTIFIC REPRTS OPEN Exploring flavour-producing core microbiota in multispecies solid- state fermentation of traditional Received:17 December 2015 Accepted:18 March 2016 Chinese vinegar Published:31 May 2016 Zong-Min Wang,Zhen-Ming Lu2,",Jin-Song Shi&Zheng-Hong Xu2.3 Multispecies solid-state fermentation(MSSF),a natural fermentation process driven by reproducible between microbiota and flavours and flavour-producing core microbiota are still poorly understood Here,acetic acid fermentation(AAF)of Zhenjiang aromatic vinegar was taken as a typical case of MSSF.The structural and functional dynamics of microbiota during AAFprocess was determined by metagenomics and favour analyses.The dominant bacteria and fungi were identified Lactobacillus,Aspergillus,and Alternaria,respectively.Total 88 flavours including 2 sugars,9 organic acids,18 amino acids,and 59 volatile flavours were detected during AAF process.O2PLS-based correlatio nalysis be tween microbiota s ssion and flavours dynamicssho wed bacteria made more contribution to flavour formation than fungi.Seven genera including Acetobacter,Lactobacillus, Enhydrobacter,Lactococcus,Gluconacetobacer,Bacillus and Staphylococcus were determined as functional core microbiotafor production of flavours,based on their dominance and.Thisstudy provides a perspective for bridging the gap between the phenotype and genotype of ecological system,and advances our understanding of MSSF mechanisms in Zhenjiang aromatic vinegar. Multispecies solid-state fermentation(MSSF),is defined as a fermentation process in which multiple microorgan isms grow on solid-state materials without present of free liquid.It might be one of the oldest and most econom- ical ways of producing and preserving foods.It has been proved MSSF may improve the nutritional value,taste, smell,and healthy function of raw materials-2.This traditional fermentation method is maintained through a spontaneous mixed-culture refreshment process without sterilisation.Enhanced by repeated practices for years, specific microbiota have been well characterised and their potential in food industry has been exploited inten- tionally3-5.It can be concluded the success of MSSF could rely on the reproducible formation of well-balanced microbiota,which determines the safety,smell,taste,texture,and aroma of fermented foods. With the develop are incr studies to investigate food fermentation. focu namics of the n 2 matio the of he microbialcom- ota and the fun commu microbio ta an core microbes from high species community,taking into account oth dominance and unctionality o can e and mfomane t Bidirectiona orthogonal partial least squares(O2PLS)method is an efficient statistic approach to integrate data collected 无卡P9二XOP5oh今 ogical Syste ute of Indu s and R ology,Chines jiangnan.edu.cn) SCIENTIFIC REPORTS |6:2681 D0:10.1038/srep26818
Scientific Reports | 6:26818 | DOI: 10.1038/srep26818 1 www.nature.com/scientificreports Exploring flavour-producing core microbiota in multispecies solidstate fermentation of traditional Chinese vinegar Zong-MinWang1,* , Zhen-Ming Lu1,2,* , Jin-Song Shi1,3 & Zheng-HongXu1,2,3 Multispecies solid-state fermentation (MSSF), a natural fermentation process driven by reproducible microbiota, is an important technique to produce traditional fermented foods. Flavours, skeleton of fermented foods, was mostly produced by microbiota in food ecosystem. However, the association between microbiota and flavours and flavour-producing core microbiota are still poorly understood. Here, acetic acid fermentation (AAF) of Zhenjiang aromatic vinegar was taken as a typical case of MSSF. The structural and functional dynamics of microbiota during AAF process was determined by metagenomics and favour analyses. The dominant bacteria and fungi were identified as Acetobacter, Lactobacillus, Aspergillus, and Alternaria, respectively. Total 88 flavours including 2 sugars, 9 organic acids, 18 amino acids, and 59 volatile flavours were detected during AAF process. O2PLS-based correlation analysis between microbiota succession and flavours dynamics showed bacteria made more contribution to flavour formation than fungi. Seven genera including Acetobacter, Lactobacillus, Enhydrobacter, Lactococcus, Gluconacetobacer, Bacillus and Staphylococcus were determined as functional core microbiota for production of flavours in Zhenjiang aromatic vinegar, based on their dominance and functionality in microbial community. This study provides a perspective for bridging the gap between the phenotype and genotype of ecological system, and advances our understanding of MSSF mechanisms in Zhenjiang aromatic vinegar. Multispecies solid-state fermentation (MSSF), is defined as a fermentation process in which multiple microorganisms grow on solid-state materials without present of free liquid. It might be one of the oldest and most economical ways of producing and preserving foods. It has been proved MSSF may improve the nutritional value, taste, smell, and healthy function of raw materials1–2. This traditional fermentation method is maintained through a spontaneous mixed-culture refreshment process without sterilisation. Enhanced by repeated practices for years, specific microbiota have been well characterised and their potential in food industry has been exploited intentionally3–5. It can be concluded the success of MSSF could rely on the reproducible formation of well-balanced microbiota, which determines the safety, smell, taste, texture, and aroma of fermented foods. With the development of ecological techniques there are increasing studies to investigate food fermentation, focusing on the patterns/dynamics of the multi-species microbiota4–7 and the functionality of the microbial community6,8–10. These studies provide crucial information to help understand the role of microbiota and the function of the community in fermented foods. However, due to the complexity of MSSF and the lack of data mining strategy, the correlation between microbiota and flavours is still not clear11. Moreover, how to pick indicative functional core microbes from high species community, taking into account both dominance and functionality, is still challenging. Along with the advance of next generation sequencing, the principal research burdens are transforming from traditional wet-lab experiments to dealing with huge and informative data12. Bidirectional orthogonal partial least squares (O2PLS) method is an efficient statistic approach to integrate data collected 1 School of Pharmaceutical Science, Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China. 2Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China. 3 National Engineering Research Centre of Solid-State Brewing, Luzhou 646000, China. * These authors contributed equally to this work. Correspondence and requests for materials should be addressed to Z.-H.X. (email: zhenghxu@ jiangnan.edu.cn) received: 17 December 2015 accepted: 18 March 2016 Published: 31 May 2016 OPEN
www.nature.com/scientificreports/ Figure 1.Distribu of microbiota in vi gar Pei and bioma s of the bacteria and fungiin different AAF PrO analysis of bacteria and fungi in vine natic vinegar,a well-k mn traditional fermented vine roduced by threemajor steps ran. nd chaff as raw mat nd nted cereals from A position.w ts the tastend ectrom -78 and flavour-proc ain to be detern nted vinegar nge,th were d A cted by chro igated b O2PLS.Finally a functi nal cor ated with flavours during A AF process Result netic landscapes and dynamics of microbiota during AAF process. PCR-hased a ity.The age of G cota (fi 2).A total ofpredom and 202f ly st AA a))Therein a).A total of 21 (#v_am)an onFigs3ntcS r1.6 n the first the hio whichnd of played key role in the solid-state AAF Comparison of the in vinegar Peibetween different AAF stages.Thouh Pei,their SCIENTIEIC REPORTS16-268181D0110 1038/4026818
www.nature.com/scientificreports/ Scientific Reports | 6:26818 | DOI: 10.1038/srep26818 2 from different analytical platform and dig into the potential associations between two disparate datasets13. This approach has been applied to investigate the metabolomic and proteomic correlation from mice samples14, the microbes and metabolic phenotype correlation in human gut15, and integrate transcript and metabolite data in plant biology16. However, there were scarce studies to inquire into associations between different omics platforms in fermented foods. Zhenjiang aromatic vinegar, a well-known traditional fermented vinegar, is produced by three major steps including alcohol fermentation, acetic acid fermentation (AAF) and aging. Hereinto, AAF is a typical MSSF process with alcohol mash, wheat bran, and chaff as raw materials and fermented cereals from the last batch of AAF (termed Pei in Chinese, inoculum size, 8%, w/w) as starter17 (Fig. S1b). The succession of microbiota in the Pei during AAF process results in a dynamic flavours composition, which directly affects the taste and aroma of vinegars. Variation of flavours in fermented vinegar has been extensively studied by nuclear magnetic resonance spectroscopy, raman spectroscopy and mass spectrometry18–23. The microbial ecology during AAF process has also been investigated by culture-based and culture-independent approaches4–7. However, the correlation between microbiota and flavours and flavour-producing core microbiota remain to be determined in fermented vinegars. To address this challenge, the assembly and dynamics of microbiota in vinegar Pei during AAF process were characterised by MiSeq sequencing. The changes of flavours composition during AAF were detected by chromatography and analysed by multivariable statistics. Based on these information, the relationship between microbiota assembly and flavours datasets was investigated by O2PLS. Finally, a functional core microbiota was selected by comparison of the comprehensive importance of microbiota correlated with flavours during AAF process. Results Phylogenetic landscapes and dynamics of microbiota during AAF process. PCR-based amplicon sequencing was applied to characterise the microbiota assembly and dynamics in vinegar Pei during AAF process. Across all samples, total 253 and 657 operational taxonomic units (OTUs) were detected for bacteria and fungi respectively with 97% similarity. The average of Good’s coverage was over 0.99 for all samples (Dataset S1), indicating the identified sequences represented majority of microbiota in vinegar Pei. Bacterial assembly were dominated by Firmicutes and Proteobacteria, while the fungi predominantly consisted of the phyla Ascomycota, Fungi_unclassified, and Basidiomycota (Fig. S2). A total of 151 bacterial genera and 202 fungal genera were identified in vinegar Pei during AAF process. As for bacteria, Lactobacillus was predominant in the early stage of AAF (days 0–9), while Acetobacter, Lactococcus, Gluconacetobacter, Enterococcus, and Bacillus were prevailing in the later stage of AAF (days 10–18). Therein Acetobacter could mainly originate from the starter cultures (#v_sp in Fig. 1a), and Lactococcus could mainly originate from alcohol mash (#v_am). Gluconacetobacter, Enterococcus, and Bacillus might originate from the raw materials (#v_mp), which were increasing with the proceeding of AAF (Fig. 1a). As for fungi, Aspergillus was existed in the whole AAF process, which increased in the early 13 days of AAF and then maintained fluctuation in small range (0.4–0.5). Alternaria was dominated in early stage of AAF (days 1–6), and then decreased gradually to the end of AAF. Fungi_unclassified accounted for more than 60% in sample #day_0 but declined rapidly once AAF started, which might originate from the alcohol mash (#v_am) and raw materials (#v_mp) (Fig. 1a). A total of 21 yeast genera were identified in vinegar Pei, including Cryptococcus, Debaryomyces, Candida, Saccharomyces, and so on (Fig. S3). However, these genera only accounted for 1.6% in fungal community. The biomass of bacteria was increasing in the first 7 days, and then decreased till the end of AAF while the biomass of fungi increased in the first 4 days, and then decreased till the end of AAF (Fig. 1b). Moreover, the biomass ratio of bacteria and fungi was in the range of 165 to 13,300, which suggested the bacteria played key role in the solid-state AAF. Comparison of the microbiota structure in vinegar Pei between different AAF stages. Though the dominant genera such as Acetobacter, Lactobacillus and Aspergillus were widely distributed across the vinegar Pei, their abundance within each sample is variable. Principal component analysis (PCA) was applied to compare the microbiota of vinegar Pei in different stages of AAF. It was shown that both bacterial and fungal community Figure 1. Distribution of microbiota in vinegar Pei and biomass of the bacteria and fungi in different samples during AAF process. (a) Average distribution of bacterial and fungal genera in vinegar Pei during AAF process. (b) Average biomass analysis of bacteria and fungi in vinegar Pei during AAF process
www.nature.com/scientificreports/ nd AMOn rity.(b)PCA rity. ()Corre n bet the first principal component(PCI,bacteria and fungi)and ture of the samples on day 0 of AAF exhibited little similarity to other samples excep raw material and varia (bl AF PROT other ty 0051 The s As for fu nga nity it wa y l an tres 0545),but the acidity ( -0.0859). urs detecteddurie 1-9,8 acids (No.10-1 vsis sh d that the first nts r'x(gm) ined 63. anal t S2).The cted co re clearly distinct in OfAAF（fig,36.Hiera sis (HCA) days(belled red.green.a adings demo OAS.I8 AA of AAF (days 8 SCIENTIFICREPORTS6:26818DOl:10.1038/srep26818
www.nature.com/scientificreports/ Scientific Reports | 6:26818 | DOI: 10.1038/srep26818 3 structure of the samples on day 0 of AAF exhibited little similarity to other samples except for raw material samples (#v_am, and #v_mp) (Fig. 2a,b). The samples in early stage were clustered separately from the samples in late stage of AAF based on the assembly and variation of microbiota, which indicated AAF process could be divided into three stages: I, day 0 (red circle in Fig. 2); II, days 1–9 (green box in Fig. 2); and III, days10–18 (blue triangle in Fig. 2). Furthermore, AMOVA showed that the degree of variation (Fs) among all stages was larger than within stages and p-value between any two stages of AAF (I vs. II, I vs. III, and II vs. III) was less than 0.001, suggesting the comparison of the divided three stages during AAF process was statistically significant (Fig. 2a,b). As for bacterial community, metastats analysis revealed a total of 38 OTUs, 21 OTUs, and 52 OTUs in stage I, II and III of AAF were significantly different from other two stages (p< 0.05) respectively. Therein, Pseudomonas, Methylobacterium, Lactobacillus, Sphingomonas, Rhizobium, Staphylococcus, Xanthomonas and Acetobacter were significant different genera in three stages. As for fungal community, it was shown that total 40 OTUs, 29 OTUs and 25 OTUs in stage I, II and III of AAF were significantly different from other two stages (p< 0.05) respectively, where Aspergillus, Verticillium, Rhizomucor, Fungi_unclassified, Pleosporales_unclassified, and Eurotiales_unclassified were significant different genera in three groups. Details of the bacterial and fungal taxonomy classification of the significant OTUs are listed in Tables S1, S2 and Dataset S1. In addition, the acidic stress and alcohol stress were two best predictors of bacterial and fungal community composition, with the principal coordinate one (PC1) being significantly associated with the gradient of titratable acidity and alcohol during AAF process (Fig. 2c, Bacteria: titratable acidity (rho, 0.906), alcohol (rho, −0.901); Fungi: titratable acidity (rho, 0.509), alcohol (rho, −0.545)), but the gradient of temperature was nearly not correlated with the bacterial and fungal community composition (Fig. S4, Bacteria: rho, −0.0974; Fungi: rho, −0.0859). Multivariate analysis of flavours during AAF process. A total of 88 flavours were detected during AAF process, including 2 sugars, 9 organic acids (OAs), 18 amino acids (AAs) and 59 volatile flavours (VFs). The volatile flavours could be divided into seven categories including 9 alcohols (No. 1–9), 8 acids (No. 10–17), 25 esters (No. 18–42), 4 ketones (No. 43–46), 7 aldehydes (No. 47–53), 3 heterocycles (No. 54–56), and 3 others (No. 57–59) (Fig. S5). PCA analysis showed that the first two components R2 X(cum) explained 63.2% of the variables and the cross-validated Q2 -value for each component were more than the cross validation threshold for that component (Limit), indicating significant components for this analysis (Dataset S2). The projected coordinate of metabolites in PC1 appeared to capture the evolutionary tendency of flavours during AAF process, and dynamics of flavours were clearly distinct in different stages of AAF (Fig. 3b). Hierarchical cluster analysis (HCA) revealed the AAF process could be divided into 3 groups based on flavours: group1, day 0; group 2, days 1–7; group 3, days 8–18 (labelled red, green, and blue in Fig. 3a respectively). A biplot integrating scores and loadings demonstrated there were 13 flavours including fructose, glucose, and 11 VFs highly correlated with group 1 (red circle in Fig. 3b); 15 VFs highly correlated with group 2 (green box in Fig. 3b); and 60 flavours including 9 OAs, 18 AAs and 33 VFs highly correlated with group 3 (blue triangle in Fig. 3b). More detailed information is provided in Table S3. These results suggested most of the flavours (OAs, AAs and half of VFs) were produced in the late stages of AAF (days 8–18). Figure 2. Comparison of the structure of microbiota in different samples and correlation between microbiota and environmental factors during AAF process. (a) PCA and AMOVA results of bacterial community in vinegar Pei at different stages of AAF based on hellinger distance with 97% similarity. (b) PCA and AMOVA results of fungal community in vinegar Pei at different stages of AAF based on hellinger distance with 97% similarity. (c) Correlation between the first principal component (PC1, bacteria and fungi) and titratable acidity and alcohol level respectively
www.nature.com/scientificreports/ 品 ciation bet wa 70 S6b)Th n ant by iota 19f in t eria more for vin ar s.Base ta and n righ 11 fungi)were AAs ( .4b 192 avours (56 than funs du nsible for the changef LAAFD I A was po ge nera for cha or AAs.Acetoba Enhydroba ter.Ro (Fi).Glutamic acid (Glu)in (la).valine (Val).and leucine (Leu)are four abundant for the SCIENTIFIC REPORTS 6:26818|DOl:10.1038/srep26818 4
www.nature.com/scientificreports/ Scientific Reports | 6:26818 | DOI: 10.1038/srep26818 4 Association between microbiota and flavours during AAF process. O2PLS method was used to analyse the association between microbiota and flavours during AAF process. It was shown R2 and Q2 of the model was 0.879 and 0.528 respectively (Dataset S2, Fig. S6a), suggesting O2PLS method was well fitted for analysis and prediction. The first two predictive components were significant by cross validation, accounting for 90% of R2 (cum) and 100% of Q2 (cum) in this model (Fig. S6b). The VIP(pred) vector (VIP value for the predictive components) of analysed microbiota varied in 0.15–1.63, in which total 85 microbial genera (VIP(pred)>1.0) including 66 bacterial genera (VIP(pred)≈ 1.03–1.63) and 19 fungal genera (VIP(pred)≈ 1.01–1.46) had important effects on the flavours (Fig. 4a, Dataset S2), suggesting bacteria were more important for vinegar production than fungi. Acetobacter, Lactobacillus, Gluconacetobacter, and Lactococcus were the biggest contributors to the production of flavours during AAF process. Based on correlation coefficient between microbiota and flavours, a total of 94 genera including 61 bacteria (green circles in left side of Fig. 4b) and 33 fungi (yellow circles in left side of Fig. 4b) were moderately and highly correlated (|ρ|>0.7) with all three flavour sets, in which total 47 genera (36 bacteria and 11 fungi) were correlated with OAs (light red circles in right side of Fig. 4b); 59 genera (48 bacteria and 11 fungi) were correlated with AAs (light green circles in right side of Fig. 4b); and 92 genera (61 bacteria and 31 fungi) were correlated with VFs (light blue labels in right side of Fig. 4b). Acetobacter and Lactobacillus possessed the largest number of correlated flavours (56 and 53 respectively), while Aspergillus and Fungi_unclassified were correlated with 39 and 34 of flavours respectively (|ρ|> 0.7) (Table S8). Most of fungal genera (75.7%) had correlated with few flavours (≤5), in which 14 genera had poor correlated with only one flavour. Details of the relationships between the microbiota and flavours are listed in Table S4 and Table S8. For OAs, bacteria played more important role than fungi, in which Lactobacillus, Enhydrobacter, and Gluconacetobacter were important genera for the production of OAs during AAF process. Acetic acid (AA) and lactic acid (LA) were main acids in cereal vinegar. Total 25 genera were correlated with AA (|ρ|> 0.7) (Fig. 4b, Table S5), in which Acetobacter, Enhydrobacter, and Lactobacillus had excellent correlation with AA (|ρ|> 0.9), indicating the three genera were mainly responsible for the change of AA during AAF process. LA was positively correlated with Phaeoseptoria and Fusarium; and negatively correlated with 14 genera during AAF process. Therein Staphylococcus and Weissella were two most important genera for change of LA during AAF process. Detailed information of genera correlated with each organic acid is summarised in Table S5. For AAs, Acetobacter, Aspergillus, Lactobacillus, Enhydrobacter, Roseomonas, Sphingobacterium, Staphylococcus, Stenotrophomonas, and Fungi_unclassified were crucial to dynamics of AAs during AAF process (Fig. 4b). Glutamic acid (Glu), alanine (Ala), valine (Val), and leucine (Leu) are four abundant flavours for the Figure 3. PCA and HCA analysis of flavours in vinegar Pei during AAF process. (a) The dendrogram of AAF process was obtained by hierarchical cluster analysis based on PCA modeling. (b) The biplot superimposed the scores and loadings of PCA analysis based on correlation scaling method. R2VX represents the fraction of X variation modeled in the component. p(corr), t(corr) is a combined vector, p(corr) represents loading p scaled as correlation coefficient between X and t; t(corr) represents score t scaled as correlation coefficient resulting in all points falling inside the circle with radius 1
www.nature.com/scientificreports/ analyses betw microbiota and flavours by O2PLSm deling during AAF pro b)Th mpo bet AFD The le nt the lieht purple circle TtelogbeenmlCobiotandnavors with 4 and 11- acterium,and s to de /gene I with to cer,En Arthrobacter,Car elation w siti en ).n which st lated e g ine),a vith chan the micr ota cor Analysis of the fu onal co microbiota f th egar Pei dur D8 AAF 8(Fie 5a.Table S9)It ighly c SCIENTIFIC REPORTS6:26818DOl:10.1038/srep26818
www.nature.com/scientificreports/ Scientific Reports | 6:26818 | DOI: 10.1038/srep26818 5 taste of vinegar. Glu and Leu, providing umami and bitter taste of vinegar, were correlated with 14 and 22 genera (|ρ|> 0.7) respectively, in which Staphylococcus, Acetobacter, Sphingobacterium, and Aspergillus were highly correlated with the changes of Glu and Leu during AAF process (|ρ|>0.8). Ala, providing sweet taste of vinegar, was correlated with 16 genera (|ρ|> 0.7), in which Acetobacter, Aspergillus, Staphylococcus, and Lactobacillus were most important (|ρ|> 0.8) for the change of Ala during AAF process. Val, providing sweet and bitter taste of vinegar, was correlated with 14 genera (|ρ|> 0.7), in which Acetobacter, Aspergillus, Sphingobacterium, and Staphylococcus were the major Val producers (|ρ|>0.8). Moreover, γ-aminobutyric acid (Gaba), a bioactive component in vinegar, has physiological functions to depress the elevation of systolic blood pressure24. Change of Gaba during AAF process was correlated with 7 genera (|ρ|> 0.6), in which Epicoccum and Alternaria were the most important genera. Details of correlated genera with each amino acid are listed in Table S6. Acetobacter, Lactococcus, Lactobacillus, and Gluconacetobacer were important to dynamics of VFs during AAF process, which were correlated with more than 30 VFs (|ρ| > 0.7) (Fig. 4b, Table S8). A total of 56 genera were correlated with 9 volatile alcohols (|ρ|> 0.7), in which Acetobacter, Lactobacillus, Enhydrobacter, Lactococcus, Bacillales_unclassified, Gluconacetobacer, Enterococcus, Arthrobacter, Carnobacterium, Verticillium, and Nitriliruptor were correlated with more than 7 alcohols (light blue hexagons in Fig. 4b). Total 51 genera were correlated with 8 volatile acids and most of the correlation were positive (|ρ|> 0.7) (light blue octagons in Fig. 4b). There were 81 genera correlated with 25 volatile esters (|ρ|> 0.7), in which most of fungal genera were correlated with few esters (≤4) (light blue circles in Fig. 4b). There were 27 genera correlated with 4 volatile ketones (|ρ|> 0.7) and most of the correlation were positive (light blue diamonds in Fig. 4b). There were 48 genera correlated with 7 volatile aldehydes (|ρ|> 0.7), in which Staphylococcus and Sphingobacterium were correlated with more than 5 aldehydes (light blue vees in Fig. 4b). Total 21 genera were correlated with 3 volatile heterocycles, and the correlation are positive except Lactobacillus (light blue rects in Fig. 4b). 2,3,5,6-tetramethyl-pyrazine (No. 56, known as ligustrazine), a functional bioactivator in vinegar, was correlated with 16 genera, in which Gluconacetobacer, Ruminococcaceae_unclassified, and Sphingobium were excellently correlated with change of ligustrazine (|ρ|> 0.9). Total 50 genera were correlated with 3 others volatile (|ρ|> 0.7)(light blue triangles in Fig. 4b). In addition, there were 9 volatile flavours exhibited a weak correlation with microbiota (|ρ|< 0.7), suggesting these metabolites might be produced by natural physiochemical process. Details of the microbiota correlated with each volatile flavour are listed in Table S7. Analysis of the functional core microbiota for vinegar fermentation. Further analysis was performed to investigate the relationship of microbiota highly correlated with three flavour sets in vinegar Pei during AAF process (|ρ|> 0.8) (Fig. 5a, Table S9). It was shown there were 23, 37 and 62 genera highly correlated Figure 4. Correlation analyses between microbiota and flavours by O2PLS modeling during AAF process. (a) VIP(pred) (variable importance for predictive components) plot of the important microbiota (VIP(pred)>1.0). (b) The correlated network between microbial genera and flavours during AAF process. The left-side circles represent the bacterial (green) and fungal (yellow) genera correlated with flavours (|ρ|>0.7). The right-side circles represent the flavours (sugars, light purple circle; organic acids, light red circle; amino acids, light green circle; volatile flavours, light blue labels (hexagons: alcohols; octagons: acids; circles: esters; diamonds: ketones; vees: aldehydes; rects: heterocycles; triangles: others)) correlated with microbiota (|ρ|>0.7). The red long dashed lines linking the circles represent positive correlation while the blue long dashed lines represent the negative correlation between microbiota and flavours