Metagenomic analysis reveals the impact of JIUYAO microbial diversity on fermentation and the volatile profile of Shaoxing-jiu

Food Microbiology 86 (2020) 103326

Contents lists available at ScienceDirect

Food Microbiology journal homepage: www.elsevier.com/locate/fm

Metagenomic analysis reveals the impact of JIUYAO microbial diversity on fermentation and the volatile profile of Shaoxing-jiu

T

Chen Chena, Yang Liua, Huaixiang Tiana, Lianzhong Aib, Haiyan Yua,∗ a b

Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, 201418, China School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

A R T I C LE I N FO

A B S T R A C T

Keywords: JIUYAO High-throughput sequencing Core microbes Flavor Artificial starters

This study focused on the microbial communities found in JIUYAO, the fermentation starter traditionally used in Shaoxing-jiu, and elucidated their relationship with the fermentation activities and volatile compounds involved in winemaking. The microbial communities found in all JIUYAO samples tested were dominated by Pediococcus and Weissella bacteria and Saccharomycopsis and Rhizopus fungi. Saccharifying power showed significant positive correlations with the presence of Pedioccoccus, Saccharomycopsis, and Rhizopus, whereas acid production capacity was strongly associated with Pedioccoccus, Weissella, and Rhizopus. Alcohol production capacity positively correlated with the presence of Pedioccoccus and Rhizopus. Fifteen important volatile compounds (odor-activity values ≥ 1) including esters, alcohols, acids, and aldehydes were identified in Huangjiu samples fermented with JIUYAO. Positive correlations were found between Saccharomycopsis and phenylethanol/ethyl butyrate, Rhizopus and ethyl propionate/ethyl laurate/ethyl butyrate, Pedioccoccus and ethyl laurate/acetic acid, and Weissella and decanoic acid/isopentanol. These results imply that these microorganisms significantly contribute to the fermentation activities and flavor of Shaoxing-jiu. Finally, the results showed that a combination of five core microbes with Saccharomyces cerevisiae could be used as a starter in winemaking. To conclude, this study provides a comprehensive overview of the core microbes found in JIUYAO and strategies for the selection of beneficial microorganisms to improve the quality and flavor of Shaoxing-jiu.

1. Introduction Huangjiu is an ancient wine with a history spanning more than 4000 years (Xu et al., 2015). Shaoxing-jiu is the most well-known Huangjiu due to its desirable flavor owing to its unique geographical location, raw materials, and manufacturing processes (Chen et al., 2013a, 2013b, 2013c). The unique craft of producing Shaoxing-jiu, with its characteristic clear brown color, subtle sweet aroma and low ethanol content has been handed down through generations (Shen et al., 2011) and it has Protected Designation of Origin (PDO) status. Typically, Shaoxing-jiu is produced by brewing wheat qu and JIUYAO (Chen et al., 2013a, 2013b, 2013c). Generally, this is a twostage process: primary fermentation using wheat qu and JIUYAO at 28 °C (3–5 days) and secondary fermentation at 10–15 °C (10–20 days) in an open environment to enrich the flavor profile (Yang et al., 2018). Wheat qu is usually used as a saccharifying agent to degrade the starch in rice into sugar, while JIUYAO promotes both saccharification and fermentation during Huangjiu fermentation. The microorganisms in JIUYAO are believed to play a critical role in the unique aroma, flavor,

∗

and fermentation of Huangjiu (Xie et al., 2012). JIUYAO is a rich source of brewers’ microbial resources, including yeasts, molds, and bacteria. However, currently studies of JIUYAO are only found in the Chinese literature, and information about the microorganisms found in JIUYAO is limited. Furthermore, due to differences in the traditional methods of processing JIUYAO, the varieties and proportions of microorganisms present vary with geographical location, manufacturer, and batch. The aroma of wine is an important characteristic that influences perceptions of quality and consumer acceptance. Many factors influence the aroma of Huangjiu products, including the raw materials, starters and the winemaking process. Among them, starters are the most important as they contain an abundance of microbial strains with varying abilities to metabolize different substrates to produce a diverse array of aroma compounds (Welke et al., 2014). The relationships between microorganisms and the fermentation activities/volatile profiles of Huangjiu have been previously investigated. Chen and Xu (2012) found that the dominant compounds in Huangjiu of different regions, including most alcohols, esters (ethyl acetate, isobutyl acetate, isoamyl acetate, and 2-phenylethyl acetate) and volatile acids (mainly acetic

Corresponding author. Department of Food Science and Technology, Shanghai Institute of Technology, 100 Haiquan Road, 201418, Shanghai, China. E-mail address: [email protected] (H. Yu).

https://doi.org/10.1016/j.fm.2019.103326 Received 25 April 2019; Received in revised form 24 July 2019; Accepted 4 September 2019 Available online 17 September 2019 0740-0020/ © 2019 Elsevier Ltd. All rights reserved.

Food Microbiology 86 (2020) 103326

C. Chen, et al.

2.4. Huangjiu fermentation on a lab-scale and its chemical analysis

acid), are mainly produced during yeast fermentation. Wang et al. (2014) found that the presence of lactic acid bacteria (LAB) was positively correlated with organic acid dynamics during fermentation of Shaoxing-jiu. Additionally, molds produce many enzymes involved in cellular metabolism and the resultant small molecules can contribute to the formation of flavor esters in the final product (Cai et al., 2018). However, these studies did not focus specifically on Shaoxing-jiu and JIUYAO. Studying the relationship between the specific microbes in JIUYAO and the resultant flavor of Shaoxing-jiu is important for a better understanding of the microbial communities and their contribution to the fermentation process. Besides, the use of traditional JIUYAO has several challenges, including the instability of batches and potential safety risks (Jiao et al., 2017). In recent years, commercial starters have been used in industrial winemaking (Tufariello et al., 2019). However, the flavor of Huangjiu produced with commercial starters is inferior to that of wine produced with traditional JIUYAO. Therefore, it is very important to develop artificial starters that can replace traditional JIUYAO in Shaoxing-jiu production, while still providing safe high-quality products for consumers. In the present work, we firstly used high-throughput sequencing to study the microbial communities of JIUYAO samples collected from different wineries in Shaoxing. Subsequently, the relationships between core microbes, fermentation activity and the resultant flavor of Shaoxing-jiu were systematically explored. Finally, novel artificial starters were made that may be useful in the industrialization of Shaoxing-jiu production.

Huangjiu fermentation steps can be generally divided into rice soaking, steaming, cooling, starter addition, and fermentation. At room temperature, 100 g of glutinous rice grains were soaked in 100 mL of water for 12 h. Then, the samples were steamed for 20 min. After the steamed rice was cooled down to between 25 °C and 30 °C, the fermentation of Huangjiu was initiated by adding JIUYAO with a concentration of 0.002 g/g steamed rice. Fermentation was allowed to proceed for 30 h at 29 °C. After two weeks of post-fermentation at 16 °C, the alcohol contents, acidity and the saccharifying power of the resultant Huangjiu samples were detected. A modification of the method of Crowell and Ough (1979) was used to analyze the alcohol content. To detect the acidity, Huangjiu was titrated with 0.01 mol/L of NaOH until pH 8.2 was sustained for 4–5 s, using phenolphthalein as a color indicator. The saccharifying power was analyzed using the chemical procedure described by Wang (2005). All of the chemical analysis experiments were repeated in triplicate. 2.5. Volatile profile analysis of Huangjiu The volatile profiles of six Huangjiu samples brewed using the six JIUYAO samples were analyzed using SPME-GCMS methods (Luo et al., 2012). Briefly, 5.00 g of each Huangjiu sample and 1.00 g of NaCl were transferred to a 30 mL vial. A DVB/CAR/PDMS-coated HS-SPME fiber (Supelco, Inc. Bellefonte, Pennsylvania, USA) was used to extract the volatile compounds. The volatile compounds were desorbed from the fiber in the GC inlet for 5 min at 250 °C. The GC and MS conditions were as described in our previous study (Yu et al., 2019). The initial temperature was held at 40 °C for 5 min, then increased to 120 °C at a rate of 3 °C/min, and finally to 200 °C at 3 °C/min. Each analysis was performed three times.

2. Materials and methods 2.1. Collection of JIUYAO samples Thirty JIUYAO samples were selected from different Shaoxing-jiuries (Tapai, Dongfang, Xianheng, Fuquan, Disan, and Small) in Zhejiang province, China. The samples were refrigerated at 4 °C before being transferred to the laboratory.

2.6. Sensory evaluation The method and procedures for sensory evaluation were as described for our previous study (Yu et al., 2019). The panelists provided a sensory descriptive analysis of each Huangjiu sample (20 mL) at room temperature. The samples were presented in a random order in clear, tulip-shaped glasses covered with petri dishes. The procedure was carried out in a sensory laboratory following GB/T 17946-2008. Ten panelists (five male and five female, 23–30 years old) were selected from 24 candidates due to their high discriminant capability. The sensory attributes to be evaluated were selected by the panelists during preliminary training sessions to describe the samples and with reference to the relevant literature (Chen et al., 2013b). Sour, ester, sauce, sweet, alcohol, caramel, fruit and qu aromas were chosen as the sensory attributes. The aromas of the Huangjiu samples were quantitatively measured on a 10-point interval scale. Intensity ratings were scored on a scale from 0 to 9, where 0 meant none detected and 9 meant extremely strong. The experiment was repeated in triplicate.

2.2. Sample processing and DNA extraction A 1-g sample of each JIUYAO was mixed with 9 mL of sterile NaCl solution (0.85%, w/v) in a homogenous suspension for 10 min. A Qiagen DNA Mini-Kit (Qiagen, Hilden, Germany) was used to extract DNA from the suspended material and the bead-beating method followed by 0.8% agarose gel electrophoresis was used to evaluate the integrity and purity of the extracted DNA. A micro-ultraviolet spectrophotometer was used to measure the OD 260/280 value, which indicates the DNA concentration of the extracted DNA. Samples were stored at −20 °C. 2.3. PCR amplification, quantification and sequencing The V3–V4 regions of the 16S ribosomal RNA (rRNA) genes of each sample were determined for each sample. A set of 6-nucleotide barcodes was added to the universal forward primer 338F (5′-ACTCCTAC GGGAGGCAGCA-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The fungal primers ITS5F (5′-GGAAGTAAAAGTCG TAACAAGG-3′) and ITS1R (5′-GCTGCGTT CTTCATCGATGC-3′) with a specific barcode were used to amplify the ITS1 regions of the fungal ITS rRNA genes. An Agilent DNA 1000 Kit and Agilent 2100 Bioanalyser (Agilent Technologies, USA) were used in combination to quantify the PCR products according to the manufacturer's instructions. The amplified products of all samples were pooled in equimolar ratios, with each having a final concentration of 100 nmol/L. These pools were sequenced using an Illumina MiSeq high-throughput sequencing platform (Illumina, San Diego, USA).

2.7. Bioinformatics and statistical analyses The original sequencing reads were trimmed to remove any lowquality reads. The reads’ quality scores were filtered using the sliding window method. After filtering, more than 85.3% of the reads were retained. Before and after the removal of the primers and labels, bioinformatics analyses were performed on the extracted high-quality reads using the QIIME analysis platform (Caporaso et al., 2010). Briefly, the sequences were aligned using PyNAST (Caporaso et al., 2010) and clustered under 100% sequence identity using UCLUST (Edgar, 2010) to obtain the unique V3–V4 sequence set. After the removal of chimera sequences, the Ribosomal Database Project (RDP) (Cole et al., 2007) classifier was applied with a minimum bootstrap threshold of 80%. A chimera-checked OTU representative set in FastTree (Price et al., 2009) 2

Food Microbiology 86 (2020) 103326

C. Chen, et al.

2.9. E-nose analysis

was used to construct a de novo taxonomic tree for downstream analyses. The Shannon, Chao1, Simpson, and ACE indices were used to evaluate alpha diversity and UniFrac metrics were used to evaluate beta diversity. Both weighted and unweighted calculations were used to predict the 16S rRNA and ITS rRNA based on high-throughput sequencing data prior to a principal coordinate analysis (PCoA). PICRUSt (Phylogenetic investigation of communities by reconstruction of unobserved states, v1.0) was used to identify microbial functional features. All statistical analyses were executed using the R program. The Kruskal-Wallis test was used to compare the relative abundances of taxa based on the rarefied OTU subset. The Benjamini-Yekutieli method was used to control for the multiple tests. PCoA analyses were performed in R using the ade4 package (Zapala and Schork, 2006). Spearman's rank correlation coefficients were used for correlation analysis of the core OTUs, and the pheatmap package was used for cluster analysis and heatmap construction in R. The correlations among core bacteria, core fungi and volatile compounds were determined using a PLS-DA model and plotted in a concatenation panel using R software with the mixOmics package (Liu et al., 2018). The data for sensory attributes and metabolic pathways were analyzed using ANOVA with Duncan's multiple range test (p < 0.05).

The E-nose analysis was performed with an electronic nose (Supernose, Isenso Group Corporation, New York, USA) with 14 metal oxide sensors and the Smart Nose intelligent identification software system. HS-SPME/GC-MS was carried out before the analysis in an airtight glass vial with a PTFE-silicon stopper for each sample (15.0 g of Huangjiu) at room temperature for 30 min. Based on the G/GO ratio (G represents the conductance of 14 sensors monitoring the sample gas and GO represents clean air), all of the sensor response data were reviewed. The environmental chamber was controlled as follows: room temperature (25 °C); sensor cleaning flow 6 L/min; automatic zero adjustment time 10 s; internal and inlet flow rate were both 600 mL/min. The sample detection time was 60 s, during which the absorbed gas was measured each second. Each sample analysis was performed in triplicate. 3. Results 3.1. Sequencing of the JIUYAO samples The JIUYAO sample data and sequencing results are shown in Tables S1 and S2. For the bacteria, a dataset containing 1,501,018 filtered, high-quality, and classifiable 16S rRNA gene sequences was generated using HTS, representing an average of 50,034 sequences per sample (range: 36,167–59,556, Table S1). All of the sequences were clustered with a 97% sequence identity cut-off, with an average of 1780 OTUs per sample (range: 1413–2021). For the fungi, a dataset consisting of 1,344,895 filtered, high-quality, and classifiable ITS gene sequences was generated using HTS, representing an average of 44,830 sequences for each individual sample (range: 30,168–60,479, Table S2). All of the sequences were clustered with representative sequences, and a 97% sequence identity cut-off was used. The number of OTUs per sample ranged from 354 to 490.

2.8. Screening of core microbial strains and preparation of starter culture Four genera (Pediococcus, Weissella, Saccharomycopsis, and Rhizopus) were selected as the core functional microbes in JIUYAO, and their strains were isolated from the JIUYAO samples. Serial dilutions (10−1 to 10−8) of the JIUYAO samples were prepared using sterile water and 0.1 mL of an appropriate dilution (10−5–10−8) and were spread on appropriate agar plates in triplicate. To isolate the LAB, aliquots were spread on MRS agar and incubated at 37 °C for 24 h, and then ropy colonies were further streaked on MRS agar plates and incubated in MRS broth, as described above. Acid production capacity and flavorforming ability were determined and used as indicators for LAB screening. To isolate the fungi, aliquots were spread on PDA agar at 30 °C for 24 h and ropy colonies were further streaked on PDA plates and incubated in MEB broth. Saccharifying power, alcohol production capacity and flavor forming ability were determined and used as indicators for Saccharomycopsis and Rhizopus screening. The strains were first distinguished based on morphological observations. The identifications were confirmed by partial sequencing of the 16S rRNA gene of the bacteria and the ITS gene of the fungi. The total viable count (TVC) of the bacteria in the JIUYAO was determined using nutrient agar incubated at 37 °C for 48 h, and the fungal content of the JIUYAO was determined with PDA incubated at 30 °C for 48 h. Bacterial and fungal counts were expressed as colonyforming units per gram of sample (CFU/g). The inoculum density of the core microorganisms in the artificial starter was determined by the addition amount of JIUYAO in winemaking (0.002 g/g steamed rice), the TVC of bacteria and fungi in JIUYAO and the abundance of these microorganisms at the genus level of JIUYAO, as determined by HTS (Table 1). To ensure the sample had the ability to produce ethanol, Saccharomyces cerevisiae was also added to the artificial starters with an inoculum density of 2.0 × 104 CFU/g. Finally, the actual inoculum density of the core microorganisms was adjusted according to their fermentation abilities and aroma-producing characteristics during the winemaking process. The artificial starter was then used to make Huangjiu. The steamed rice was prepared as mentioned above, and fermentation was initiated by adding the artificial starter according to the inoculum density of each strain. After two weeks of post-fermentation, the alcohol content detection and E-nose analysis was used to compare these samples with those fermented with traditional starters.

3.2. Alpha diversity analysis of JIUYAO samples from different wineries The QIIME platform was used to compare the alpha diversities of the microbes from different wineries, including the Shannon, Chao1, Simpson, and ACE indices (Fig. 1). For the bacteria, the alpha diversity of the samples collected from the Fuquan winery yielded the highest Shannon, Chao1, Simpson, and ACE index values. These results imply that the Fuquan JIUYAO had the highest microbial diversity and uniformity. For the fungi, the Chao1 and ACE indices showed that the JIUYAO samples from the Dongfang winery had greater fungal diversity and uniformity than those of the other five JIUYAO samples. The Chao1 and ACE index values for the Fuquan and Disan wineries were slightly lower, whereas the Simpson and Shannon index values were higher, implying that the fungal community in the Fuquan and Disan sample had higher species evenness and higher alpha diversity. 3.3. Microbial composition and core microbiota of the JIUYAO samples We quantified the predominant bacteria in the JIUYAO samples and found that the quantities of Pediococcus (39.20%), Enterobacter (16.06%), Weissella (8.83%), Klebsiella (5.04%), Escherichia-Shigella (3.64%), Pelomonas (1.65%), Cronobacter (1.53%), Ochrobactrum (1.35%), and Ralstonia (1.04%) all exceeded 1% (Fig. 2A). Among them, Pediococcus was the most abundant genus in the sample. For the fungi, the top 10 most abundant fungi were Rhizopus, Saccharomycopsis, Aspergillus, Simplicillium, Gibberella, Fusarium, Chaetomium, Wickerhamomyces, Maiassezia, and Alternaria. Among them, the dominant genera were Rhizopus (36.08%) and Saccharomycopsis (34.08%). A Spearman's rank correlation analysis was used to determine correlations among the core microbiota (Fig. 2B). A negative correlation was 3

Food Microbiology 86 (2020) 103326

C. Chen, et al.

Table 1 The core microorganisms isolated in JIUYAO and their inoculum densities as artificial starters in winemaking. Microflora In JIUYAO

Concentrationa （CFU/g）

Strain

Abundanceb （%）

Calculated Inoculumc (CFU/g)

Adjusted Inoculumd (CFU/g)

LAB

1.02 × 107

39.2 8.83

Yeast Mold

1.28 × 107 1.36 × 106

Pediococcus pentosaceus Weissella cibaria Weissella confusa Saccharomycopsis fibuligera Rhizopus arrhizus

8.0 × 103 1.8 × 103 1.8 × 103 8.7 × 103 9.8 × 102

8.6 × 103 2.2 × 103 2.2 × 103 9.6 × 103 10.6 × 102

34.08 36.08

a

The concentration of LAB, yeast, and mold in JIUYAO measured by TVC on agar plates and the addition of JIUYAO in winemaking (0.002 g/g steamed rice). The abundance of these core microorganisms at the genus level in JIUYAO as determined by HTS. c The calculated inoculum density of these microorganisms in the artificial starter was determined by the addition of JIUYAO during the winemaking (0.002 g/g steamed rice), the viable count of bacteria and fungi in JIUYAO, and the abundance of core microorganisms at the genus level in JIUYAO as determined by HTS. For example, the inoculum density of Pediococcus pentosaceus was calculated as follows: inoculum (8.0 × 10cCFU/g) = the concentration of LAB in JIUYAO (1.02 × 107 CFU/g) × the abundance of Pediococcus pentosaceus in the bacteria of JIUYAO (39.2%) × the addition of JIUYAO in winemaking (0.002 g/g steamed rice). d The inoculum density was adjusted by the fermentation and aroma-producing characteristics of the core microorganisms. b

Bacillis, and Pantoea were found at higher levels in the samples from the Small and Dongfang wineries than in the other samples. In contrast, the amounts of Cronobacter, Enterobacter, Klebsiella, and Escherichia-Shigella in the samples from these two wineries were significantly lower than in the other samples. For the fungi, Wicherhamomyces was more abundant in the Fuquan winery sample than in other samples, whereas Mucor was present in higher levels in the Small winery sample than in the other samples.

found between Pediococcus and other bacteria. For the fungi, a positive correlation was found between Rhizopus and Aspergillus, and a negative correlation was found between Rhizopus and Saccharomycopsis. 3.4. Beta diversity in the JIUYAO samples To analyze the beta diversities of the bacteria and fungi in the JIUYAO samples, the Weight UniFrac and Unweight UniFrac distances of the JIUYAO samples collected from different wineries were compared. As shown in Fig. 3, the sites that represent the bacterial communities of the Small winery and Dongfang winery are clearly separated from the other wineries. As shown in Supplementary Table S3, the structures of the bacterial communities in the Small and Dongfang wineries were significantly different from those of the Xianxiang, Fuquan, and Disan wineries (p < 0.0001). The bacterial communities of the Tapai, Xianxiang, Fuquan, and Disan wineries were highly mixed, and not significantly different from each other. The community structure of the fungi in the JIUYAO samples was less diverse than that of bacteria, and the degree of mixing of each sample point was high. As shown in Supplementary Table S4, the fungal community structure of the Tapai winery was significantly different from that of the Small (p < 0.05) and Xianxiang wineries (p < 0.0001). To further investigate the differences at the genus level in the JIUYAO samples, the differences in bacteria (Fig. 4A) and fungi (Fig. 4B) at the genus level in the different samples were identified. For the bacteria, Weissella,

3.5. The predicted metabolic pathways in JIUYAO and their correlation with core microbiota To better understand the important roles of the microbiota present in the JIUYAO samples, the levels of various metabolic pathways among the different types of JIUYAO samples were predicted and compared. As shown in Table S5, metabolic pathways were abundant in JIUYAO samples from Shaoxing, implying that microbial metabolism in JIUYAO tended to be vigorous. Among these active metabolic pathways, membrane transport, carbohydrate metabolism and amino acid metabolism were the most active in all JIUYAO samples. Meanwhile, the correlation between core microbiota and metabolic pathways was explored using Spearman's rank correlation analysis (Fig. 5). Positive correlations were found between membrane transport and Rhizopus, Saccharomycopsis, Aspergillus, Pediococcus and Weissella; between amino acid metabolism and Rhizopus, Saccharomycopsis, Wickerhamomyces and

Fig. 1. Differences in the microbial alpha diversities of the JIUYAO samples. a, b, e, f are respectively the Simpson, Shannon, Chao1, and ACE indices of alpha diversity in bacterial communities. c, d, g, h are respectively the Simpson, Shannon, Chao1, and ACE indices of alpha diversity in fungal communities. A, B, C, D, E, F are JIUYAO samples from Tapai, Small, Dongfang, Xianheng, Fuquan, and Disan wineries, respectively. 4

Food Microbiology 86 (2020) 103326

C. Chen, et al.

Fig. 2. Composition of core microbiota in the JIUYAO samples. (A) Box-plots showing the predominant bacteria as quantified by q-PCR. (B) Correlation matrix showing the Spearman's rank correlations among the nine core species. The Spearman's rank correlation coefficients ranged from 1.0 to −1.0, indicating a range from strongly positive correlations to strongly negative correlations. (C) Box-plots showing the predominant fungi as quantified by q-PCR. (D) Correlation matrix showing the Spearman's rank correlations among the 10 core species. The Spearman's rank correlation coefficients ranged from 1.0 to −1.0, indicating a range from strongly positive correlations to strongly negative correlations. The color of the circles denotes the nature of the correlation, with 1 indicating a perfect positive correlation (dark blue) and −1 indicating a perfect negative correlation (dark red). Strong correlations are indicated by darker colored circles, whereas weak correlations are indicated by lighter colored circles. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

3.7. Correlation analysis of core microbiota and major volatile flavor compounds

Weissella; and between carbohydrate metabolism and Rhizopus, Saccharomycopsis, Pediococcus and Weissella.

The volatile flavor compounds in six Shaoxing-jiu samples fermented with JIUYAO were analyzed using the SPME-GCMS approach (Table S6). The amount of each compound and its odor threshold value were used to measure the contributions of each volatile compound to the samples (Zhu et al., 2016). According to the results of Guth (1997), odor-activity values (OAVs) greater than 1 indicate contributions to a sample's aroma. As summarized in Table 2, 15 volatile compounds with OAVs ≥1 were identified in the 6 samples, including 8 esters, 4 alcohols, 2 acids and 1 aldehyde. Aromatic esters and alcohols are the most common and important groups of volatile compounds and they determine the flavor of Huangjiu (Lytra et al., 2013; Pineau et al., 2009). The top abundant alcohols in Shaoxing-jiu are phenylethanol, 1-octen3-ol, isobutanol, and isopentanol. In addition, the principal aromatic esters found in Shaoxing-jiu were ethyl acetate, isoamyl acetate, ethyl caprylate, and ethyl caproate. These results are consistent with studies of the volatile flavor substances in Huangjiu (Xu et al., 2018). The relationships among the 15 major volatile components, 9 representative bacterial taxa and 10 representative fungal taxa of the six JIUYAO samples were explored using the PLS-DA algorithm and

3.6. Correlation analysis of core microbiota and fermentation activities Correlations between core microbiota, the saccharifying power of the JIUYAO starter, and the alcohol level and acidity of the resultant Huangjiu were explored using Spearman's rank correlation coefficient analysis. As shown in Fig. 6, of the nine core bacteria, Pediococcus was positively associated with saccharifying power and the production of alcohol and acid. The production of acid was positively correlated with Weissella. Among the ten core fungi, a generally positive correlation was observed between Rhizopus and three fermentation activities indicators (saccharifying power and the production capacity of alcohol and acid). Saccharifying power was positively correlated with Saccharomycopsis. In addition, alcohol production was positively correlated with Enterobacter and Klebsiella. Saccharifying power was positively correlated with Aspergillus and Gibberella. Acid production was positively correlated with Pelomonas, Ochrobactrumi, and Ralstonia.

5

Food Microbiology 86 (2020) 103326

C. Chen, et al.

Fig. 3. Microbial beta diversity of the JIUYAO samples. (A) beta diversity of bacteria based on Unweighted UniFrac distance. (B) beta diversity of fungi based on Unweighted UniFrac distance. (C) beta diversity of bacteria based on Weighted UniFrac distance. (D) beta diversity of fungi based on Weighted UniFrac distance.

microbes, fermentation activities, and volatile compounds, four key core microbes were identified: Pediococcus, Weissella, Rhizopus, and Saccharomycopsis.

visualized using R software. The correlations among the three datasets were defined using a correlation cut-off of 0.9, as shown in Fig. 7. Saccharomycopsis was positively correlated with phenylethanol and ethyl butyrate; Rhizopus was positively correlated with ethyl propionate, ethyl laurate, and ethyl butyrate; and Pedioccoccus was positively correlated with ethyl laurate and acetic acid. Weissella was positively correlated with decanoic acid and isopentanol. Decanoic acid was negatively correlated with Klebsiella and Enterobacter. Ethyl laurate was positively correlated with Pedioccoccus and Saccharomycopsis. From these results, it can be deduced that only a few microbes play a key role in the formation of volatile compounds in Shaoxing-jiu. Based on the analysis of microbial diversity and correlations between representative

3.8. Sensory evaluation and its correlation with major volatile flavor compounds The statistical results for the eight aroma attributes were analyzed by ANOVA with Duncan's multiple range test (p < 0.05) (Table S7). The Tapai wine sample had the lowest scores for the sweet, alcoholic, and fruity attributes. The Small wine sample had the lowest scores for the ester attribute. The Dongfang wine sample had the highest scores 6

Food Microbiology 86 (2020) 103326

C. Chen, et al.

Fig. 4. Microbiological diversity at the genus level of the JIUYAO samples. (a) Bacteria (b) Fungi.

Fig. 5. Correlation analysis of core microbiota and metabolic pathways. 7

Food Microbiology 86 (2020) 103326

C. Chen, et al.

samples fermented with the artificial starter with Saccharomyces cerevisiae was comparable to that of Huangjiu fermented with JIUYAO (Table S8). Therefore, an artificial starter for winemaking was created from five isolated representative core microorganisms (Pediococcus pentosaceus, Weissella cibaria, Weissella confusa, Saccharomycopsis fibuligera and Rhizopus arrhizus) and Saccharomyces cerevisiae. The inoculum density of these microorganisms was determined by the TVC of the bacteria and fungi in JIUYAO, the amount of JIUYAO used in winemaking, and the abundance of these microorganisms at the genus level in the JIUYAO as investigated by HTS (Table 2). Huangjiu samples fermented with the artificial starter were compared to those fermented with traditional JIUYAO. The alterations in acidity and alcohol content followed the same trends during fermentation (data not shown), and the fermentation period of Huangjiu fermented with the artificial starter (15–16 days) was comparable to that of Huangjiu fermented with JIUYAO (14–15 days). At the end of fermentation, the flavor profiles of the products were compared using the E-nose. As shown in Fig. 9, most of the responses were not significantly different for Huangjiu fermented with an artificial starter and that fermented with traditional JIUYAO, expect for the response values of the first and second sensors. These results suggest that the products had similar flavors, thus demonstrating the feasibility of using these core microbes in industrial winemaking. 4. Discussion JIUYAO is used as the fermentation starter in Shaoxing-jiu. It contains numerous microorganisms, which determine the characteristics of Shaoxing-jiu (Hong et al., 2016; Yang et al., 2017). However, until now, the microbial composition of JIUYAO used in Shaoxing-jiu has been poorly characterized. Furthermore, JIUYAO is not stable and uniform in quality among different batches, which results in uncontrolled fermentation processes and inconsistent taste and flavor (Liu et al., 2018). Hence, this study explored the core microbes in JIUYAO and the relationships between the microbial composition, fermentation activities and volatile compounds of the resultant Huangjiu. Based on these results, an artificial starter consisting of a representative selection of bacteria and fungi was made and used to make Huangjiu. Following HTS analysis, we investigated the bacterial and fungal profiles of these starters. The analysis of the bacterial communities demonstrated that the microbial profiles of the JIUYAO varied among wineries; the samples collected from Fuquan had the best alpha diversity, yielding the highest Shannon, Chao1, Simpson, and ACE index values. Based on differences in bacterial composition, the samples from the Small and Dongfang winery can be grouped together, and the Xiansiang, Fuquan, and Disan wineries form another group. Geographically, the Small and Dongfang wineries are located in the Hutang region, whereas the Xianxiang, Fuquan, and Disan wineries are located outside this region. Hence, we deduce that the microbial composition of JIUYAO is influenced by geographical features. Moreover, we confirmed that Pediococcus was the most abundant bacteria genus in all of the samples, followed by Enterobacter, Weissella, Klebsiella, Escherichia-Shigella, Pelomonas, Cronobacter, Ochrobactrum, and Ralstonia. A noteworthy feature of the bacterial profile in JIUYAO was LAB; they were primarily composed of Pediococcus and Weissella, which were detected in all of the samples, although Weissella was more abundant in the Dongfang sample. Although the microbial genera and species of starters used in Chinese rice wine production are similar, their relative abundance in the bacterial compositions differed. LAB are predominant in JIUYAO, whereas the relative abundance of LAB in other Chinese wine starters is not high; Bacillus is the dominant genus in Chinese sweet wine starters and hongqu starters (Huang et al., 2018; Lv et al., 2012; Cai et al., 2018). In addition to the bacteria, the fungi in a starter also play a major role in winemaking. The fungal composition of the JIUYAO samples had a higher degree of confoundment than that of the bacteria. The

Fig. 6. Correlation between core microbiota and fermentation activities, including alcohol production capacity, acid production capacity and saccharification.

for the sweet, ester, caramel and sauce attributes. The Xianheng wine sample had the strongest sour characteristic, but the weakest caramel and sauce aroma. The Disan wine sample had the highest scores for the alcoholic attribute, but the weakest sour and qu aroma. To determine the contributions of volatile components to the sensory quality of Shaoxing-jiu, the relationships between sensory attributes and volatile compounds were studied using the PLSR model. The 15 key aroma compounds were specified as the X-matrix and the sensory attributes were specified as the Y-matrix in the model. As shown in Fig. 8, the ester attribute was found to be associated with ethyl butyrate, ethyl laurate, ethyl decanoate and ethyl caprylate, and the fruit and sour attributes were correlated with several key aroma compounds, including phenylethanol, decanoic acid and ethyl caproate. 3.9. Fermentation of Huangjiu with an artificial starter To verify the role of the core microorganisms in the fermentation of Huangjiu, the representative core bacteria and fungi in JIUYAO (Pediococcus, Weissella, Rhizopus, and Saccharomycopsis) were screened and isolated. A preliminary experiment showed that an artificial starter consisting of these four core microbes resulted in a longer fermentation period than the samples fermented with JIUYAO, probably due to their low ethanol-producing abilities (Table S8). As Saccharomyces cerevisiae can effectively metabolize sugars and other substrates to alcohol, it was included in our artificial starter. The ethanol content of Huangjiu 8

Food Microbiology 86 (2020) 103326

C. Chen, et al.

Table 2 OAVs of volatile compounds detected in Shaoxing-jiu samples. OAV No

Compounds

Aa

B

C

D

E

F

G

Tc (ug/kg)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

phenylethanol ethyl caprylate ethyldecanoate isoamyl acetate 1-octen-3-ol ethyl caproate ethyl butyrate ethylpropionate ethyl acetate furfural ethyl laurate isopentanol isobutanol acetic acid decanoic acid

111 5 2 208 5 <1 1 1 114 1 <1 782 12 <1 1

111 6 3 192 5 1 1 1 115 2 <1 586 5 <1 1

197 55 41 530 5 2 22 1 134 4 11 913 10 1 6

181 21 10 562 6 1 3 2 238 −b 2 1163 12 1 3

126 19 7 566 5 1 3 2 283 3 1 754 6 1 <1

159 11 5 502 4 1 1 1 112

216 7 3 298 11 <1 1 1 123 2 <1 689 5 <1 2

60 200 530 2 2.7 530 59 29 5 8 500 6.1 33 10000 70

1 841 8 1 <1

a A, B, C, D, E, F are Shaoxing-jiu samples fermented with JIUYAO from Tapai, Small, Dongfang, Xianheng, Fuquan and Disan wineries, respectively. G is Huangjiu sample fermented with the artificial starter made in the present study. b The OAV was not calculated in the sample. c The detection threshold was drawn from the literature (Gemert, 2011).

Fig. 7. Correlation analysis of the major volatile components and representative microbiota using PLS-DA modeling. Red lines in the circle represent positive correlations and black lines represent negative correlations between volatile components and microbiota. The green blocks on the circle represent the 15 major volatile components; the red blocks on the circle represent the 9 genus-level representative bacterial taxa; the purple blocks represent the 10 genus-level representative fungal taxa. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

starters are characterized mainly by the occurrence of Monascus, Aspergillus, and Candida, while Rhizopus and Saccharomycopsis do not dominate. This indicates that there are differences among the starters used for Chinese rice wine production and demonstrates the unique features of JIUYAO (Cai et al., 2018; Huang et al., 2019). In this study, we predicted the metabolic pathways related to specific microbes in the JIUYAO samples from Shaoxing, and found an enrichment in some metabolic pathways, mainly membrane transport, carbohydrate metabolism and amino acid metabolism. We studied metabolic features and the core microbiota in the JIUYAO samples using correlation analysis, which revealed that four representative core microorganisms (Pediococcus, Weissella, Saccharomycopsis and Rhizopus) are the major contributors to the three most abundant metabolic pathways. Lennartsson and Taherzadeh (2014) found that Rhizopus is

abundance of fungal communities in Dongfang was significantly higher than in the other five JIUYAO samples. Rhizopus and Saccharomycopsis were the predominant genera in all of the JIUYAO samples. Although Rhizopus and Saccharomycopsis are also found in other Chinese starters, they are not the most abundant fungal genus. For example, Chinese sweet rice wine starters are mainly composed of Rhizopus, followed by Aspergillus, Saccharomyces, Mucor and Saccharomycopsis; among them, Saccharomycopsis is not the most abundant fungi. Chinese hongqu

Fig. 8. PLSR regression correlation loading plots of the sensory attributes of Huangjiu samples and the volatile compounds identified by GC-MS. The model was derived from 8 sensory attributes as the X-matrix and 15 key volatile compounds data as the Y-matrix. Elipses represent r2 = 0.5 and 1.0, respectively. 9

Food Microbiology 86 (2020) 103326

C. Chen, et al.

great contributions to saccharifying starch during fermentation. Currently, using a well-defined artificial starter culture could be an effective approach to improving the quality of final wine products (Lv et al., 2015). For this reason, this study established a basis for an optimal artificial starter for the manufacture of high-quality Shaoxing-jiu. We found that Pediococcus, Weissella, Saccharomycopsis, and Rhizopus were the core functional microbes of JIUYAO, and are responsible for the production of the main fermentation activities and volatile flavor. As these four microbes were found to be poor producers of ethanol, S. cerevisiae was included in the artificial starter due to its ability to produce ethanol. Therefore, six of the representative microbes species in JIUYAO were selected and isolated, and then used in mixed artificial starters that can produce high-quality and flavorful Huangjiu. The Enose analysis showed that the volatile profile of Huangjiu fermented with the artificial starter was not significantly different from that of the products fermented with a traditional starter. These results suggest that these strains contribute significantly to the fermentation activities and flavor of Shaoxing-jiu. Together, the results confirm the feasibility of using this artificial starter as a substitute for JIUYAO and provide a reference for industrial winemaking.

Fig. 9. Comparison of radar charts of E-nose data of Huangjiu sample fermented with the artificial starter and those fermented with JIUYAO. A, B, C, D, E, F are Huangjiu samples fermented with JIUYAO from Tapai, Small, Dongfang, Xianheng, Fuquan, and Disan wineries, respectively.

5. Conclusion Metagenomic analysis was used to investigate the diversity of microbiota in JIUYAO from different Shaoxing wineries and identify the core microbiota. Unlike in other Chinese wine starters, the core bacteria in JIUYAO were Pediococcus and Weissella, and the core fungi were Saccharomycopsis and Rhizopus. This study also established comprehensive information about these core microbes and their role in fermentation and production of volatile compounds, which are closely related to the quality and flavor of Shaoxing-jiu. These results were used to develop an artificial starter that can be used to improve the fermentation and aromatic quality of Shaoxing-jiu. It is clear that the interactions between LAB, yeasts and molds are the most important factors in producing the distinctive flavor of Shaoxing-jiu although the details still require further study.

associated with multiple pathways of carbohydrate metabolism and amino acid metabolism. Son et al. (2018) found the abundant metabolites of Saccharomycopsis fibuligera were esters and alcohols during Huangjiu fermentation, which correlated with high enzymatic activities of membrane transport enzymes and carbohydrate metabolism. These results are in excellent agreement with our study. To identify the core functional microbiota responsible for fermentation and volatile flavor compounds, correlation modeling was used to reveal the potential correlations between core microorganisms, fermentation, volatile flavor compounds and sensory attributes in the traditional brewing of Shaoxing-jiu. Pediococcus had a significant positive correlation with three fermentation activities indicators (acid production, alcohol production and saccharifying power) and the volatile compound ethyl laurate. Weissella was positively correlated with acid production, decanoic acid and isopentanol, implying it contributes to the sour and qu aroma attributes. Weissella and Pediococcus have wide applications in fermented foods as they can increase the content of organic acids, short-chain fatty acids and esters during food fermentation (Kamboj et al., 2015; Porto et al., 2017). These results support the conclusion that Weissella and Pediococcus enhance the production of volatile acids, alcohols and esters in Shaoxing-jiu. Rhizopus was significantly positively correlated with acid production, alcohol production, saccharifying power and the volatile compounds of ethyl propionate, isoamyl acetate, and ethyl acetate. Isoamyl acetate was found to be associated with the alcoholic attribute and ethyl acetate was related to the sour and qu aroma attributes, in line with the results of Yang et al. (2017). Rhizopus has been commonly used in fermented foods such as grains, fruits, and legumes (Ling et al., 2018), particularly in winemaking. Rhizopus has the ability to produce amylase and glucoamylase, which can decompose starch into glucose and generate flavoring substances such as lactic acid and alcohols. This may explain the role of Rhizopus in ester production during the fermentation of Shaoxing-jiu. Yeasts also play a critical role in determining fermentation speed, wine flavor and other wine qualities (Fleet, 2003). In this study, Saccharomycopsis was positively correlated with phenylethanol, ethyl butyrate and saccharifying power, which has the potential to greatly affect the sauce and ester attributes of Shaoxing-jiu. Recently, due to the contribution of non-saccharomyces (mainly Saccharomycopsis) to the final aroma and taste of wines, researchers have paid more attention to their role in winemaking (Nuñez-Guerrero et al., 2016). Although Saccharomycopsis is not an effective ethanol producer, it can secrete amylase, protease and β-glucosidase, which can make

Additional information Nucleotide sequence accession numbers: The sequence data reported in this paper have been deposited in the NCBI database (Accession Number: PRJNA412680). Conflicts of interest The authors declare no conflict of interests. Acknowledgements The authors thank the Natural Science Foundation of Shanghai (No. 17ZR1429500) for financial support, the Shanghai Personal Biotechnology Co., Ltd. for sequencing and Jiachao Zhang in Hainan University for assistance with data analysis. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.fm.2019.103326. References Cai, H., Zhang, T., Zhang, Q., Luo, J., Cai, C., Mao, J., 2018. Microbial diversity and chemical analysis of the starters used in traditional Chinese sweet rice wine. Food Microbiol. 73, 319. Caporaso, J.G., Kuczynski, J., Stombaugh, J., 2010. QIIME allows analysis of highthroughput community sequencing data. Nat. Methods 7 (5), 335. Chen, S., Xu, Y., 2012. The influence of yeast strains on the volatile flavour compounds of

10

Food Microbiology 86 (2020) 103326

C. Chen, et al.

Lytra, G., Tempere, S., Le Floch, A., de Revel, G., Barbe, J.C., 2013. Study of sensory interactions among red wine fruity esters in a model solution. J. Agric. Food Chem. 61, 8504–8513. Nuñez-Guerrero, M.E., Páez-Lerma, J.B., Rutiaga-Quiñones, O.M., González-Herrera, S.M., Soto-Cruz, N.O., 2016. Performance of mixtures of Saccharomyces and nonSaccharomyces native yeasts during alcoholic fermentation of Agave duranguensis juice. Food Microbiol. 54, 91–97. Pineau, B., Barbe, J.C., Van Leeuwen, C., Dubourdieu, D., 2009. Examples of perceptive interactions involved in specific "red-" and "black-berry" aromas in red wines. J. Agric. Food Chem. 57, 3702–3708. Porto, M.C., Kuniyoshi, T.M., Azevedo, P.O., Vitolo, M., Oliveira, R.P., 2017. Pediococcus spp.: an important genus of lactic acid bacteria and pediocin producers. Biotechnol. Adv. 35, 361–374. Price, M.N., Dehal, P.S., Arkin, A.P., 2009. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26 (7), 1641–1650. Shen, F., Ying, Y.B., Li, B.B., Zheng, Y.F., Hu, J.G., 2011. Prediction of sugars and acids in Chinese rice wine by mid-infrared spectroscopy. Food Res. Int. 445, 1521–1527. Son, E.Y., Lee, S.M., Kim, M., Seo, J.A., Kim, Y.S., 2018. Comparison of volatile and nonvolatile metabolites in rice wine fermented by Koji inoculated with Saccharomycopsis fibuligera and Aspergillus oryzae. Food Res. Int. 109, 596–605. Tufariello, M., Maiorano, G., Rampino, P., Spano, F., Grieco, C., Perrotta, V., Capozzi, F., 2019. Selection of an autochthonous yeast starter culture for industrial production of Primitivo “Gioia del Colle” PDO/DOC in Apulia (Southern Italy). LWT - Food Sci. Technol. (Lebensmittel-Wissenschaft -Technol.) 99, 188–196. Wang, F.R., 2005. Wine Analysis and Testing. Chemical Industry Press, Beijing. Wang, P.X., Mao, J., Meng, X.Y., Li, X.Z., Liu, Y.Y., Feng, H., 2014. Changes in flavour characteristics and bacterial diversity during the traditional fermentation of Chinese rice wines from Shaoxing region. Food Control 44, 58–63. Welke, J.E., Zanus, M., Lazzarotto, M., Alcaraz Zini, C., 2014. Quantitative analysis of headspace volatile compounds using comprehensive two-dimensional gas chromatography and their contribution to the aroma of Chardonnay wine. Food Res. Int. 59, 85–99. Xie, G.F., Li, W.J., Jian, L., Yu, C., Hua, F., Zou, H.J., Hu, Z.M., 2012. Isolation and identification of representative fungi from Shaoxing rice wine Wheat Qu using a polyphasic approach of culture-based and molecular-based methods. J. Inst. Brew. 113, 272–279. Xu, E., Long, J., Wu, Z., Hong, Y.L., Fang, W., Xue, M.X., Zheng, Y.J., Ai, Q.J., 2015. Characterization of volatile flavor compounds in Chinese rice wine fermented from enzymatic extruded rice[J]. J. Food Sci. 80 (7), C1476–C1489. Xu, J., Wu, H., Wang, Z., Zheng, F., Lu, X., Li, Z., Ren, Q., 2018. Microbial dynamics and metabolite changes in Chinese Rice Wine fermentation from sorghum with different tannin content. Sci. Rep. 8, 4639. Yang, J.J., Xia, Y.J., Wang, G.Q., Yu, J.S., Ai, J.S., 2017. Effect of mixed yeast starter on volatile flavor compounds in Chinese rice wine during different brewing stages. LWT - Food Sci. Technol. (Lebensmittel-Wissenschaft -Technol.) 78, 373–381. Yang, Y., Xia, Y., Lin, X., Wang, G., Zhang, H., Xiong, Z., Yu, H., Yu, J., Ai, L., 2018. Improvement of flavor profiles in Chinese rice wine by creating fermenting yeast with superior ethanol tolerance and fermentation activity. Food Res. Int. 108, 83–92. Yu, H.Y., Xie, T., Xie, J.R., Ai, L.Z., Tian, H.X., 2019. Characterization of key aroma compounds in Chinese rice wine using gas chromatography–mass spectrometry and gas chromatography–olfactometry. Food Chem. S0308–8146 (19) 30548-5. Zapala, M.A., Schork, N.J., 2006. Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proc. Natl. Acad. Sci. U.S.A. 103, 19430–19435. Zhu, J.C., Chen, F., Wang, L., Niu, Y.W., Chen, H., Wang, H., Xiao, Z.B., 2016. Characterization of the key aroma volatile compounds in Cranberry (Vaccinium macrocarpon Ait.) using gas chromatography-olfactometry (GC-O) and odor activity value (OAV). J. Agric. Food Chem. 64, 4990–4999.

Chinese rice wine. J. Inst. Brew. 116, 190–196. Chen, S., Xu, Y., Qian, M.C., 2013a. Aroma characterization of Chinese rice wine by gas chromatography-olfactometry, chemical quantitative analysis, and aroma reconstitution. J. Agric. Food Chem. 61, 11295–11302. Chen, S., Wang, D., Xu, Y., 2013b. Characterization of odor-active compounds in sweettype Chinese rice wine by aroma extract dilution analysis with special emphasis on sotolon. J. Agric. Food Chem. 61, 9712–9718. Chen, S., Xu, Y., Qian, M.C., 2013c. Aroma characterization of Chinese rice wine by gas chromatography–olfactometry, chemical quantitative analysis, and aroma reconstitution. J. Agric. Food Chem. 61 (47), 11295–11302. Cole, J.R., Chai, B., Farris, R.J., Wang, Q., Kulam-Syed-Mohideen, A.S., Mcgarrell, D.M., Bandela, A.M., Cardenas, E., Garrity, G.M., Tiedje, J.M., 2007. The ribosomal database project (RDP-II): introducing myRDP space and quality controlled public data. Nucleic Acids Res. 35, D169–D172. Crowell, E.A., Ough, C.S., 1979. A modified procedure for alcohol determination by dichromate oxidation. Am. J. Enol. Vitic. 61–63. Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. Fleet, G.H., 2003. Yeast interactions and wine flavour. Int. J. Food Microbiol. 86, 11–22. Gemert, L.J.V., 2011. Odour threshold values in water in odour thresholds. In: Punter, Oliemans, Partners, B.V. (Eds.), Compilations of Odour Threshold Values in Air, Water and Other Media, pp. 157–289 The Netherlands. Guth, H., 1997. Identification of character impact odorants of different white wine varieties. J. Agric. Food Chem. 45 (8), 3022–3026. Hong, X., Chen, J., Liu, L., Wu, H., Tan, H., Xie, G., Xu, Q., Zou, H., Yu, W., Wang, L., Qin, N., 2016. Metagenomic sequencing reveals the relationship between microbiota composition and quality of Chinese rice wine. Sci. Rep. 6. Huang, Z.R., Hong, J.L., Xu, J.X., Li, L., Guo, W.L., Pan, Y.Y., Chen, S.J., Bai, W.D., Rao, P.F., Ni, L., Zhao, L.N., Liu, B., Lv, X.C., 2018. Exploring core functional microbiota responsible for the production of volatile flavour during the traditional brewing of Wuyi Hong Qu glutinous rice wine. Food Microbiol. 76, 487–496. Huang, Z.R., Guo, W.L., Zhou, W.B., Li, L., Xu, J.X., Hong, J.L., Liu, H.P., Zeng, F., Bai, W.D., Liu, B., Ni, L., Rao, P.F., Lv, X.C., 2019. Microbial communities and volatile metabolites in different traditional fermentation starters used for Hong Qu glutinous rice wine. Food Res. Int. 121, 593–603. Jiao, A., Xu, X., Jin, Z., 2017. Research progress on the brewing techniques of new-type rice wine. Food Chem. 215, 508–515. Kamboj, K., Vasquez, A., Balada-Llasat, J.M., 2015. Identification and significance of weissella species infections. Front. Microbiol. 6, 1204. Lennartsson, P.R., Taherzadeh, M.J., 2014. Rhizopus. In: Batt, C.A., Tortorello, M.L. (Eds.), Encyclopedia of Food Microbiology. Academic press., London, pp. 284–290. Ling, D.Y., Xiao, J., Timothy, Y.J., Jun, Z.Q., Xiao, Y.L., 2018. Relationship between saccharifying capacity and isolation sources for strains of the Rhizopus arrhizus complex. Mycoscience 59 (5), 409–414. Liu, Z., Wang, Z., Lv, X.C., Zhu, X., Chen, L., Ni, L., 2018. Comparison study of the volatile profiles and microbial communities of Wuyi Qu and Gutian Qu, two major types of traditional fermentation starters of Hong Qu glutinous rice wine. Food Microbiol. 69, 105–115. Luo, T., Fan, W., Yan, X., 2012. Characterization of volatile and semi-volatile compounds in Chinese rice wines by headspace solid phase microextraction followed by gas chromatography mass spectrometry. J. Inst. Brew. 114, 172–179. 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 PCR-mediated DGGE. Food Control 28, 426–434. Lv, X.C., Cai, Q.Q., Ke, X.X., Chen, F., Rao, P.F., Ni, L., 2015. Characterization of fungal community and dynamics during the traditional brewing of Wuyi Hong Qu glutinous rice wine by means ofmultiple culture-independent methods. Food Control 54, 231–239.

11