Skip to main content
NCBI home page
Journal of Food Science and Technology logoLink to Journal of Food Science and Technology
. 2021 Jan 24;59(1):34–45. doi: 10.1007/s13197-021-04976-y

Study on taste characteristics and microbial communities in Pingwu Fuzhuan brick tea and the correlation between microbiota composition and chemical metabolites

Mao-Yun Li 1, Yue Xiao 1, Kai Zhong 1, Hong Gao 1,✉
PMCID: PMC8758844  PMID: 35068549

Abstract

Pingwu Fuzhuan brick tea (PWT) is considered the “Sichuan western road” border-selling tea. The taste and quality of Fuzhuan brick tea (FBT) is greatly influenced by microorganisms. Considering the dearth of studies on the taste and microbial community of PWT, this study aimed to investigate the taste characteristics using electronic tongue system and microbial community structures using high-throughput sequencing, followed by comparison with FBT from other regions and determining the correlation between microbial communities and chemical compositions. The taste strengths of sweetness, bitterness, umami and astringency in PWT were all at lower level compared to other regions FBT. Regarding microbial diversity, the fungal communities in PWT were distinct from those of other regions FBT in terms of taxonomic composition and abundance. Unclassified_k_Fungi and Aspergillus were the most dominant fungal genera in PWT. Candidatus_Microthrix, norank_f_Saprospiraceae, and norank_c_C10-SB1A were dominant bacterial genera in PWT, only distinct from those in Hunan FBT (HNT). Principal component analysis results showed that fungal or bacterial community structures of PWT and other regions FBT were distinctly different. Correlation analysis revealed important links between the top 50 microbial populations and metabolites.

Supplementary Information

The online version of this article contains supplementary material available at (10.1007/s13197-021-04976-y)

Keywords: Pingwu Fuzhuan brick tea, Taste characteristics, Electronic tongue, High-throughput sequencing, Fungal community, Bacterial community

Introduction

Chinese teas are classified into six categories based on the degree of fermentation: green tea (unfermented), white tea (slightly fermented), yellow tea (lightly fermented), oolong tea (semi-fermented), black tea (fully fermented) and dark tea (post-fermented) (Li et al. 2017). Dark tea, a unique post-fermented tea produced by microbial activities, is popular owing to its special flavor and health benefits including anti-hyperlipidemic, anti-obesity, antimicrobial and antioxidant properties (Zhu et al. 2015). Fuzhuan brick tea (FBT), one of the major brands varieties of dark teas, also called border-selling tea, is one of the special life necessities of the ethnic groups living in the border of southwestern and northwestern China (Xu et al. 2011). The production areas of FBT include Sichuan, Hunan, Shaanxi and Guizhou provinces. In particular, FBT obtained from the Pingwu region of the Sichuan province in China (PWT) is considered the “Sichuan western road” border-selling tea. FBT is produced using primary dark tea as material, and primary dark is produced from the leaves of Camellia sinensis var. sinensis via microbial fermentation (Li et al. 2018a). The manufacturing process of FBT involves steaming, piling, pressing, microbial fermentation and drying, and microbial fermentation is considered to be the key for generating the special flavor and health benefits of FBT (Li et al. 2019b).

The characteristic chemical factors related to taste and flavor of FBT, such as major catechins, volatile aroma compounds and amino acids changed during the manufacturing process of fermentation (Xu et al. 2015; Zhu et al. 2015). We had investigated and compared the chemical characteristics of PWT with those of other regions FBT in a previous report (Li et al. 2019a). In this previous report, determination of taste characteristics in PWT and other FBT is scarce. As microbial fermentation is key contributor to the quality formation of post-fermented tea, the structures and diversity in microbial communities are necessary to investigate.

The taste of FBT is a key contributor to its quality. The taste quality of FBT improves during microbial fermentation due to reduction in coarse astringency and increase in alcoholic taste (Zhu et al. 2015). The electronic tongue sensor system is used for analyzing taste, as it operates via the same mechanism as that of the human tongue. This sensor system has good reproducibility with low detection limits and high sensitivity, compared to the high variability, subjectivity and time consuming nature of human sense of taste (Zhang et al. 2015).

Previously, the microorganisms involved in the post-fermentation process of FBT and their contributions to chemical characteristics have been investigated. Eurotium, Debaryomyces, Aspergillus, Cyberlindnera and Candida were found to be the dominant fungal genera associated with the fermentation of FBT based on culture-dependent and culture-independent methods (Xu et al. 2011; Li et al. 2017). Klebsiella, Pseudomonas, Lactococcus, Stenotrophomonas, Enterococcus, and Bacillus were the dominant bacterial species present in FBT during the fermentation (Li et al. 2019b). In addition, the correlation between bacterial and fungal communities in FBT was analyzed (Rui et al. 2019). It is well known that microbial succession is influenced by the environment and processing conditions. Until now, studies on microbial communities in FBT of different regions and the correlation between metabolites and microbial communities are scarce. However, as some important microbial populations are difficult to culture, traditional culture-dependent microbiological methods cannot be used for analyzing the succession and structure of microbial communities. In addition, molecular biology techniques, such as polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) and terminal restriction fragment length polymorphism (TRFLP), are difficult to provide integrated information regarding microbial community structure due to low throughput and sensitivity. In contrast, high-throughput sequencing technology is used to precisely characterize microbial diversity in food matrixes, as thousands of sequences can be generated to cover the complex microbial communities, including those that are poor in abundance (Wang et al. 2018). Owing to its rapid, sensitive, and comprehensive advantages, high-throughput sequencing has provided new insights regarding microbial communities and diversity in fermented food, such as in dark teas (Li et al. 2017; Mao et al. 2017), vinegar (Wang et al. 2016), sausages (Wang et al. 2019b), and rice wine (Ji et al. 2018).

In this study, we aimed to determine the taste characteristics of PWT and FBT of other regions using the electronic tongue. In addition, the structures and diversity in the fungal and bacterial communities of PWT and FBT of other regions were investigated using Illumina MiSeq sequencing. Furthermore, the correlation between microbial community structure and chemical metabolites was elucidated. Results provide insights into “western road” border-selling tea from the point of taste and microorganisms, and lay a foundation for revealing the reason for quality formation of PWT.

Materials and methods

Sample collection

PWT, Hunan FBT (HNT), Shaanxi FBT (SNT), and Guizhou FBT (GZT) were obtained from Pingwu Xuebaoding Cha Industry Development Co., Ltd (Pingwu county of Sichuan province, China), Hunan Shen’s tea manufacturing Co., Ltd (Anhua county of Hunan, China), Xianyang Jingwei Fu tea Co., Ltd (Qindu district of Xianyang city of Shaanxi province, China) and Guizhou Fanjin tea Industry Co., Ltd (Songtao county of Guizhou province, China), respectively. All tea samples of special grade were produced in April, 2014.

Electronic tongue measurements

The electronic tongue system (TS-5000Z, INSENT, Japan), based on an artificial lipid membrane that consistently responds to taste similar to the human tongue, was used for the measurement of membrane potential, which corresponded to the taste intensity of the tea infusion. Sample preparation of FBT samples and the electronic tongue analysis was conducted as described previously (Xu et al. 2019). Six types of taste sensors, including AAE, CT0, CA0, C00, AE1, and GL1, were used for testing umami, saltiness, sourness, bitterness, astringency and sweetness, respectively. The preparation of reference and washing solutions was performed as described by Zhang et al. (2015).

Sequence processing and community structure analysis

The microorganisms in FBT samples were collected as described previously with some modifications (Mao et al. 2017). Tea leaves were suspended in sterile phosphate buffered saline (PBS) and vortexed twice for 20 min at 180 r/min. Then, the suspension was sonicated for 10 min at 160 W, following which the suspension was centrifuged at 12,000 r/min for 10 min and the sediment was collected for DNA extraction.

Microbial DNA was extracted from FBT samples using the E.Z.N.A.® soil DNA kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer’s protocols. The final DNA concentration and purification were determined using the NanoDrop 2000 UV–vis spectrophotometer (Thermo Scientific, Wilmington, USA), and DNA quality was determined using 1% agarose gel electrophoresis.

Primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) were used to amplify the V3-V4 hypervariable regions of the bacterial 16S rRNA gene, and primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) were used to amplify the fungal ITS1 region using thermocycler PCR system (GeneAmp® 9700, ABI, USA) (Li et al. 2017, 2019b). Fungal and bacterial PCR amplification and sequencing were performed using Illumina Miseq sequencing in Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). The details of the sequencing are summarized in Supplemental materials.

The sequencing data were processed using Trimmomatic and merged by FLASH as the methods Wang et al. (2019a) mentioned. Operational taxonomic units (OTUs) were clustered with 97% similarity cut-off using UPARSE (version 7.1) and the taxonomy of each 16S rRNA and ITS gene sequence was analyzed using the RDP Classifier algorithm against the Silva (Release128 http://www.arb-silva.de) 16S rRNA database and the Unite (Release 7.0 http://unite.ut.ee/index.php) ITS database using confidence threshold of 70%.

The biodiversity indices, Coverage, Ace, Chao1, Shannon, and Simpson index, were calculated to describe microbial diversity using MOTHUR (version v.1.30.1 http://www.mothur.org/wiki/Schloss_SOP#Alpha_diversity). In addition, Venn diagram, rank-abundance curve, rarefaction curve (Sobs and Shannon curves), principal component analysis (PCA), and heatmap analyses were performed using R (version 3.5.1, https://mirrors.tuna.tsinghua.edu.cn/CRAN/).

Results and discussion

Scores of the taste strength in FBT samples

The scores of specific tastes in FBT solutions determined using the electronic tongue are showed in Table S1 (Supplementary materials). Nine taste indicators were investigated, including sourness, bitterness, astringency, umami, saltiness, sweetness, richness, aftertaste of bitterness, and aftertaste of astringency. The taste indicators of richness, aftertaste of bitterness and aftertaste of astringency represented the persistence of umami, bitterness and astringency. The data of the reference solutions were considered scores of tastelessness to simulate the status of the saliva. As shown in Table S1, the scores of sourness and saltiness in FBT solutions were lower than that of the reference solution, which indicated that sourness and saltiness were not tasted in FBT samples. The scores of the taste indicators mentioned above, with the exception of sourness and saltiness in PWT, HNT, SNT, and GZT are shown in a radar plot (Fig. 1). The taste strength of sweetness in PWT was obviously lower than that of HNT, SNT, and GZT. Sugars and certain amino acids such as glycine, L-threonine, L-proline, L-alanine, and L-serine were found to provide sweet taste (Yang et al. 2018). The taste strength of bitterness, umami, and astringency in PWT were also low, especially compared to that of SNT and GZT. Studies indicated that alkaloids such as caffeine, theophylline, and theobromine provided bitter taste in teas, and caffeine was a main contributor to bitterness. Furthermore, flavonoids, catechins, and certain amino acids including L-histidine, L-lysine, L-valine, L-tryptophan, L-arginine, L-tyrosine, L-phenylalanine, L-isoleucine, and L-leucine, contributed to bitter taste in teas (Yang et al. 2018; Zou et al. 2018; Yoshimatsu et al. 2020). L-Theanine, L-glutamic acid, L-glutamine, L-aspartic acid, and L-asparagine contributed to the umami taste (Yang et al. 2018). γ-Aminobutyric acid (GABA), gallic acid, and catechins such as epicatechin gallate (ECG), epigallocatechin (EGC), epicatechin (EC), catechin (C), and epigallocatechin gallate (EGCG) contributed to astringent taste, with catechins being the main source of astringency in teas (Rotzoll et al. 2005; Zou et al. 2018). The taste scores of richness, aftertaste of astringency and aftertaste of bitterness in PWT were comparable to those of FBT of other regions. It was noteworthy that components with different tastes can enhance or suppress each other to affect the taste of tea (Drewnowski 2001). For example, umami constituents can modulate sweetness, and suppress sourness and bitterness in foods (Wang et al. 2020). During manufacturing process of tea, complex of substances with different type of taste affect the taste of tea. For instance, the interactions of polyphenols and protein were found to reduce the bitter taste of tea (Bandyopadhyay et al. 2012).

Fig. 1.

Fig. 1

Open in a new tab

Radar plot for scores of taste strength in FBT samples. Tasteless, reference solutions as no taste; PWT, Pingwu Fuzhuan brick tea. HNT, Hunan Fuzhuan brick tea. SNT, Shaanxi Fuzhuan brick tea. GZT, Guizhou Fuzhuan brick tea

Basic sequence analysis of high-throughput sequencing

Illumina MiSeq sequencing generated a total of 480,558 raw ITS1 sequences with the average length of 237 bp and 289,292 valid 16S rRNA gene sequences with the average length of 437 bp. After merging, filtering, and trimming of raw reads, 125,032 high quality bacterial tags and 239,289 high-quality fungal tags were obtained. As shown in Table 1, an average of 59,822 fungal tags and 31,258 bacterial tags were generated from each sample. All high-quality sequence reads were clustered into 115 fungal OTUs and 1,332 bacterial OTUs based on a 97% sequence similarity level. The detail OTU information is shown in Table S2a and Table S2b (Supplementary materials). The number of bacterial OTUs was higher than that of fungal OTUs, indicating that the number of bacterial species was considerably higher than that of fungal species. However, it is well known that fungi are predominant in FBT (Zhang et al. 2013). The number of fungal OTUs was highest in PWT (105) and lowest in HNT (9). Furthermore, the number of bacterial OTUs in PWT (935) was comparable to that of SNT (926) and GZT (929), but was higher than that in HNT (550). The rank abundance curves indicated that the abundance and evenness of species could be positively related to the width and smoothness of curves, respectively. The bacterial abundance and evenness of PWT, SNT and GZT was higher than that of HNT (Fig. 2b), and the fungal abundance and evenness of PWT was the highest compared to those of other FBT samples (Fig. 2a). The Good’s coverage indices calculated the probability of sequencing of a randomly selected amplicon sequence from a sample (Zhao et al. 2016). As shown in Table 1, the Good’s coverage indices exceeded 99% in all bacterial and fungal sequencing samples, indicating that this sequencing result represented majority of microbiota to evaluate the diversity of fungi and bacteria (Wang et al. 2016). The rarefaction curves based on Sobs index (Fig. S1a, b) and Shannon index (Fig. S1c, d) of fungi and bacteria showed similar pattern prior to reaching the saturation state, indicating that few new species can be identified by increasing the sequencing depth and that sample diversity can be accurately determined based on these sequencing data (Zhao et al. 2016).

Table 1.

The sequence abundance information and alpha diversity index of FBT samples

Group No. of reads No. of OTUs Coverage Ace Chao1 Shannon Simpson
Fungi Bacteria Fungi Bacteria Fungi Bacteria Fungi Bacteria Fungi Bacteria Fungi Bacteria Fungi Bacteria
PWT 80,302 35,264 105 935 0.999838 0.9981 111.7867 961.0918 108.9 969.0154 1.5278 5.5080 0.2559 0.0133
HNT 42,303 37,471 9 550 1 0.999466 9 556.0324 9 560.5556 0.5350 5.0801 0.6722 0.0171
SNT 52,730 25,504 46 926 0.999829 0.996314 52.5492 996.1917 50 979.9630 0.0491 5.3657 0.9897 0.0203
GZT 63,954 26,793 29 929 0.999906 0.995223 33.3463 999.8413 31.1429 1029.3457 0.1682 5.4553 0.9410 0.0130
Open in a new tab

Fig. 2.

Fig. 2

Open in a new tab

Rank abundance curves of fungal (a) and bacterial (b) OTUs derived from each FBT sample. Venn diagrams illustrated of fungal (c) and bacterial (d) OTUs in FBT samples. PWT, Pingwu Fuzhuan brick tea; HNT, Hunan Fuzhuan brick tea; SNT, Shaanxi Fuzhuan brick tea; GZT, Guizhou Fuzhuan brick tea

Analysis of microbial diversity in FBT samples

To compare microbiota species diversity in FBT samples, alpha diversity indices including Chao1, Ace, Shannon and Simpson were computed to evaluate the richness and evenness of species. Chao1 and Ace indexes, were richness indices that estimated the OTU numbers directly according to different algorithms (Zhao et al. 2019). Shannon and Simpson indices are community diversity estimators that indicating the approximate number of species and the evenness of their distributions overall in the samples (Zang et al. 2018). The Shannon index value is positively related to, whereas Simpson index value is negatively related to community diversity (Zhao et al. 2019). As reflected by Ace, Chao1, Shannon, and Simpson indices shown in Table 1, the diversity of bacterial community was considerably higher than that of the fungal community. Based on above estimators, PWT exhibited the highest fungal diversity considering the richness and evenness of all FBT samples. The microbiota richness of HNT was the lowest compared to those of SNT and GZT according to Ace and Chao1 indices, whereas the sampling diversity of HNT was higher than that of SNT and GZT based on Shannon and Simpson indices. Regarding bacterial diversity, PWT, similar to SNT and GZT, showed higher richness than HNT based on Ace and Chao1 estimators, while it showed diversity similar to those of HNT, SNT, and GZT based on Shannon and Simpson indexes. The fungal diversity estimators of HNT, SNT, and GZT were similar to those reported previously (Li et al. 2017; Rui et al. 2019) although PWT showed distinction. The bacterial diversity estimators of the FBT samples in our study were higher than those reported previously (Rui et al. 2019; Li et al. 2019b). The differences in microbiota diversity among FBT samples might be due to different raw materials, microorganisms, and fermentation environments of different origins (Li et al. 2018b).

Taxonomic compositions of fungal and bacterial communities of FBT samples

Venn diagrams were constructed to illustrate the number of unique and shared fungal (Fig. 2c) and bacterial (Fig. 2d) OTUs. Only 2 of 115 fungal OTUs were shared among FBT of four regions. The common fungal OTUs in all FBT samples were OTU7 and OTU8, both of which belonged to genus Aspergillus. In total, 37% fungal OTUs (42/115) present in PWT were also found in SNT, indicating that the fungal species composition of PWT was similar to that of SNT. Regarding bacterial OTUs, 9.46% bacterial OTUs (126/1332) were shared among all FBT samples; 65.47% bacterial OTUs (872/1332) overlapped between PWT and SNT, and 64.56% bacterial OTUs (860/1332) were present both in PWT and SNT. It was noteworthy that 824 bacterial OTUs (61.86%) were shared among PWT, SNT, and GZT.

Within the classified reads analyzed based on Silva database, 5 phyla, 15 classes, 28 orders, 35 families and 49 genera were obtained from ITS1 sequences. The relative abundance of different fungal communities in all samples at phylum, order, and genus levels were analyzed and are shown in Fig. 3a, b, c, respectively. Regarding fungal community composition, PWT was unique compared to the other FBT samples. Ascomycota was the most dominant phylum in HNT, SNT, and GZT, accounting for 99.9% of the fungal population. Similarly, at the order and genus levels, the primary member of fungal communities in HNT, SNT, and GZT were Eurotiales and Aspergillus, respectively, which accounted for > 99.5% of the fungal community. This was in accordance with the results of previous reports on FBT samples (Xu et al. 2011; Li et al. 2017; Rui et al. 2019). In contrast, the fungal flora mentioned above only occupied approximately 34% of the PWT microbial population, while unclassified_k_Fungi was the most dominant fungal flora, which might be responsible for the unique quality of PWT (Li et al. 2019a). Thus, the identification of this unclassified fungi and its effect on fermentation process requires further investigations. Many species of the genus Aspergillus are found to play important roles in another dark tea called Pu-erh tea (Li et al. 2018b) and other fermented food, such as vinegar (Wang et al. 2016), rice wine (Ji et al. 2018), and soybean (Xie et al. 2019). It secretes various industrial enzymes including cellulases, proteases, α-amylases, and hemicellulases (Li et al. 2017), which affect flavor and taste.

Fig. 3.

Fig. 3

Open in a new tab

Relative abundance of fungal community proportions at phylum (a), order (b) and genus (c) level. Relative abundance of bacterial community proportions at phylum (d), order (e) and genus (f) level. Phyla, orders and genera occurred at < 1% abundance in all the samples are defined as “others”. PWT, Pingwu Fuzhuan brick tea; HNT, Hunan Fuzhuan brick tea; SNT, Shaanxi Fuzhuan brick tea; GZT, Guizhou Fuzhuan brick tea

Regarding bacterial communities, 43 phyla, 86 classes, 157 orders, 284 families, and 534 genera were obtained from the 16S rRNA gene sequences. The distribution of bacterial communities in FBT of four regions was analyzed at the phylum, order and genus levels (Fig. 3d, e, f). At the phylum level, we identified 37, 21, 37 and 39 bacterial phyla in PWT, HNT, SNT, and GZT samples, respectively. As shown in Fig. 3d, Proteobacteria (24.28%), Bacteroidetes (20.27%), Firmicutes (16.98%), Actinobacteria (14.11%), and Chloroflexi (12.16%) were the predominant phylum in all FBT samples. The predominant bacterial phyla in PWT were Proteobacteria (26.72%), Chloroflexi (18.48%), Bacteroidetes (17.08%), and Actinobacteria (16.78%). Similar to that in PWT, the bacterial distribution in SNT and GZT was also dominated by Proteobacteria (25.13%, 28.80%), Chloroflexi (18.77%, 14.05%), Bacteroidetes (16.68%, 20.93%), and Actinobacteria (20.52%, 17.07%). However, Firmicutes was the most abundant phylum in HNT (48.55%), followed by Bacteroidetes (25.23%) and Proteobacteria (18.17%). These results were in agreement with those of previous reports, which concluded that Proteobacteria and Firmicutes were the predominant bacterial phyla in FBT samples (Rui et al. 2019; Li et al. 2019b).

At the order level, 157 orders were identified and 137, 81, 135, and 138 orders were detected in PWT, HNT, SNT, and GZT, respectively (Fig. 3e). Bacterial communities at the order level, the relative abundances of which exceeded 1% are shown at Fig. 3e. Clostridiales (12.59%), Acidimicrobiales (9.44%), Sphingobacteriales (9.14%), Bacteroidales (7.54%), and Burkholderiales (5.50%) were the predominant bacteria present in the FBT samples. The dominant orders in PWT were Sphingobacteriales (12.22%), Acidimicrobiales (12.20%), and Burkholderiales (5.98%), which was similar to that in SNT and GZT. The proportion of orders mentioned above in SNT and GZT were 11.90% and 15.10% for Sphingobacteriales, 16.14% and 12.62% for Acidimicrobiales, and 5.87% and 6.61% for Burkholderiales, respectively. The major orders in HNT were Clostridiales (38.14%), Bacteroidales (24.46%), and Selenomonadales (5.16%).

Microbial structures (relative abundance > 1%) of FBT samples from all four regions were further compared at the genus level (Fig. 3f). In PWT, HNT, SNT, and GZT, 392, 283, 386, and 404 genera were identified, respectively. The predominant genera in all FBT samples were Candidatus_Microthrix (7.32%), norank_f_Saprospiraceae (5.59%), norank_c_C10-SB1A (2.95%), Faecalibacterium (2.54%) and Bacteroides (2.41%). Candidatus_Microthrix, norank_f_Saprospiraceae and norank_c_C10-SB1A were dominant in PWT, SNT, and GZT, with relative abundance of 9.53%, 7.50%, and 4.05% in PWT, 13.17%, 7.47%, and 4.82% in SNT, and 9.09%, 9.07%, and 3.86% in GZT, respectively. However, the predominant genera in HNT were Faecalibacterium (8.42%), Bacteroides (7.98%), and Prevotella_9 (5.71%). Bacteroides are able to ferment carbohydrate, maintaining a complex and generally beneficial relationship in the human gut (Chen et al. 2019).

PCA of microbial communities

Comparison of FBT samples from the four regions based on fungal and bacterial community structure was performed using PCA. PCA is an unsupervised method, which can show reliable multivariable consequences in tracing geographic origins (Xu et al. 2015). As shown in Fig. 4, PWT could be well distinguished from the other three FBT samples based on fungal (Fig. 4a) or bacterial genera (Fig. 4b). For fungal community structures, the first principal component (PC1) and the second component (PC2) represented 93.52% and 6.48% of the cumulative percentage variance, respectively. In total, 98% genus variances were explained by the two axes. PWT was distant from FBT of other regions in the scatter plot (Fig. 4a), indicating that PWT was distinct from FBT samples in terms of fungal communities. For bacterial community structures, PC1 and PC2 showed 95.8% and 2.64% cumulative percentage variance, respectively. The distance among the four FBT samples indicated that the difference between PWT and SNT was relatively minimal based on bacterial communities. All samples could be separately spotted based on fungal or bacterial communities, which suggested that the characteristic fungal and bacterial communities were present in each sample and that the microbial structures were affected by the traceability of geographic origin.

Fig. 4.

Fig. 4

Open in a new tab

Multiple samples of principle component analysis (PCA) according to the fungal (a) and bacterial (b) diversity. PWT, Pingwu Fuzhuan brick tea; HNT, Hunan Fuzhuan brick tea; SNT, Shaanxi Fuzhuan brick tea; GZT, Guizhou Fuzhuan brick tea

Analysis of correlation between microbial genera and chemical factors

The correlations between the top 50 identified microbial genera and chemical characteristics are shown in Fig. 5a b. In total, 23 chemical factors including gallic acid (GA), catechins (C, EC, ECG, EGC, EGCG), alkaloids (caffeine, theobromine, theophylline), free amino acids (total essential amino acids (TEAAs), total non-essential amino acids (TNEAAs), Thea, GABA, volatiles (ketones, aldehydes, alcohols, esters, hydrocarbons, methoxyphenols, acids), and color factors (L*, a*, b*) in PWT, HNT, SNT, and GZT were qualitatively and quantitatively analyzed in our previous report (Li et al. 2019a). Five groups of fungal genera were obtained from the heatmap analysis with hierarchical clustering method between fungal communities and chemical factors (Fig. 5a). Lentinula and Byssochlamys present in Group I correlated negatively with GA, catechins, amino acids, volatile acids, L* value and theophylline, while they correlated positively with volatile aldehydes and a* value. Thirteen fungal genera belonging to Group II displayed highly positive correlation with free amino acids and volatile acids. Positive correlations were observed between Group III containing 14 fungal genera, and GABA and volatile acids, while strong negative correlations were observed with most of the remaining volatiles, b* value and theobromine. Group IV including 5 fungal genera showed negative correlation with some volatiles (aldehydes, ketones, methoxyphenols), a* value, caffeine, and theobromine. Fifteen fungal genera of Group V showed strong negative correlation with volatile aldehydes, but were positively correlated with most chemical factors. Aspergillus, the dominant fungal genus in FBT samples, correlated negatively with caffeine, a* value, GABA, volatile acids, and volatile aldehydes. As another dominant fungal genus present only in PWT, unclassified_k_Fungi showed strong positive correlation with amino acid indicators, including GABA and volatile acids. Therefore, in view of the opposite correlation of GABA with Aspergillus and unclassified_k_Fungi, unclassified_k_Fungi potentially played a more significant role in GABA synthesis with higher activity of glutamate decarboxylase (Ueno 2000). The effect of unclassified_k_Fungi in GABA synthesis and FBT quality formation will be investigated in future.

Fig. 5.

Fig. 5

Open in a new tab

Heatmap of the correlations between characteristic chemical factors and top 50 microbial genera in FBT samples. a fungal; b bacterial. Thea Theanine; TEAAs Total essential amino acids; TNEAAS Total non-essential amino acids; GABA γ-aminobutyric acid; GA Gallic acid; EGC Epigallocatechin; C Catechin, EGCG Epigallocatechin gallate; EC Epicatechin; ECG Epicatechin gallate

The top 50 of bacterial genera were classified into four groups (Fig. 5b) from heatmap analysis with hierarchical clustering method. Group I, II, III, and IV contained 5, 14, 1 and 30 bacterial genera, respectively. Thirty bacterial genera were present in Group IV. It was found that Group I correlated negatively with GA, catechins, amino acids, L* value, theophylline, and volatile acids, while it correlated positively with the remaining chemical factors. The trend of Group IV was the opposite to that of Group I. Group II correlated positively with volatile aldehydes and a* value. Group III which contained only Alicycliphilus displayed negative correlation with GABA, volatile aldehydes, a* value, caffeine, theobromine and volatile methoxyphenols, while it showed positive correlation with the remaining factors except ketones.

Conclusion

This study indicated that the taste strength of sweetness, bitterness, astringency and umami in PWT was lower than those of FBT from other regions, while the difference regarding aftertaste of astringency, aftertaste of bitterness and richness among FBT samples was not distinct. The complex fungal and bacterial community structures in PWT, compared to those in HNT, SNT and GZT, were revealed. The diversity of bacterial community was higher than that of fungal community. PWT showed higher fungal and bacterial diversity. The dominance of the fungal genera unclassified_k_Fungi (65.91%) and Aspergillus (33.65%) in PWT was different from that observed in HNT, SNT, and GZT. The dominant bacterial community distribution of PWT (Candidatus_Microthrix, norank_f_Saprospiraceae, norank_c_C10-SB1A) was in accordance with those in SNT and GZT, but differed from that in HNT (Faecalibacterium, Bacteroides, Prevotella_9). In addition, results of correlation between microbiota and characteristic chemical factors showed that most of fungal and bacterial genera correlated positively with L* value, volatile acids, amino acids, GA, catechins, and theophylline. In the future, the typical unclassified_k_Fungi and the effect of unclassified_k_Fungi on the fermentation and quality of FBT needs further investigations.

Supplementary Information

Supplementary file1 (DOCX 409 kb) (408.9KB, docx)

Acknowledgements

Thanks are due to Jin-Rong Bai for assistance with the experiments and to Yan-Ping Wu for valuable discussion.

Abbreviations

PWT

Pingwu Fuzhuan brick tea

FBT

Fuzhuan brick tea

HNT

Hunan Fuzhuan brick tea

SNT

Shaanxi Fuzhuan brick tea

GZT

Guizhou Fuzhuan brick tea

PCR-DGGE

Polymerase chain reaction denaturing gradient gel electrophoresis

TRFLP

Terminal restriction fragment length polymorphism

PBS

Phosphate buffered saline

OTUs

Operational taxonomic units

PCA

Principal component analysis

GABA

γ-Aminobutyric acid

GA

Gallic acid

ECG

Epicatechin gallate

EGC

Epigallocatechin

EC

Epicatechin

C

Catechin

EGCG

Epigallocatechin gallate

TEAAs

Total essential amino acids

TNEAAs

Total non-essential amino acids

Author contributions

M-YL: Conceptualization, Methodology, Visualization, Investigation, Writing—Original Draft; YX: Resources, Data Curation, Investigation, Visualization; KZ: Validation, Supervision; HG: Funding acquisition, Project administration, Writing—Review & Editing.

Funding

This work was supported by funds of science and technology plan project of Sichuan province of China (No. 2020YFG0073).

Data availability

The data that supports the findings of this study are available in the supplementary material of this article.

Code availability

The application of software had mentioned in materials and methods section of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest currently.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Bandyopadhyay P, Ghosh AK, Ghosh C. Recent developments on polyphenol-protein interactions: effects on tea and coffee taste, antioxidant properties and the digestive system. Food Function. 2012;3(6):592–605. doi: 10.1039/c2fo00006g. [DOI] [PubMed] [Google Scholar]
  2. Chen YH, Liu XW, Huang JL, Baloch S, Xu X, Pei XF. Microbial diversity and chemical analysis of Shuidouchi, traditional Chinese fermented soybean. Food Res Int. 2019;116:1289–1297. doi: 10.1016/j.foodres.2018.10.018. [DOI] [PubMed] [Google Scholar]
  3. Drewnowski A. The science and complexity of bitter taste. Nutr Rev. 2001;59(6):163–169. doi: 10.1111/j.1753-4887.2001.tb07007.x. [DOI] [PubMed] [Google Scholar]
  4. Ji Z, Jin J, Yu G, Mou R, Mao J, Liu S, Zhou Z, Peng L. Characteristic of filamentous fungal diversity and dynamics associated with wheat Qu and the traditional fermentation of Chinese rice wine. Int J Food Sci Tech. 2018;53(7):1611–1621. doi: 10.1111/ijfs.13743. [DOI] [Google Scholar]
  5. Li Q, Huang J, Li Y, Zhang Y, Luo Y, Chen Y, Lin H, Wang K, Liu Z. Fungal community succession and major components change during manufacturing process of Fu brick tea. Sci Rep. 2017;7(1):6947. doi: 10.1038/s41598-017-07098-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Li Q, Chai S, Li Y, Huang J, Luo Y, Xiao L, Liu Z. Biochemical components associated with microbial community shift during the pile-fermentation of primary dark tea. Front Microbiol. 2018;9:1509. doi: 10.3389/fmicb.2018.01509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Li Z, Feng C, Luo X, Yao H, Zhang D, Zhang T. Revealing the influence of microbiota on the quality of Pu-erh tea during fermentation process by shotgun metagenomic and metabolomic analysis. Food Microbiol. 2018;76:405–415. doi: 10.1016/j.fm.2018.07.001. [DOI] [PubMed] [Google Scholar]
  8. Li MY, Xiao Y, Zhong K, Bai JR, Wu YP, Zhang JQ, Gao H. Characteristics and chemical compositions of Pingwu Fuzhuan brick-tea, a distinctive post-fermentation tea in Sichuan province of China. Int J Food Prop. 2019;22(1):878–889. doi: 10.1080/10942912.2019.1614951. [DOI] [Google Scholar]
  9. Li Q, Li Y, Luo Y, Zhang Y, Chen Y, Lin H, Wang K, Huang J, Liu Z. Shifts in diversity and function of the bacterial community during the manufacture of Fu brick tea. Food Microbiol. 2019;80:70–76. doi: 10.1016/j.fm.2019.01.001. [DOI] [PubMed] [Google Scholar]
  10. Mao Y, Wei B, Teng J, Huang L, Xia N. Analyses of fungal community by Illumina MiSeq platforms and characterization of Eurotium species on Liupao tea, a distinctive post-fermented tea from China. Food Res Int. 2017;99(Pt 1):641–649. doi: 10.1016/j.foodres.2017.06.032. [DOI] [PubMed] [Google Scholar]
  11. Rotzoll N, Dunkel A, Hofmann T. Activity-guided identification of (S)-malic acid 1-O-D-glucopyranoside (morelid) and gamma-aminobutyric acid as contributors to umami taste and mouth-drying oral sensation of morel mushrooms (Morchelia deliciosa Fr.) J Agric Food Chem. 2005;53(10):4149–4156. doi: 10.1021/jf050056i. [DOI] [PubMed] [Google Scholar]
  12. Rui Y, Wan P, Chen G, Xie M, Sun Y, Zeng X, Liu Z. Analysis of bacterial and fungal communities by Illumina MiSeq platforms and characterization of Aspergillus cristatus in Fuzhuan brick tea. LWT-Food Sci Technol. 2019;110:168–174. doi: 10.1016/j.lwt.2019.04.092. [DOI] [Google Scholar]
  13. Ueno H. Enzymatic and structural aspects on glutamate decarboxylase. J Mol Catal B: Enzym. 2000;10(1–3):67–79. doi: 10.1016/S1381-1177(00)00114-4. [DOI] [Google Scholar]
  14. Wang ZM, Lu ZM, Shi JS, Xu ZH. Exploring flavour-producing core microbiota in multispecies solid-state fermentation of traditional Chinese vinegar. Sci Rep. 2016;6:26818. doi: 10.1038/srep26818. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Wang X, Zhang Y, Ren H, Zhan Y. Comparison of bacterial diversity profiles and microbial safety assessment of salami, Chinese dry-cured sausage and Chinese smoked-cured sausage by high-throughput sequencing. LWT-Food Sci Technol. 2018;90:108–115. doi: 10.1016/j.lwt.2017.12.011. [DOI] [Google Scholar]
  16. Wang X, Chen D, Li Y, Zhao S, Chen C, Ning D. Alleviating effects of walnut green husk extract on disorders of lipid levels and gut bacteria flora in high fat diet-induced obesity rats. J Funct Foods. 2019;52:576–586. doi: 10.1016/j.jff.2018.11.022. [DOI] [Google Scholar]
  17. Wang X, Wang S, Zhao H. Unraveling microbial community diversity and succession of Chinese Sichuan sausages during spontaneous fermentation by high-throughput sequencing. J Food Sci Technol-Mysore. 2019;56(7):3254–3263. doi: 10.1007/s13197-019-03781-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Wang W, Zhou X, Liu Y (2020) Characterization and evaluation of umami taste: A review. TrAC-Trends Anal Chem. 127.
  19. Xie M, An F, Wu J, Liu Y, Shi H, Wu R. Meta-omics reveal microbial assortments and key enzymes in bean sauce mash, a traditional fermented soybean product. J Sci Food Agric. 2019;99(14):6522–6534. doi: 10.1002/jsfa.9932. [DOI] [PubMed] [Google Scholar]
  20. Xu A, Wang Y, Wen J, Liu P, Liu Z, Li Z. Fungal community associated with fermentation and storage of Fuzhuan brick-tea. Int J Food Microbiol. 2011;146(1):14–22. doi: 10.1016/j.ijfoodmicro.2011.01.024. [DOI] [PubMed] [Google Scholar]
  21. Xu J, Hu FL, Wang W, Wan XC, Bao GH. Investigation on biochemical compositional changes during the microbial fermentation process of Fu brick tea by LC-MS based metabolomics. Food Chem. 2015;186:176–184. doi: 10.1016/j.foodchem.2014.12.045. [DOI] [PubMed] [Google Scholar]
  22. Xu S, Wang J-J, Wei Y, Deng W-W, Wan X, Bao G-H, Xie Z, Ling T-J, Ning J. Metabolomics based on UHPLC-Orbitrap-MS and global natural product social molecular networking reveals effects of time scale and environment of storage on the metabolites and taste quality of raw Pu-erh Tea. J Agric Food Chem. 2019;67(43):12084–12093. doi: 10.1021/acs.jafc.9b05314. [DOI] [PubMed] [Google Scholar]
  23. Yang C, Hu ZY, Lu ML, Li PL, Tan JF, Chen M, Lv HP, Zhu Y, Zhang Y, Guo L, Peng QH, Dai WD, Lin Z. Application of metabolomics profiling in the analysis of metabolites and taste quality in different subtypes of white tea. Food Res Int. 2018;106:909–919. doi: 10.1016/j.foodres.2018.01.069. [DOI] [PubMed] [Google Scholar]
  24. Yoshimatsu J, Toko K, Tahara Y, Ishida M, Habara M, Ikezaki H, Kojima H, Ikegami S, Yoshida M, Uchida T. Development of taste sensor to detect non-charged bitter substances. Sensors. 2020;20(12):3455. doi: 10.3390/s20123455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Zang J, Xu Y, Xia W, Yu D, Gao P, Jiang Q, Yang F. Dynamics and diversity of microbial community succession during fermentation of Suan yu, a Chinese traditional fermented fish, determined by high throughput sequencing. Food Res Int. 2018;111:565–573. doi: 10.1016/j.foodres.2018.05.076. [DOI] [PubMed] [Google Scholar]
  26. Zhang L, Zhang ZZ, Zhou YB, Ling TJ, Wan XC. Chinese dark teas: postfermentation, chemistry and biological activities. Food Res Int. 2013;53(2):600–607. doi: 10.1016/j.foodres.2013.01.016. [DOI] [Google Scholar]
  27. Zhang XZ, Zhang YW, Meng QX, Li N, Ren LP. Evaluation of beef by electronic tongue system TS-5000Z flavor assessment recognition and chemical compositions according to its correlation with flavor. Plos One. 2015 doi: 10.1371/journal.pone.0137807. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Zhao D, Lu F, Qiu M, Ding Y, Zhou X. Dynamics and diversity of microbial community succession of surimi during fermentation with next-generation sequencing. J Food Saf. 2016;36(3):308–316. doi: 10.1111/jfs.12245. [DOI] [Google Scholar]
  29. Zhao Z, Mu T, Sun H. Microbial characterization of five Chinese traditional sourdoughs by high-throughput sequencing and their impact on the quality of potato steamed bread. Food Chem. 2019;274:710–717. doi: 10.1016/j.foodchem.2018.08.143. [DOI] [PubMed] [Google Scholar]
  30. Zhu YF, Chen JJ, Ji XM, Hu X, Ling TJ, Zhang ZZ, Bao GH, Wan XC. Changes of major tea polyphenols and production of four new B-ring fission metabolites of catechins from post-fermented Jing-Wei Fu brick tea. Food Chem. 2015;170:110–117. doi: 10.1016/j.foodchem.2014.08.075. [DOI] [PubMed] [Google Scholar]
  31. Zou G, Xiao Y, Wang M, Zhang H. Detection of bitterness and astringency of green tea with different taste by electronic nose and tongue. Plos One. 2018 doi: 10.1371/journal.pone.0206517. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 409 kb) (408.9KB, docx)

Data Availability Statement

The data that supports the findings of this study are available in the supplementary material of this article.

The application of software had mentioned in materials and methods section of the manuscript.

Articles from Journal of Food Science and Technology are provided here courtesy of Springer

RESOURCES