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Microbial dynamics and flavor formation during the traditional brewing of Monascus vinegar
Food Research International 125 (2019) 108531
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Microbial dynamics and flavor formation during the traditional brewing of Monascus vinegar
T
Yajun Jianga,1, Xucong Lva,1, Chong Zhanga, Yimei Zhenga, Baodong Zhenga,b,c, Xu Duand, ⁎ Yuting Tiana,b,c, a
College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, PR China Fujian Provincial Key Laboratory of Quality Science and Processing Technology in Special Starch, Fujian Agriculture and Forestry University, Fuzhou 350002, PR China c China-Ireland International Cooperation Centre for Food Material Science and Structure Design, Fujian Agriculture and Forestry University, Fuzhou 350002, PR China d Food and Biology Engineering College, Henan University of Science and Technology, Luoyang 471000, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Monascus vinegar Microbiota community Volatile compounds Amino acids Organic acids Network analysis
Monascus vinegar is one of the most famous and popular Chinese vinegars. The present study identified 60 volatile compounds, 23 amino acids, and seven organic acids during the traditional brewing of Monascus vinegar. Acetic acid, alanine, alcohols, esters, lactic acid, and valine were the predominant metabolic compounds found during the fermentation process. Komagataeibacter medellinensis, Lactobacillus acetotolerans, Saccharomycopsis fibuligera, Sterigmatomyces halophilus, and Yarrowia lipolytica were the dominant microorganisms during the traditional brewing of Monascus vinegar. Furthermore, based on Spearman's correlation analysis, K. medellinensis showed a positive correlation with acetic acid, acetoin, benzaldehyde, phenethyl acetate, 4ethylphenol, proline, threonine, and isoleucine. Saccharomyces cerevisiae was positively associated with the production of acetoin, benzeneacetaldehyde, 2,3,5-trimethylpyrazine, proline, threonine, and isoleucine. Bacillus velezensis and Yarrowia lipolytica were positively correlated with esters and alcohols, implying that these microorganisms might make a significant contribution to the flavor of vinegar. These findings demonstrated that some microorganisms may play important roles in improving the aromatic quality of Monascus vinegar.
1. Introduction Vinegar has been used worldwide as flavoring agent in the food industry (Nie et al., 2013; Solieri & Giudici, 2009). Moreover, with a history of approximately 3000 years, vinegar plays an important role in our daily lives (Chen et al., 2017; Tesfaye, Morales, Garcia-Parrilla, & Troncoso, 2002). Various types of vinegar are made from different raw materials including rice, onions, tomatoes, apples, cider, pineapples, and honey (Solieri & Giudici, 2009). However, traditional Chinese vinegar is mostly made from different types of cereals, including rice, wheat, bran, and so on (Haruta et al., 2006; Liu, Chan et al., 2018). Generally, the production of traditional Chinese vinegar usually involves either solid or liquid state fermentation (Haruta et al., 2006; Liu, Chen, et al., 2018). Reportedly, liquid state fermentation (LSF) features higher acetic acid yield, shorter fermentation time, and a lower cost than solid state fermentation (SSF). Vinegar brewed by LSF technology has a unique flavor and taste (Nie, Zheng, Du, Xie, & Wang, 2015). Monascus vinegar (also called Hong Qu vinegar) is one of the most
famous and popular Chinese vinegars in the world because of its special quality and bioactivity (Wang et al., 2017). One special feature of Monascus vinegar is that it is produced from glutinous rice with the addition of traditional starter—Hong Qu—based on a spontaneous liquid-state fermentation technique. Monascus vinegar reduce blood lipids, and it possesses antioxidant activity caused by the addition of Hong Qu as a fermentation starter (Heber et al., 1999; Junsei, Chika, & Yoko, 2002; Tseng, Yang, Chang, Lee, & Mau, 2006). Furthermore, this spontaneous fermentation process generally leads to a rapid increase in microbial populations. All of these have a critical effect on the quality of Monascus vinegar, and their metabolites can give the vinegar soft taste and special flavor, caused by the presence of abundant amounts of organic acids and flavoring compounds. In particular, the flavor metabolite as well as the amino acids and organic acids are important indictors that can be used to evaluate vinegar quality, thus gaining consumers approval. Three organic acids, seven amino acids, and 20 volatile flavor compounds were identified as the dominant vinegar metabolites in Zhenjiang aromatic vinegar (Wang et al., 2017).
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Corresponding author at: College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, PR China. E-mail address: [email protected] (Y. Tian). 1 Co-first author: Yajun Jiang and Xucong Lv contributed equally to this study. https://doi.org/10.1016/j.foodres.2019.108531 Received 30 December 2018; Received in revised form 29 April 2019; Accepted 1 July 2019 Available online 03 July 2019 0963-9969/ © 2019 Elsevier Ltd. All rights reserved.
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2.2. DNA extraction from Pei samples of Monascus vinegar
Meanwhile, eight organic acids, 16 free amino acids, and 66 aromatic compounds were detected in Shanxi aged vinegar (Li, Li, Liu, Luo, & Lin, 2016). To our knowledge, few reports have been published on the flavor metabolites of Monascus vinegar. Unfortunately, maintaining quality with a stable and consistent flavor between different batches of Monascus vinegar products has proved to be difficult because the fermentation process occurs under open and non-sterile environmental conditions. Currently, little information has been made available related to the relationship between microbiota and metabolites during the entire brewing process of Monascus vinegar. Therefore, the dynamic changes in microbiota and metabolites during the entire brewing process of Monascus vinegar should be investigated to further evaluate the relationship between microbes and flavors. Thus, this study aimed to reveal the potential correlations between dominant microbiota and flavor metabolites during the traditional brewing of Monascus vinegar, to enhance its the flavor and quality. Therefore, it is meaningful to explore the most important microbes responsible for the flavor of Monascus vinegar. This type of analysis may be helpful for improving industrial production of Monascus vinegar.
Total bacterial and fungal DNA were extracted from eight Pei samples of Monascus vinegar using a rapid DNA extraction kit (BioTeke Corporation, Beijing, China), following the manufacturer's instructions. The extracted bacterial and fungal DNA were checked by agarose gel electrophoresis and then stored at −80 °C for further analysis.
2.3. Microbial community analysis through PCR- denaturing gradient gel electrophoresis (DGGE) The V3–V4 region of the bacterial 16 s rRNA gene was amplified with primers F357 and R518, and the fungal D1/D2 region of the 26 s rRNA gene was amplified with primers NL1GC and LS2. In addition, PCR reactions were performed in a 50 μL volume containing 41.25 μL ddH2O, 5 μL 10 × buffer (containing 2.0 mM MgCl2), 1 μL dNTP (10 Mm), 0.25 μL Taq polymerase, 1 μL of each primer, and 0.5 μL DNA. The PCR was performed using an initial denaturation at 94 °C for 4 min, followed by 30 cycles with a temperature profile of 94 °C for 0.5 min, 56 °C for 1 min, and 72 °C for 0.5 min. An extension period of 7 min at 72 °C was carried out at the end of the 30 cycles. The PCR products were analyzed using DGGE and a BioRad DCode Universal Mutation Detection System (BioRad, Richmond, CA, USA). Samples were applied to 8% (w/v) polyacrylamide gels. Optimal separation was achieved with a 30–60% urea formamide denaturing gradient for both bacterial and fungal community (100% denaturant corresponds to 7 mol/L urea and 40% formamide). The gels were stained with ethidium bromide for 30 min, washed with deionized water, and viewed by UV transillumination. Our previous research found that the species identified by sequencing were the same for the bands with the same migration position in different lanes (Lv, Weng, Zhang, Rao, & Ni, 2012). Therefore, we only labeled the bands with different migration positions in the graph, and did not label all the different bands in each lane accordingly. Quantity One Software (vers. 4.0.1, BioRad, USA) was used to convert individual DGGE lanes to densitometric profiles. These profiles were then subjected to a series of standard functions provided in the software, namely background subtraction, band detection, and band intensity. The intensity and mobility of each band were calculated (Lv et al., 2017). The relative abundance of each band was calculated by the relative intensity of each band when compared with all bands of each lane.
2. Materials and methods 2.1. Sample collection The traditional brewing of Monascus vinegar is based on empirical knowledge gained under non-sterile environmental conditions. The processing of Monascus vinegar is shown in Fig. 1, while the physicochemical characteristics of samples are shown in Fig. S1. Pei samples (fermentation samples from liquid-state vinegar culture) of Monascus vinegar were collected at different intervals during the traditional brewing process (7 and 20 days, and 2, 4, 6, 8, 10, and 12 months). Vinegar Pei samples from 7 and 20 days, as well as 2, 4, 6, 8, 10, and 12 months of traditional brewing are represented by the symbols TB7d, TB20d, TB2m, TB4m, TB6m, TB8m, TB10m, and TB12m, respectively. Samples of approximately 200 g each were collected in triplicate from Fujian Yongchun Jinyuan Co., Ltd. (Fujian, China). These samples were collected in sterile centrifuge tubes and then immediately stored in a sterilized ice box. After collection, the samples were transported to the laboratory and stored at appropriately −80 °C prior to flavor compounds analysis and total DNA extraction.
Fig. 1. Schematic representation of the traditional brewing process for Monascus vinegar. 2
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2.5. Detection of organic acid and amino acid compounds Organic acid analysis was conducted following the HPLC method. Samples (50 mL) were centrifuged (8000 ×g, 10 min) and the supernatants were passed through the 0.45 μm membrane filter. SMT-C18 (250 mm × 4.6 mm, 5 μm) was used for chromatographic analysis. The mobile phase was composed of potassium phosphate monobasic: acetonitrile = 98:2 (A) and methanol (B). Chromatographic analysis was performed at 30 °Cwith a the flow rate of 0.8 mL/min, and a detection wavelength of 210 nm; injections volumes were 10 μL for HPLC analysis. Sample compositions were identified and quantified based on retention times and standard absorbance calibration curves. Free amino acids were analyzed by an model L-8900 amino acid analyzer (Hitachi, Tokyo, Japan). The sample was centrifuged at 12,000 rpm for 10 min. The supernatant was mixed with 10% sulfosalicylic acid 1:1 (precipitated protein and macromolecular substance), placed at 4 °C for > 4 h, centrifuged at 12,000 rpm for 10 min, and the supernatant was diluted 20 times. 2.6. Statistical analysis All statistical analyses were conducted in triplicate, with the experimental results expressed as means ± SD. The volatile profiles were analyzed by principal component analysis (PCA) using SIMCA-14.1 software (UMETRICS, Sweden). To associate the microbiota and flavor metabolites, Spearman's correlations coefficient (r) was calculated among the microbial genera and metabolites; |r| > 0.6 with statistical significance (P < .05) was considered a strong correlation (Wang, Lu, Shi, & Xu, 2016; Zhu et al., 2018). The relative abundances of the organic acids were visualized with a column graph using EXCEL 2013 software (Microsoft Office, Redmond, WA, USA). The correlation networks between the selected metabolites and microbial communities were constructed with Cytoscape software (Version 3.7.1) (http:// www.cytoscape.org/).
Fig. 2. Heatmap plot indicating the content of amino acids present in Monascus vinegar. The heatmap plot indicates the relative abundances of amino acids in different samples (variables clustered on the vertical axis). The phylogenetic tree is mainly concerned with the correlation between amino acids. The phylogenetic tree was calculated using the neighbor-joining method. For hierarchical clustering of the heatmap, the peak areas were normalized per compound using lg (peak area + 1); the color scale bar represents the normalized peak area value. A deeper red indicates more abundance, a deeper blue indicates less abundance. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3. Results 3.1. Analysis of amino acids and organic acids during the traditional brewing of Monascus vinegar Many amino acids serve as intermediates for some volatile compounds that can influence the quality of vinegar. An intrinsic difference in amino acids was observed during the traditional brewing of Monascus vinegar, so we further analyzed differences among amino acids with a Hitachi amino acid analyzer. Amino acids changes in Monascus vinegar during different traditional fermentation stages are shown in Fig. 2 and Table S1. Twenty-three amino acids were detected in eight samples from different fermentation periods. The results indicated that the TB7d stage, as defined below, was the most different from other periods, as cysteine (Cys), phenylalanine (Phe), glutamic acid (Glu), citrulline (Cit), arginine (Arg), ornithine (Orn), lysine (Lys), β-aminoisobutyric acid (b-AiBA), histidine (His), β-alanine (b-Ala), aspartic acid (Asp), and tyrosine (Tyr) contents were generally higher than those of other fermentation periods. In addition, the amino acid species were relatively more abundant in the late stage of fermentation. Valine (Val) and alanine (Ala) were the most abundant amino acids in the fermentation culture. Asp and Glu belong to the umami amino acid group. Sweet amino acids include threonine (Thr), serine (Ser), glycine (Gly), and Ala. His, leucine (Leu), isoleucine (Ile), Val, Tyr, Arg, methionine (Met), and Phe are classified as bitter amino acids. Whereas Lys, proline (Pro), and Cys are tasteless amino acids. The results of the Monascus vinegar organic acid analysis are shown in Fig. 3 and Table S2. A total of seven organic acids were formed during fermentation as indicated by HPLC analysis. The assessment allowed for the identification of seven organic acids, including acetic acid, lactic acid, malic acid, tartaric acid, pyroglutamic acid, succinic
2.4. Volatile profiles analyzed by head-space solid phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC–MS) Here, 50/30 μm divinyl-benzene/carboxen/polydimethylsiloxane fiber (Supelco, Inc., Bellefonte, PA, USA) was used in the aroma extraction. The conditions for HS-SPME were carried out as described by previously reported methods (Luo, Fan, & Xu, 2008). The extraction of volatile compounds was carried out on a Trace GC-2010 Ultra gas chromatograph-DSQ II mass spectrometer (Thermo Electron Corp, Waltham, MA, USA) equipped with a DB-wax capillary column (30.0 m × 0.25 mm × 0.25 μm, Agilent Technology, Santa Clara, CA, USA). The mass spectrum was generated in the electron impact mode at 70 eV ionization energy using the full scan mode (45–400 amu). GC operating conditions were carried out as described by previously reported methods (Liu, Wang et al., 2018). The constituents were tentatively identified by mass spectra comparison and retention indices. Retention indices were calculated using C7–C40 alkane standards (Sigma-Aldrich, Steinheim, Germany) under the same chromatographic conditions, and matching the mass spectrum with the NIST11 spectrum database and Wiley (NY, 320 k compounds, Ver. 6.0). Quantification analysis was performed using 2-octanol as an internal standard.
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bacterial community of vinegar Pei samples from different brewing stages is shown in Fig. 6A, while the evolutionary trees of the sequences from denaturing gradient gel electrophoresis (DGGE) fingerprinting of the bacterial community in Monascus vinegar is shown in Fig. S2. A comparison of sequences from the excised bands to those available in the GenBank database revealed that all bacterial communities were 98% similar to those currently in the database. Notably, the bacterial community differed during the entire fermentation process. Ten bacterial genera were identified in vinegar Pei samples during the traditional brewing process, including Acinetobacter, Bacillus, Enterobacter, Flavobacterium, Herbaspirillum, Komagataeibacter, Lactobacillus, Pantoea, Staphylococcus, and Weissella. Band 5 was the brightest band emerging in each sample, which corresponds to Lactobacillus acetotolerans indicating that it was the most dominant bacterial species in Monascus vinegar. The DGGE profile for the fungi community of vinegar Pei samples from different brewing stages is shown in Fig. 6B, while the evolutionary trees of the sequences from denaturing gradient gel electrophoresis (DGGE) fingerprinting of the fungal community in Monascus vinegar is shown in Fig. S3. A comparison of the sequences from excised bands to those available in the GenBank database revealed that all bands were 99% similar to those in the database. Notably, the bacterial and fungal communities differed during the entire fermentation process. Thirteen fungal genera were identified in vinegar Pei samples during the traditional brewing process, including Ascomycota, Aspergillus, Eutypell, Malassezia, Meyerozyma, Peroneutypa, Pithomyces, Poteriochroomonas, Rhizopus, Saccharomycopsis, Saccharomyces, Sterigmatomyces, and Yarrowia. Band 13 was the brightest band emerging in each sample, which corresponds to Sterigmatomyces halophilus (F13), indicating that it was the most dominant fungal species in Monascus vinegar.
Fig. 3. The content of the organic acids present in Monascus vinegar.
acid, and citric acid. We observed that acetic acid and lactic acid were the predominant organic acids during the entire fermentation process. In general, the abundance of lactic acid decreased during the entire fermentation process, while the amount of acetic acid remained relatively stable and later increased during fermentation. 3.2. Analysis of volatile metabolites during the traditional brewing of Monascus vinegar An analysis of the volatile metabolites in liquid-state vinegar culture (termed Pei in Chinese) from different brewing stages was performed using HS-SPME-GC–MS. The assessment allowed the identification of 60 volatile compounds (Fig. 4 and Table S3), including esters (23), alcohols (14), acids (3), aldehydes (7), ketones (4), alkanes (3), phenols (3), and pyrazines (3). Esters and alcohols were the predominant volatile compounds in the fermentation process. In addition, 2-methylpyrazine, benzeneacetaldehyde, 2,3,5-trimethylpyrazine, 4-ethylphenol, 2,3,5,6-tetramethylpyrazine, furfural, 2-methylbenzaldehyde, 1,3-butanediol, hexanoic acid, benzaldehyde, 2,3-butanedione, and acetoin were the predominant volatile compounds in the late stage of fermentation. The correlation between the volatile flavor compounds and the different brewing stages was also highlighted by PCA (Fig. 5). The PCA score scatter plot of the volatile flavor compounds showed that PC 1 and PC 2 accounted for 47.1% and 20.4% of the variance in the population, respectively (Fig. 5A). Vinegar Pei samples from different brewing stages were distributed at different locations in the PCA loading plots (Fig. 5B). The vinegar Pei samples of different fermentation stages in the PCA were distributed counterclockwise (Fig. 5A), suggesting that volatile metabolites varied significantly during different stages of traditional brewing of Monascus vinegar. Vinegar Pei samples from different brewing stages could be divided into three clusters, with TB7d in the third quadrant, TB12m in the second quadrant, and TB20d, TB2m, TB4m, TB6m, TB8m, and TB10m grouped together in the second, third, and fourth quadrants.
3.4. Statistical correlations between flavor metabolites and microbiota Correlation analysis was performed between flavor metabolites and microbiota composition (Fig. 7) to analyze the interaction between flavor metabolites and microbiota composition. Spearman's correlation analysis revealed that the populations of several microorganisms were significantly correlated with the abundance of non-volatile and volatile flavors. Among them, Bacillus velezensis (B11) was significantly positively correlated with several volatile flavors, including ethyl hexanoate, isobutanol, tetradecane, ethyl octanoate, 2-nonanol, ethyl decanoate, ethyl myristate, ethyl oleate, methionol, ethyl nonanoate, ethyl valerate, ethyl laurate, ethyl 9-hexadecenoate, ethyl palmitate, 1Hexanol, 1-octanol, 2-nonanone, ethyl acetate, ethyl stearate, heptadecane, ethyl 9,12-octadecadienoatev, ethyl lactate, and octanal. Moreover, Yarrowia lipolytica (F5) was significantly positively correlated with phenethyl acetate, phenylethyl alcohol, octanoic acid, hexanoic acid, 4-ethylguaiacol, acetoin, 2,3,5-trimethylpyrazine, 1,3-butanediol, 2-methyl-propionic acid, furfural, and 2,3,5,6tetramethylpyrazine. Additionally, several microorganisms were significantly correlated with the abundance of non-volatile flavors, including Saccharomyces cerevisiae (F11), Aspergillus gracilis (F4), Y. lipolytica (F5), and Komagataeibacter medellinensis (B8), all of which positively correlated with Pro, Thr, and Ile. Weissella confusa (B6) were positively correlated with Ala, Gly, Leu, γ-aminobutyric acid (GABA), malic acid, α-aminobutyric acid (a-ABA), and Met. Specially, a network correlation of microorganisms and amino acids/organic acids was constructed (Fig. 7A). A correlation based network analysis (|r| > 0.6 with P < .05) showed that Pantoea anthophila (B12) and Lactobacillus pasteurii (B13)were positively associated with Glu, while lactic acid was positively associated with L. pasteurii (B13). Meanwhile, acetic acid had the highest relative abundance in Monascus vinegar, and it was highly correlated with Acinetobacter calcoaceticus (B4), K. medellinensis (B8), P. anthophila (B12), and L. pasteurii (B13). However, B. velezensis (B11) was strongly
3.3. Microbiota community and dynamics assessed by nested PCR-DGGE To obtain an overview of the community structure of the dominant bacteria by a culture-independent technique, PCR-DGGE analysis of the bacterial 16 s rRNA gene and fungal 26 s rDNA genes was conducted using vinegar Pei samples from different brewing stages and universal primers. The relative abundance of various bacteria and fungi in the Monascus vinegar are shown in the Table S4. The DGGE profile for the 4
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Fig. 4. Heatmap and dendrogram of the volatile organic compounds present in the eight vinegar Pei samples from different traditional fermentation stages of Monascus vinegar. The heatmap plot indicates the relative abundance of volatile organic compounds in different samples (variables clustered on the vertical axis). The phylogenetic tree is mainly concerned with the correlation between volatile flavor compounds. The phylogenetic tree was calculated using the neighbor-joining method. The color intensity is proportional to the relative abundance of volatile organic compounds.
4. Discussion
negatively associated with acetic acid. In terms of volatile flavors (Fig. 7B), B. velezensis (B11) was strongly positively associated with esters and alcohols. S. cerevisiae (F11) was strongly positively associated with acetoin, benzeneacetaldehyde and 2,3,5-trimethylpyrazine. Rhizopus microsporus (F10) was strongly positively associated with 2,3,5,6-tetramethylpyrazine. K. medellinensis (B8) was strongly positively associated with acetoin, benzaldehyde, phenethyl acetate, and 4-ethylphenol. However, A. gracilis (F4) was negatively associated with 2-ethyl-1-hexanol.
Currently, the relationship between microbiota succession and flavor metabolites dynamics during the traditional brewing of Monascus vinegar remains unclear. The microbial diversity and flavor metabolites involved in different fermentation stages should be analyzed to improve aromatic quality and to stabilize Monascus vinegar fermentation. PCR-DGGE technology has been applied to investigate the microbial community of traditional balsamic vinegar (De Vero et al., 2006), traditional Chinese vinegar (Nie et al., 2013; Wu, Ma, Zhang, & Chen, 2012; Xu et al., 2011), and strawberry vinegar biofilm (Valera, Torija, Mas, & Mateo, 2015). Among the previously mentioned 5
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Fig. 5. Principal component analysis (PCA) of volatile organic compounds in the eight vinegar Pei samples from different traditional fermentation stages of Monascus vinegar. PCA of volatile organic compounds based on (A) the first and second principal components, and (B) the first and third principal component. 6
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Fig. 6. Denaturing gradient gel electrophoresis (DGGE) fingerprinting of the bacterial (A) and fungal (B) communities in Monascus vinegar. The DGGE patterns are based on the amplification of (A) the bacterial V3–V4 region of 16S rDNA and (B) fungal D1/D2 region of 26S rDNA from Monascus vinegar samples.
et al., 2016). Lactobacillales are anaerobes that are very abundant during the aerobic fermentation process, but they are inhibited by low pH and high acidity (Li, Li, Feng, & Luo, 2015; Xu et al., 2011). Elevated levels of acetic acid and lactic acid during fermentation culture result in acidic stress, which selects for the most of acid-tolerant microbes (Li et al., 2016). The acetic acid bacteria (AAB) members, with a remarkable capacity to adapt to a wide range of acidity and pH, were isolated from the acetic acid fermentation process of various vinegars (Li et al., 2016). The genus Komagataeibacter plays an important role in resisting higher concentrations of acetic acid (Andres-Barrao et al., 2016; Yamada, 2003). Several Komagataeibacter strains have been reported to be the predominant species in vinegar with a high acetic acid percentage (> 4%) (Schüller, Hertel, & Hammes, 2000; Sievers, Sellmer, & Teuber, 1992; Stornik, Skok, & Trcek, 2016; Trcek, Raspor, & Teuber, 2000). K. medellinensis was initially reported to be able to survive at vinegar with acidity > 4%. However, relevant studies also reported that K. medellinensis cannot tolerate > 2% acidity (Carlos et al., 2017; Castro et al., 2013), which is inconsistent with the results of this study. A
genera, Flavobacterium, Acinetobacter, Bacillus, Komagataeibacter, Saccharomyces, Aspergillus, Lactobacillus, and Enterobacter have been reported in Shanxi aged vinegar and Zhenjiang aromatic vinegar (Li et al., 2016; Nie et al., 2015; Wang et al., 2016; Xu et al., 2011). Komagataeibacter and Lactobacillus were also found in seed vinegar, as well as organic apple cider submerged vinegar production (Trcek, Mahnic, & Rupnik, 2016). Furthermore, two genera of lactic acid bacteria (LAB) including Lactobacillus and Weissella were found in Monascus vinegar. Previously, a great many LAB taxa have been reported to produce large amounts of lactic acid, amino acids, and other flavor compounds during vinegar fermentation. This could be the main reason that lactic acid is the predominant organic acid present during the entire fermentation process. Lactic acid produced by LAB is a second dominant organic acid which can contribute to the fresh, sour taste of vinegar. These bacteria could also promote a soft taste by moderating the irritating sour smell (Unden & Zaunmüller, 2009; Wu et al., 2012), and they were the main producers of Glu. Additionally, in our study, L. pasteurii (B13) was strongly correlated with the presence of acetic acid, which was consistent with the findings related to Zhenjiang aromatic vinegar (Wang 7
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Fig. 7. Visualization of the correlation network according to the partial correlations between the relative abundance of dominant microbial communities and (A) amino acids and organic acids and (B) volatile organic compounds in traditional Monascus vinegar. Each node represents a flavor metabolite and a microorganism. The red solid and black dotted lines represent positive and negative correlations, respectively. In addition, line width indicates the strength of the correlation. Only the significant edges are drawn in the network using the Spearman correlation test (FDR adjusted P < .01). The code representative is displayed in Fig. 6. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
formation of the alkaloid tetramethylpyrazine (termed ligustrazine in China) through the Maillard reaction (Wu et al., 2017; Xiao & Lu, 2014). Tetramethylpyrazine is a dominant flavor and bioactive compound in Zhenjiang aromatic vinegar and Shanxi aged vinegar. Acetoin (3-hydroxy-2-butanone) is an important physiological metabolite that is excreted by many microorganisms, and it serves as a quality index of fermented products and as an important flavor in vinegar (Li et al., 2016; Xiao & Xu, 2007). A previous study has shown that microorganisms inhabiting vinegar Pei samples can use ethanol and glucose as substrates to synthesize acetoin. Two possible routes exist for the origin of acetoin. The first route is through the non-enzymatic decomposition of 2-acetolactate and the following biocatalysis by diacetyl
possible explanation is that there are different subspecies or strains of K. medellinensis, and their fermentation performance may be significantly different. In other words, some strains can tolerate high acidity, while some strains cannot tolerate high acidity. Of course, the acid resistance of K. medellinensis and its related strains remains to be determined. In the follow-up study, we will delve into the fermentation performance of K. medellinensis in Monascus vinegar. Reportedly, AAB has a capability that is different from other microorganisms, as they can transform alcohols into their corresponding organic acids. In the present study, K. medellinensis was strongly positively associated with acetoin, benzaldehyde, phenethyl acetate, 4ethylphenol, and acetic acid. In particular, acetoin is a precursor for the 8
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reductase (DR); the other route is a mixture of α-acetolactate produced by the proline metabolism pathway to form diacetyl by oxidative decarboxylation, which is then synthesized by the action of diacetyl reductase (Lu et al., 2016). In our study, the relative content of acetoin increased rapidly during the late stage of fermentation; the result was consistent with findings related to Shanxi aged vinegar and has been reported to be related to the high relative abundance of Lactobacillus and Acetobacter detected during the fermentation process (Li et al., 2016). In the present study, S. cerevisiae (F11)was strongly positively associated with acetoin, benzeneacetaldehyde, and 2,3,5-trimethylpyrazine. Additionally, among the diverse aromatic compounds, pyrazine compounds have been reported to be the major bioactive components in vinegar (Chen et al., 2010; Li et al., 2016; Lu et al., 2011). Pyrazine compounds are an important factor that affects vinegar quality, so the quality of vinegar may be improved by increasing pyrazine content. Reportedly, Saccharomyces has a strong ability to produce ethanol, which is considered an important factor that affects the microbial diversity during the fermentation process. Ethanol also has a positive correlation related to inhibiting the growth of numerous microorganisms. B. velezensis (B11) was found to be strong positively associated esters and alcohols. It was reported that the genus Bacillus was highly correlated with the formation of acetate esters in Shanxi vinegar (Li et al., 2016). Esters and alcohols play important roles in affecting vinegar quality and flavor, suggesting that B. velezensis (B11) may make an important contribution to Monascus vinegar flavor.
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Evaluation of different PCR primers for denaturing gradient gel electrophoresis (DGGE) analysis of fungal community structure in traditional fermentation starters used for Hong Qu glutinous rice wine. International Journal of Food Microbiology, 255, 58–65. Lv, X. C., Weng, X., Zhang, W., Rao, P. F., & Ni, L. (2012). Microbial diversity of traditional fermentation starters for Hong Qu glutinous rice wine as determined by PCRmediated DGGE. Food Control, 28(2), 426–434. Nie, Z. Q., Zheng, Y., Du, H. F., Xie, S. K., & Wang, M. (2015). Dynamics and diversity of microbial community succession in traditional fermentation of Shanxi aged vinegar. Food Microbiology, 47, 62–68. Nie, Z. Q., Zheng, Y., Wang, M., Han, Y., Wang, Y. N., Luo, J. M., & Niu, D. D. (2013). Exploring microbial succession and diversity during solid-state fermentation of Tianjin duliu mature vinegar. Bioresource Technology, 148, 325–333. Schüller, G., Hertel, C., & Hammes, W. P. (2000). Gluconacetobacter entanii sp. nov., isolated from submerged high-acid industrial vinegar fermentations. International Journal of Systematic and Evolutionary Microbiology, 50, 2013–2020. Sievers, M., Sellmer, S., & Teuber, M. (1992). Acetobacter europaeus sp. nov., a main component of industrial vinegar fermenters in Central Europe. Systematic and Applied Microbiology, 15, 386–392. Solieri, L., & Giudici, P. (2009). Vinegars of the world. Italy: Springer Milan. Stornik, A., Skok, B., & Trcek, J. (2016). Comparison of cultivable acetic acid bacterial microbiota in organic and conventional apple cider vinegar. Food Technology and Biotechnology, 54(1), 113–119. Tesfaye, W., Morales, M. L., Garcia-Parrilla, M. C., & Troncoso, A. M. (2002). Wine vinegar: Technology, authenticity and quality evaluation. Trends in Food Science & Technology, 13(1), 12–21. Trcek, J., Mahnic, A., & Rupnik, M. (2016). Diversity of the microbiota involved in wine and organic apple cider submerged vinegar production as revealed by DHPLC analysis and next-generation sequencing. International Journal of Food Microbiology, 223, 57–62. Trcek, J., Raspor, P., & Teuber, M. (2000). Molecular identification of Acetobacter isolates from submerged vinegar production, sequence analysis of plasmid pJK2-1 and application in the development of a cloning vector. Applied Microbiology and Biotechnology, 53, 289–295. Tseng, Y. H., Yang, J. H., Chang, H. L., Lee, Y. L., & Mau, J. L. (2006). Antioxidant properties of methanolic extracts from monascal adlay. LWT- Food Science and Technology, 39(7), 740–747. Unden, G., & Zaunmüller, T. (2009). Metabolism of sugars and organic acids by lactic acid
5. Conclusions This study provided information about the dominant microbiota and flavor formation during the traditional brewing of Monascus vinegar. The potential correlations between dominant microbiota and flavor metabolites were revealed based on Spearman's correlation analysis. The results are meaningful because they explore the most important microbes responsible for Monascus vinegar flavor, and they may be helpful for improving the industrial production of Monascus vinegar. Nevertheless, further studies should be devoted to validating the relationship between the core microorganisms and the specific flavor using multiomic approaches including metagenomics, metaproteomics, metatranscriptomics, etc. Acknowledgements The authors wish to thank Philip E. Hyatt for his comments that helped improve this article. Funding: This work was supported by the National Natural Science Foundation of China (No. 31601466), Project Funded by China Postdoctoral Science Foundation (2015T80671, 2016T90591), Fujian Provincial Education Department Key Project (JA13095), Fujian University New Century Excellent Talent Support Plan (2015-54), and Funds for Outstanding Young Scientific Talents of Fujian Agriculture and Forestry University (XJQ201418, XJQ201607). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodres.2019.108531. References Andres-Barrao, C., Saad, M. M., Ferrete, E. C., Bravo, D., Chappuis, M. L., Perez, R. O., ... Barja, F. (2016). Metaproteomics and ultrastructure characterization of Komagataeibacter spp. involved in high-acid spirit vinegar production. Food Microbiology, 55, 112–122. Carlos, M. R., Castro, M., Osorio, M., Torres-Taborda, M., Gómez, B., Zuluaga, R., ... Rojas, O. J. (2017). Effect of different carbon sources on bacterial nanocellulose production and structure using the low ph resistant strain Komagataeibacter
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Microbiology, 62, 23–31. Xiao, Z., & Lu, J. R. (2014). Strategies for enhancing fermentative production of acetoin: A review. Biotechnology Advances, 32(2), 492–503. Xiao, Z., & Xu, P. (2007). Acetoin metabolism in bacteria. Critical Reviews in Microbiology, 33(2), 127–140. Xu, W., Huang, Z., Zhang, X., Li, Q., Lu, Z., Shi, J., ... Ma, Y. (2011). Monitoring the microbial community during solid-state acetic acid fermentation of Zhenjiang aromatic vinegar. Food Microbiology, 28(6), 1175–1181. Yamada, Y. (2003). Taxonomy of acetic acid bacteria utilized for vinegar fermentation. The first international symposium and workshop on “Insight into the World of Indigenous Fermented Foods of Technology Development and Food Safety”, Bangkok (pp. I–8). . Zhu, Y. P., Zhang, F. F., Zhang, C. N., Yang, L., Fan, G. S., Xu, Y. Q., ... Li, X. T. (2018). Dynamic microbial succession of Shanxi aged vinegar and its correlation with flavour metabolites during different stages of acetic acid fermentation. Scientific Reports, 8(1), 8612.
bacteria from wine and must. Berlin Heidelberg: Springer. Valera, M. J., Torija, M. J., Mas, A., & Mateo, E. (2015). Acetic acid bacteria from biofilm of strawberry vinegar visualized by microscopy and detected by complementing culture-dependent and culture-independent techniques. Food Microbiology, 46, 452–462. Wang, Z. B., Li, T. T., Liu, F. Y., Zhang, C. S., Ma, H. L., Wang, L., & Zhao, S. (2017). Effects of ultrasonic treatment on the maturation of Zhenjiang vinegar. Ultrasonics Sonochemistry, 39, 272–280. Wang, Z. M., Lu, Z. M., Shi, J. S., & Xu, Z. H. (2016). Exploring flavour-producing core microbiota in multispecies solid-state fermentation of traditional Chinese vinegar. Scientific Reports, 6, 10. Wu, J. J., Ma, Y. K., Zhang, F. F., & Chen, F. S. (2012). Biodiversity of yeasts, lactic acid bacteria and acetic acid bacteria in the fermentation of "Shanxi aged vinegar", a traditional Chinese vinegar. Food Microbiology, 30(1), 289–297. Wu, L. H., Lu, Z. M., Zhang, X. J., Wang, Z. M., Yu, Y. J., Shi, J. S., & Xu, Z. H. (2017). Metagenomics reveals flavour metabolic network of cereal vinegar microbiota. Food
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Microbial dynamics and flavor formation during the traditional brewing of Monascus vinegar
T
Yajun Jianga,1, Xucong Lva,1, Chong Zhanga, Yimei Zhenga, Baodong Zhenga,b,c, Xu Duand, ⁎ Yuting Tiana,b,c, a
College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, PR China Fujian Provincial Key Laboratory of Quality Science and Processing Technology in Special Starch, Fujian Agriculture and Forestry University, Fuzhou 350002, PR China c China-Ireland International Cooperation Centre for Food Material Science and Structure Design, Fujian Agriculture and Forestry University, Fuzhou 350002, PR China d Food and Biology Engineering College, Henan University of Science and Technology, Luoyang 471000, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Monascus vinegar Microbiota community Volatile compounds Amino acids Organic acids Network analysis
Monascus vinegar is one of the most famous and popular Chinese vinegars. The present study identified 60 volatile compounds, 23 amino acids, and seven organic acids during the traditional brewing of Monascus vinegar. Acetic acid, alanine, alcohols, esters, lactic acid, and valine were the predominant metabolic compounds found during the fermentation process. Komagataeibacter medellinensis, Lactobacillus acetotolerans, Saccharomycopsis fibuligera, Sterigmatomyces halophilus, and Yarrowia lipolytica were the dominant microorganisms during the traditional brewing of Monascus vinegar. Furthermore, based on Spearman's correlation analysis, K. medellinensis showed a positive correlation with acetic acid, acetoin, benzaldehyde, phenethyl acetate, 4ethylphenol, proline, threonine, and isoleucine. Saccharomyces cerevisiae was positively associated with the production of acetoin, benzeneacetaldehyde, 2,3,5-trimethylpyrazine, proline, threonine, and isoleucine. Bacillus velezensis and Yarrowia lipolytica were positively correlated with esters and alcohols, implying that these microorganisms might make a significant contribution to the flavor of vinegar. These findings demonstrated that some microorganisms may play important roles in improving the aromatic quality of Monascus vinegar.
1. Introduction Vinegar has been used worldwide as flavoring agent in the food industry (Nie et al., 2013; Solieri & Giudici, 2009). Moreover, with a history of approximately 3000 years, vinegar plays an important role in our daily lives (Chen et al., 2017; Tesfaye, Morales, Garcia-Parrilla, & Troncoso, 2002). Various types of vinegar are made from different raw materials including rice, onions, tomatoes, apples, cider, pineapples, and honey (Solieri & Giudici, 2009). However, traditional Chinese vinegar is mostly made from different types of cereals, including rice, wheat, bran, and so on (Haruta et al., 2006; Liu, Chan et al., 2018). Generally, the production of traditional Chinese vinegar usually involves either solid or liquid state fermentation (Haruta et al., 2006; Liu, Chen, et al., 2018). Reportedly, liquid state fermentation (LSF) features higher acetic acid yield, shorter fermentation time, and a lower cost than solid state fermentation (SSF). Vinegar brewed by LSF technology has a unique flavor and taste (Nie, Zheng, Du, Xie, & Wang, 2015). Monascus vinegar (also called Hong Qu vinegar) is one of the most
famous and popular Chinese vinegars in the world because of its special quality and bioactivity (Wang et al., 2017). One special feature of Monascus vinegar is that it is produced from glutinous rice with the addition of traditional starter—Hong Qu—based on a spontaneous liquid-state fermentation technique. Monascus vinegar reduce blood lipids, and it possesses antioxidant activity caused by the addition of Hong Qu as a fermentation starter (Heber et al., 1999; Junsei, Chika, & Yoko, 2002; Tseng, Yang, Chang, Lee, & Mau, 2006). Furthermore, this spontaneous fermentation process generally leads to a rapid increase in microbial populations. All of these have a critical effect on the quality of Monascus vinegar, and their metabolites can give the vinegar soft taste and special flavor, caused by the presence of abundant amounts of organic acids and flavoring compounds. In particular, the flavor metabolite as well as the amino acids and organic acids are important indictors that can be used to evaluate vinegar quality, thus gaining consumers approval. Three organic acids, seven amino acids, and 20 volatile flavor compounds were identified as the dominant vinegar metabolites in Zhenjiang aromatic vinegar (Wang et al., 2017).
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Corresponding author at: College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, PR China. E-mail address: [email protected] (Y. Tian). 1 Co-first author: Yajun Jiang and Xucong Lv contributed equally to this study. https://doi.org/10.1016/j.foodres.2019.108531 Received 30 December 2018; Received in revised form 29 April 2019; Accepted 1 July 2019 Available online 03 July 2019 0963-9969/ © 2019 Elsevier Ltd. All rights reserved.
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2.2. DNA extraction from Pei samples of Monascus vinegar
Meanwhile, eight organic acids, 16 free amino acids, and 66 aromatic compounds were detected in Shanxi aged vinegar (Li, Li, Liu, Luo, & Lin, 2016). To our knowledge, few reports have been published on the flavor metabolites of Monascus vinegar. Unfortunately, maintaining quality with a stable and consistent flavor between different batches of Monascus vinegar products has proved to be difficult because the fermentation process occurs under open and non-sterile environmental conditions. Currently, little information has been made available related to the relationship between microbiota and metabolites during the entire brewing process of Monascus vinegar. Therefore, the dynamic changes in microbiota and metabolites during the entire brewing process of Monascus vinegar should be investigated to further evaluate the relationship between microbes and flavors. Thus, this study aimed to reveal the potential correlations between dominant microbiota and flavor metabolites during the traditional brewing of Monascus vinegar, to enhance its the flavor and quality. Therefore, it is meaningful to explore the most important microbes responsible for the flavor of Monascus vinegar. This type of analysis may be helpful for improving industrial production of Monascus vinegar.
Total bacterial and fungal DNA were extracted from eight Pei samples of Monascus vinegar using a rapid DNA extraction kit (BioTeke Corporation, Beijing, China), following the manufacturer's instructions. The extracted bacterial and fungal DNA were checked by agarose gel electrophoresis and then stored at −80 °C for further analysis.
2.3. Microbial community analysis through PCR- denaturing gradient gel electrophoresis (DGGE) The V3–V4 region of the bacterial 16 s rRNA gene was amplified with primers F357 and R518, and the fungal D1/D2 region of the 26 s rRNA gene was amplified with primers NL1GC and LS2. In addition, PCR reactions were performed in a 50 μL volume containing 41.25 μL ddH2O, 5 μL 10 × buffer (containing 2.0 mM MgCl2), 1 μL dNTP (10 Mm), 0.25 μL Taq polymerase, 1 μL of each primer, and 0.5 μL DNA. The PCR was performed using an initial denaturation at 94 °C for 4 min, followed by 30 cycles with a temperature profile of 94 °C for 0.5 min, 56 °C for 1 min, and 72 °C for 0.5 min. An extension period of 7 min at 72 °C was carried out at the end of the 30 cycles. The PCR products were analyzed using DGGE and a BioRad DCode Universal Mutation Detection System (BioRad, Richmond, CA, USA). Samples were applied to 8% (w/v) polyacrylamide gels. Optimal separation was achieved with a 30–60% urea formamide denaturing gradient for both bacterial and fungal community (100% denaturant corresponds to 7 mol/L urea and 40% formamide). The gels were stained with ethidium bromide for 30 min, washed with deionized water, and viewed by UV transillumination. Our previous research found that the species identified by sequencing were the same for the bands with the same migration position in different lanes (Lv, Weng, Zhang, Rao, & Ni, 2012). Therefore, we only labeled the bands with different migration positions in the graph, and did not label all the different bands in each lane accordingly. Quantity One Software (vers. 4.0.1, BioRad, USA) was used to convert individual DGGE lanes to densitometric profiles. These profiles were then subjected to a series of standard functions provided in the software, namely background subtraction, band detection, and band intensity. The intensity and mobility of each band were calculated (Lv et al., 2017). The relative abundance of each band was calculated by the relative intensity of each band when compared with all bands of each lane.
2. Materials and methods 2.1. Sample collection The traditional brewing of Monascus vinegar is based on empirical knowledge gained under non-sterile environmental conditions. The processing of Monascus vinegar is shown in Fig. 1, while the physicochemical characteristics of samples are shown in Fig. S1. Pei samples (fermentation samples from liquid-state vinegar culture) of Monascus vinegar were collected at different intervals during the traditional brewing process (7 and 20 days, and 2, 4, 6, 8, 10, and 12 months). Vinegar Pei samples from 7 and 20 days, as well as 2, 4, 6, 8, 10, and 12 months of traditional brewing are represented by the symbols TB7d, TB20d, TB2m, TB4m, TB6m, TB8m, TB10m, and TB12m, respectively. Samples of approximately 200 g each were collected in triplicate from Fujian Yongchun Jinyuan Co., Ltd. (Fujian, China). These samples were collected in sterile centrifuge tubes and then immediately stored in a sterilized ice box. After collection, the samples were transported to the laboratory and stored at appropriately −80 °C prior to flavor compounds analysis and total DNA extraction.
Fig. 1. Schematic representation of the traditional brewing process for Monascus vinegar. 2
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2.5. Detection of organic acid and amino acid compounds Organic acid analysis was conducted following the HPLC method. Samples (50 mL) were centrifuged (8000 ×g, 10 min) and the supernatants were passed through the 0.45 μm membrane filter. SMT-C18 (250 mm × 4.6 mm, 5 μm) was used for chromatographic analysis. The mobile phase was composed of potassium phosphate monobasic: acetonitrile = 98:2 (A) and methanol (B). Chromatographic analysis was performed at 30 °Cwith a the flow rate of 0.8 mL/min, and a detection wavelength of 210 nm; injections volumes were 10 μL for HPLC analysis. Sample compositions were identified and quantified based on retention times and standard absorbance calibration curves. Free amino acids were analyzed by an model L-8900 amino acid analyzer (Hitachi, Tokyo, Japan). The sample was centrifuged at 12,000 rpm for 10 min. The supernatant was mixed with 10% sulfosalicylic acid 1:1 (precipitated protein and macromolecular substance), placed at 4 °C for > 4 h, centrifuged at 12,000 rpm for 10 min, and the supernatant was diluted 20 times. 2.6. Statistical analysis All statistical analyses were conducted in triplicate, with the experimental results expressed as means ± SD. The volatile profiles were analyzed by principal component analysis (PCA) using SIMCA-14.1 software (UMETRICS, Sweden). To associate the microbiota and flavor metabolites, Spearman's correlations coefficient (r) was calculated among the microbial genera and metabolites; |r| > 0.6 with statistical significance (P < .05) was considered a strong correlation (Wang, Lu, Shi, & Xu, 2016; Zhu et al., 2018). The relative abundances of the organic acids were visualized with a column graph using EXCEL 2013 software (Microsoft Office, Redmond, WA, USA). The correlation networks between the selected metabolites and microbial communities were constructed with Cytoscape software (Version 3.7.1) (http:// www.cytoscape.org/).
Fig. 2. Heatmap plot indicating the content of amino acids present in Monascus vinegar. The heatmap plot indicates the relative abundances of amino acids in different samples (variables clustered on the vertical axis). The phylogenetic tree is mainly concerned with the correlation between amino acids. The phylogenetic tree was calculated using the neighbor-joining method. For hierarchical clustering of the heatmap, the peak areas were normalized per compound using lg (peak area + 1); the color scale bar represents the normalized peak area value. A deeper red indicates more abundance, a deeper blue indicates less abundance. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3. Results 3.1. Analysis of amino acids and organic acids during the traditional brewing of Monascus vinegar Many amino acids serve as intermediates for some volatile compounds that can influence the quality of vinegar. An intrinsic difference in amino acids was observed during the traditional brewing of Monascus vinegar, so we further analyzed differences among amino acids with a Hitachi amino acid analyzer. Amino acids changes in Monascus vinegar during different traditional fermentation stages are shown in Fig. 2 and Table S1. Twenty-three amino acids were detected in eight samples from different fermentation periods. The results indicated that the TB7d stage, as defined below, was the most different from other periods, as cysteine (Cys), phenylalanine (Phe), glutamic acid (Glu), citrulline (Cit), arginine (Arg), ornithine (Orn), lysine (Lys), β-aminoisobutyric acid (b-AiBA), histidine (His), β-alanine (b-Ala), aspartic acid (Asp), and tyrosine (Tyr) contents were generally higher than those of other fermentation periods. In addition, the amino acid species were relatively more abundant in the late stage of fermentation. Valine (Val) and alanine (Ala) were the most abundant amino acids in the fermentation culture. Asp and Glu belong to the umami amino acid group. Sweet amino acids include threonine (Thr), serine (Ser), glycine (Gly), and Ala. His, leucine (Leu), isoleucine (Ile), Val, Tyr, Arg, methionine (Met), and Phe are classified as bitter amino acids. Whereas Lys, proline (Pro), and Cys are tasteless amino acids. The results of the Monascus vinegar organic acid analysis are shown in Fig. 3 and Table S2. A total of seven organic acids were formed during fermentation as indicated by HPLC analysis. The assessment allowed for the identification of seven organic acids, including acetic acid, lactic acid, malic acid, tartaric acid, pyroglutamic acid, succinic
2.4. Volatile profiles analyzed by head-space solid phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC–MS) Here, 50/30 μm divinyl-benzene/carboxen/polydimethylsiloxane fiber (Supelco, Inc., Bellefonte, PA, USA) was used in the aroma extraction. The conditions for HS-SPME were carried out as described by previously reported methods (Luo, Fan, & Xu, 2008). The extraction of volatile compounds was carried out on a Trace GC-2010 Ultra gas chromatograph-DSQ II mass spectrometer (Thermo Electron Corp, Waltham, MA, USA) equipped with a DB-wax capillary column (30.0 m × 0.25 mm × 0.25 μm, Agilent Technology, Santa Clara, CA, USA). The mass spectrum was generated in the electron impact mode at 70 eV ionization energy using the full scan mode (45–400 amu). GC operating conditions were carried out as described by previously reported methods (Liu, Wang et al., 2018). The constituents were tentatively identified by mass spectra comparison and retention indices. Retention indices were calculated using C7–C40 alkane standards (Sigma-Aldrich, Steinheim, Germany) under the same chromatographic conditions, and matching the mass spectrum with the NIST11 spectrum database and Wiley (NY, 320 k compounds, Ver. 6.0). Quantification analysis was performed using 2-octanol as an internal standard.
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bacterial community of vinegar Pei samples from different brewing stages is shown in Fig. 6A, while the evolutionary trees of the sequences from denaturing gradient gel electrophoresis (DGGE) fingerprinting of the bacterial community in Monascus vinegar is shown in Fig. S2. A comparison of sequences from the excised bands to those available in the GenBank database revealed that all bacterial communities were 98% similar to those currently in the database. Notably, the bacterial community differed during the entire fermentation process. Ten bacterial genera were identified in vinegar Pei samples during the traditional brewing process, including Acinetobacter, Bacillus, Enterobacter, Flavobacterium, Herbaspirillum, Komagataeibacter, Lactobacillus, Pantoea, Staphylococcus, and Weissella. Band 5 was the brightest band emerging in each sample, which corresponds to Lactobacillus acetotolerans indicating that it was the most dominant bacterial species in Monascus vinegar. The DGGE profile for the fungi community of vinegar Pei samples from different brewing stages is shown in Fig. 6B, while the evolutionary trees of the sequences from denaturing gradient gel electrophoresis (DGGE) fingerprinting of the fungal community in Monascus vinegar is shown in Fig. S3. A comparison of the sequences from excised bands to those available in the GenBank database revealed that all bands were 99% similar to those in the database. Notably, the bacterial and fungal communities differed during the entire fermentation process. Thirteen fungal genera were identified in vinegar Pei samples during the traditional brewing process, including Ascomycota, Aspergillus, Eutypell, Malassezia, Meyerozyma, Peroneutypa, Pithomyces, Poteriochroomonas, Rhizopus, Saccharomycopsis, Saccharomyces, Sterigmatomyces, and Yarrowia. Band 13 was the brightest band emerging in each sample, which corresponds to Sterigmatomyces halophilus (F13), indicating that it was the most dominant fungal species in Monascus vinegar.
Fig. 3. The content of the organic acids present in Monascus vinegar.
acid, and citric acid. We observed that acetic acid and lactic acid were the predominant organic acids during the entire fermentation process. In general, the abundance of lactic acid decreased during the entire fermentation process, while the amount of acetic acid remained relatively stable and later increased during fermentation. 3.2. Analysis of volatile metabolites during the traditional brewing of Monascus vinegar An analysis of the volatile metabolites in liquid-state vinegar culture (termed Pei in Chinese) from different brewing stages was performed using HS-SPME-GC–MS. The assessment allowed the identification of 60 volatile compounds (Fig. 4 and Table S3), including esters (23), alcohols (14), acids (3), aldehydes (7), ketones (4), alkanes (3), phenols (3), and pyrazines (3). Esters and alcohols were the predominant volatile compounds in the fermentation process. In addition, 2-methylpyrazine, benzeneacetaldehyde, 2,3,5-trimethylpyrazine, 4-ethylphenol, 2,3,5,6-tetramethylpyrazine, furfural, 2-methylbenzaldehyde, 1,3-butanediol, hexanoic acid, benzaldehyde, 2,3-butanedione, and acetoin were the predominant volatile compounds in the late stage of fermentation. The correlation between the volatile flavor compounds and the different brewing stages was also highlighted by PCA (Fig. 5). The PCA score scatter plot of the volatile flavor compounds showed that PC 1 and PC 2 accounted for 47.1% and 20.4% of the variance in the population, respectively (Fig. 5A). Vinegar Pei samples from different brewing stages were distributed at different locations in the PCA loading plots (Fig. 5B). The vinegar Pei samples of different fermentation stages in the PCA were distributed counterclockwise (Fig. 5A), suggesting that volatile metabolites varied significantly during different stages of traditional brewing of Monascus vinegar. Vinegar Pei samples from different brewing stages could be divided into three clusters, with TB7d in the third quadrant, TB12m in the second quadrant, and TB20d, TB2m, TB4m, TB6m, TB8m, and TB10m grouped together in the second, third, and fourth quadrants.
3.4. Statistical correlations between flavor metabolites and microbiota Correlation analysis was performed between flavor metabolites and microbiota composition (Fig. 7) to analyze the interaction between flavor metabolites and microbiota composition. Spearman's correlation analysis revealed that the populations of several microorganisms were significantly correlated with the abundance of non-volatile and volatile flavors. Among them, Bacillus velezensis (B11) was significantly positively correlated with several volatile flavors, including ethyl hexanoate, isobutanol, tetradecane, ethyl octanoate, 2-nonanol, ethyl decanoate, ethyl myristate, ethyl oleate, methionol, ethyl nonanoate, ethyl valerate, ethyl laurate, ethyl 9-hexadecenoate, ethyl palmitate, 1Hexanol, 1-octanol, 2-nonanone, ethyl acetate, ethyl stearate, heptadecane, ethyl 9,12-octadecadienoatev, ethyl lactate, and octanal. Moreover, Yarrowia lipolytica (F5) was significantly positively correlated with phenethyl acetate, phenylethyl alcohol, octanoic acid, hexanoic acid, 4-ethylguaiacol, acetoin, 2,3,5-trimethylpyrazine, 1,3-butanediol, 2-methyl-propionic acid, furfural, and 2,3,5,6tetramethylpyrazine. Additionally, several microorganisms were significantly correlated with the abundance of non-volatile flavors, including Saccharomyces cerevisiae (F11), Aspergillus gracilis (F4), Y. lipolytica (F5), and Komagataeibacter medellinensis (B8), all of which positively correlated with Pro, Thr, and Ile. Weissella confusa (B6) were positively correlated with Ala, Gly, Leu, γ-aminobutyric acid (GABA), malic acid, α-aminobutyric acid (a-ABA), and Met. Specially, a network correlation of microorganisms and amino acids/organic acids was constructed (Fig. 7A). A correlation based network analysis (|r| > 0.6 with P < .05) showed that Pantoea anthophila (B12) and Lactobacillus pasteurii (B13)were positively associated with Glu, while lactic acid was positively associated with L. pasteurii (B13). Meanwhile, acetic acid had the highest relative abundance in Monascus vinegar, and it was highly correlated with Acinetobacter calcoaceticus (B4), K. medellinensis (B8), P. anthophila (B12), and L. pasteurii (B13). However, B. velezensis (B11) was strongly
3.3. Microbiota community and dynamics assessed by nested PCR-DGGE To obtain an overview of the community structure of the dominant bacteria by a culture-independent technique, PCR-DGGE analysis of the bacterial 16 s rRNA gene and fungal 26 s rDNA genes was conducted using vinegar Pei samples from different brewing stages and universal primers. The relative abundance of various bacteria and fungi in the Monascus vinegar are shown in the Table S4. The DGGE profile for the 4
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Fig. 4. Heatmap and dendrogram of the volatile organic compounds present in the eight vinegar Pei samples from different traditional fermentation stages of Monascus vinegar. The heatmap plot indicates the relative abundance of volatile organic compounds in different samples (variables clustered on the vertical axis). The phylogenetic tree is mainly concerned with the correlation between volatile flavor compounds. The phylogenetic tree was calculated using the neighbor-joining method. The color intensity is proportional to the relative abundance of volatile organic compounds.
4. Discussion
negatively associated with acetic acid. In terms of volatile flavors (Fig. 7B), B. velezensis (B11) was strongly positively associated with esters and alcohols. S. cerevisiae (F11) was strongly positively associated with acetoin, benzeneacetaldehyde and 2,3,5-trimethylpyrazine. Rhizopus microsporus (F10) was strongly positively associated with 2,3,5,6-tetramethylpyrazine. K. medellinensis (B8) was strongly positively associated with acetoin, benzaldehyde, phenethyl acetate, and 4-ethylphenol. However, A. gracilis (F4) was negatively associated with 2-ethyl-1-hexanol.
Currently, the relationship between microbiota succession and flavor metabolites dynamics during the traditional brewing of Monascus vinegar remains unclear. The microbial diversity and flavor metabolites involved in different fermentation stages should be analyzed to improve aromatic quality and to stabilize Monascus vinegar fermentation. PCR-DGGE technology has been applied to investigate the microbial community of traditional balsamic vinegar (De Vero et al., 2006), traditional Chinese vinegar (Nie et al., 2013; Wu, Ma, Zhang, & Chen, 2012; Xu et al., 2011), and strawberry vinegar biofilm (Valera, Torija, Mas, & Mateo, 2015). Among the previously mentioned 5
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Fig. 5. Principal component analysis (PCA) of volatile organic compounds in the eight vinegar Pei samples from different traditional fermentation stages of Monascus vinegar. PCA of volatile organic compounds based on (A) the first and second principal components, and (B) the first and third principal component. 6
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Fig. 6. Denaturing gradient gel electrophoresis (DGGE) fingerprinting of the bacterial (A) and fungal (B) communities in Monascus vinegar. The DGGE patterns are based on the amplification of (A) the bacterial V3–V4 region of 16S rDNA and (B) fungal D1/D2 region of 26S rDNA from Monascus vinegar samples.
et al., 2016). Lactobacillales are anaerobes that are very abundant during the aerobic fermentation process, but they are inhibited by low pH and high acidity (Li, Li, Feng, & Luo, 2015; Xu et al., 2011). Elevated levels of acetic acid and lactic acid during fermentation culture result in acidic stress, which selects for the most of acid-tolerant microbes (Li et al., 2016). The acetic acid bacteria (AAB) members, with a remarkable capacity to adapt to a wide range of acidity and pH, were isolated from the acetic acid fermentation process of various vinegars (Li et al., 2016). The genus Komagataeibacter plays an important role in resisting higher concentrations of acetic acid (Andres-Barrao et al., 2016; Yamada, 2003). Several Komagataeibacter strains have been reported to be the predominant species in vinegar with a high acetic acid percentage (> 4%) (Schüller, Hertel, & Hammes, 2000; Sievers, Sellmer, & Teuber, 1992; Stornik, Skok, & Trcek, 2016; Trcek, Raspor, & Teuber, 2000). K. medellinensis was initially reported to be able to survive at vinegar with acidity > 4%. However, relevant studies also reported that K. medellinensis cannot tolerate > 2% acidity (Carlos et al., 2017; Castro et al., 2013), which is inconsistent with the results of this study. A
genera, Flavobacterium, Acinetobacter, Bacillus, Komagataeibacter, Saccharomyces, Aspergillus, Lactobacillus, and Enterobacter have been reported in Shanxi aged vinegar and Zhenjiang aromatic vinegar (Li et al., 2016; Nie et al., 2015; Wang et al., 2016; Xu et al., 2011). Komagataeibacter and Lactobacillus were also found in seed vinegar, as well as organic apple cider submerged vinegar production (Trcek, Mahnic, & Rupnik, 2016). Furthermore, two genera of lactic acid bacteria (LAB) including Lactobacillus and Weissella were found in Monascus vinegar. Previously, a great many LAB taxa have been reported to produce large amounts of lactic acid, amino acids, and other flavor compounds during vinegar fermentation. This could be the main reason that lactic acid is the predominant organic acid present during the entire fermentation process. Lactic acid produced by LAB is a second dominant organic acid which can contribute to the fresh, sour taste of vinegar. These bacteria could also promote a soft taste by moderating the irritating sour smell (Unden & Zaunmüller, 2009; Wu et al., 2012), and they were the main producers of Glu. Additionally, in our study, L. pasteurii (B13) was strongly correlated with the presence of acetic acid, which was consistent with the findings related to Zhenjiang aromatic vinegar (Wang 7
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Fig. 7. Visualization of the correlation network according to the partial correlations between the relative abundance of dominant microbial communities and (A) amino acids and organic acids and (B) volatile organic compounds in traditional Monascus vinegar. Each node represents a flavor metabolite and a microorganism. The red solid and black dotted lines represent positive and negative correlations, respectively. In addition, line width indicates the strength of the correlation. Only the significant edges are drawn in the network using the Spearman correlation test (FDR adjusted P < .01). The code representative is displayed in Fig. 6. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
formation of the alkaloid tetramethylpyrazine (termed ligustrazine in China) through the Maillard reaction (Wu et al., 2017; Xiao & Lu, 2014). Tetramethylpyrazine is a dominant flavor and bioactive compound in Zhenjiang aromatic vinegar and Shanxi aged vinegar. Acetoin (3-hydroxy-2-butanone) is an important physiological metabolite that is excreted by many microorganisms, and it serves as a quality index of fermented products and as an important flavor in vinegar (Li et al., 2016; Xiao & Xu, 2007). A previous study has shown that microorganisms inhabiting vinegar Pei samples can use ethanol and glucose as substrates to synthesize acetoin. Two possible routes exist for the origin of acetoin. The first route is through the non-enzymatic decomposition of 2-acetolactate and the following biocatalysis by diacetyl
possible explanation is that there are different subspecies or strains of K. medellinensis, and their fermentation performance may be significantly different. In other words, some strains can tolerate high acidity, while some strains cannot tolerate high acidity. Of course, the acid resistance of K. medellinensis and its related strains remains to be determined. In the follow-up study, we will delve into the fermentation performance of K. medellinensis in Monascus vinegar. Reportedly, AAB has a capability that is different from other microorganisms, as they can transform alcohols into their corresponding organic acids. In the present study, K. medellinensis was strongly positively associated with acetoin, benzaldehyde, phenethyl acetate, 4ethylphenol, and acetic acid. In particular, acetoin is a precursor for the 8
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reductase (DR); the other route is a mixture of α-acetolactate produced by the proline metabolism pathway to form diacetyl by oxidative decarboxylation, which is then synthesized by the action of diacetyl reductase (Lu et al., 2016). In our study, the relative content of acetoin increased rapidly during the late stage of fermentation; the result was consistent with findings related to Shanxi aged vinegar and has been reported to be related to the high relative abundance of Lactobacillus and Acetobacter detected during the fermentation process (Li et al., 2016). In the present study, S. cerevisiae (F11)was strongly positively associated with acetoin, benzeneacetaldehyde, and 2,3,5-trimethylpyrazine. Additionally, among the diverse aromatic compounds, pyrazine compounds have been reported to be the major bioactive components in vinegar (Chen et al., 2010; Li et al., 2016; Lu et al., 2011). Pyrazine compounds are an important factor that affects vinegar quality, so the quality of vinegar may be improved by increasing pyrazine content. Reportedly, Saccharomyces has a strong ability to produce ethanol, which is considered an important factor that affects the microbial diversity during the fermentation process. Ethanol also has a positive correlation related to inhibiting the growth of numerous microorganisms. B. velezensis (B11) was found to be strong positively associated esters and alcohols. It was reported that the genus Bacillus was highly correlated with the formation of acetate esters in Shanxi vinegar (Li et al., 2016). Esters and alcohols play important roles in affecting vinegar quality and flavor, suggesting that B. velezensis (B11) may make an important contribution to Monascus vinegar flavor.
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5. Conclusions This study provided information about the dominant microbiota and flavor formation during the traditional brewing of Monascus vinegar. The potential correlations between dominant microbiota and flavor metabolites were revealed based on Spearman's correlation analysis. The results are meaningful because they explore the most important microbes responsible for Monascus vinegar flavor, and they may be helpful for improving the industrial production of Monascus vinegar. Nevertheless, further studies should be devoted to validating the relationship between the core microorganisms and the specific flavor using multiomic approaches including metagenomics, metaproteomics, metatranscriptomics, etc. Acknowledgements The authors wish to thank Philip E. Hyatt for his comments that helped improve this article. Funding: This work was supported by the National Natural Science Foundation of China (No. 31601466), Project Funded by China Postdoctoral Science Foundation (2015T80671, 2016T90591), Fujian Provincial Education Department Key Project (JA13095), Fujian University New Century Excellent Talent Support Plan (2015-54), and Funds for Outstanding Young Scientific Talents of Fujian Agriculture and Forestry University (XJQ201418, XJQ201607). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodres.2019.108531. References Andres-Barrao, C., Saad, M. M., Ferrete, E. C., Bravo, D., Chappuis, M. L., Perez, R. O., ... Barja, F. (2016). Metaproteomics and ultrastructure characterization of Komagataeibacter spp. involved in high-acid spirit vinegar production. Food Microbiology, 55, 112–122. Carlos, M. R., Castro, M., Osorio, M., Torres-Taborda, M., Gómez, B., Zuluaga, R., ... Rojas, O. J. (2017). Effect of different carbon sources on bacterial nanocellulose production and structure using the low ph resistant strain Komagataeibacter
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bacteria from wine and must. Berlin Heidelberg: Springer. Valera, M. J., Torija, M. J., Mas, A., & Mateo, E. (2015). Acetic acid bacteria from biofilm of strawberry vinegar visualized by microscopy and detected by complementing culture-dependent and culture-independent techniques. Food Microbiology, 46, 452–462. Wang, Z. B., Li, T. T., Liu, F. Y., Zhang, C. S., Ma, H. L., Wang, L., & Zhao, S. (2017). Effects of ultrasonic treatment on the maturation of Zhenjiang vinegar. Ultrasonics Sonochemistry, 39, 272–280. Wang, Z. M., Lu, Z. M., Shi, J. S., & Xu, Z. H. (2016). Exploring flavour-producing core microbiota in multispecies solid-state fermentation of traditional Chinese vinegar. Scientific Reports, 6, 10. Wu, J. J., Ma, Y. K., Zhang, F. F., & Chen, F. S. (2012). Biodiversity of yeasts, lactic acid bacteria and acetic acid bacteria in the fermentation of "Shanxi aged vinegar", a traditional Chinese vinegar. Food Microbiology, 30(1), 289–297. Wu, L. H., Lu, Z. M., Zhang, X. J., Wang, Z. M., Yu, Y. J., Shi, J. S., & Xu, Z. H. (2017). Metagenomics reveals flavour metabolic network of cereal vinegar microbiota. Food
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