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Pit mud quality is a key parameter that impacts the quality of Chinese strong-flavor Baijiu production.This study was developed to explore spatial bacterial community distributions and the relationships between these distributions and the volatile compound accumulation within the pit mud used in the production of Chinese strong-flavor Baijiu. The results revealed Lactobacillus pasteurii and Limnochorda pilosa were found to be the dominant bacteria present in the upper wall, middle wall, and bottom pit mud layers, whereas the Clostridium genus was detectable at high levels in the lower layer of the pit wall and played a role in contributing to the overall aroma and flavor compounds in produced Chinese strong-flavor Baijiu, with Clostridium abundance being strongly correlated with caproic acid, ethyl caproate, ethyl butyrate, and hexanol levels as well as moderately correlated with butyric acid levels. The abundance of the Lactobacillus genus was positively correlated with levels of ethyl lactate, 1-butanol, and 2,3-butanediol. Limno- chorda pilosa was closely associated with ethyl acetate levels. Additionally, the correlations between bacterial com- 3− + munities and chemical properties also investigated, and the results demonstrated PO4 , total carbon, K , humus, + 2+ NH -N, and Mg levels significantly affected the bacterial community structure of pit mud, and they were positively correlated with the relative abundance of Clostridium. Together, these findings can serve as a foundation for future studies exploring the mechanisms whereby volatile compounds accumulate in different pit mud layers, which facili- tates the fermentation regulation and pit mud quality improvement of Chinese strong-flavor Baijiu. Keywords Pit mud, Bacterial community, Volatile flavor compounds, Chinese strong-flavor Baijiu Introduction Chinese strong-flavor Baijiu is renowned for its charac - teristic “strong pit flavor, soft, sweet and mellow, har - monious flavor, and long aftertaste”, and it accounts for over 70% of total Baijiu sales in China (Yan et al. 2015). Co-first author: Jia YongLei The aroma of Chinese strong-flavor Baijiu is primarily *Correspondence: Pu Shunchang associated with the metabolic activities of the different email@example.com microorganisms involved in its fermentation, with the Shi Cuie dominant flavor of this type of Baijiu being derived from firstname.lastname@example.org Department of biology engineering, Huainan Normal University, pit mud microorganisms. The pit mud used to ferment Huainan 232038, Anhui, China Chinese strong-flavor Baijiu is home to countless aro - Department of biology and food engineering, Bozhou University, matic compound-producing microbes, including species Bozhou 236800, China Liquor Making Biotechnology and Application Key Laboratory of Clostridium, Bacillus, and Methanobacter, which play of Sichuan Province, Sichuan University of Science & Engineering, critical roles in determining the quality of the resultant Yibin 644000, China Chinese strong-flavor Baijiu. During the fermentation © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Shoubao et al. AMB Express (2023) 13:3 Page 2 of 13 process, the microbes that inhabit the pit mud interact mud microbes (Hu et al. 2020). Pit mud physicochemical with grains at the interface between these grains and the properties have been found to impact microbial survival, pit mud, with the resultant compound exchange lead- with water content, for example, influencing soil pH and ing to the production of various aromatic compounds microbial growth (Xiang et al. 2009). An appropriate (Yan et al. 2019), thus enabling these microbes to shape microenvironmental pH can support alcohol fermenta- the taste and quality of this beverage. Traditionally, fer- tion while promoting the production of aromatic precur- mentation cellars are used for many years with the fer- sor compounds and other improvements in liquor quality mented grain placed in the lower portion of the pit (Liao et al. 2010). Ammonium nitrogen is required for cellar serving as a source of high-quality liquor. While the growth of microbes and the synthesis of a range of the underlying mechanisms have yet to be fully clari- enzymes and other proteins, with appropriate ammo- fied, position-dependent fermentation effects have been nium nitrogen concentrations being critical to the main- attributed to the distinct microbes that are primarily pre- tenance of pit mud quality and the overall improvement sent within the lower portion of the pit cellar (Bi et al. of liquor quality (Hu et al. 2021). Both phosphorus and 2022). To clarify how these microbes shape the process potassium in pit mud can also support microbial growth of Chinese strong-flavor Baijiu production, there is thus a (Wu et al. 2022). With rising pit age, increases in water, clear need to examine microbial distributions and aroma organic matter, and available potassium concentrations compounds present in different spatial locations within have been reported (Li et al. 2018). The ability of pit mud the pit mud. to impact the produced Chinese strong-flavor liquor is Levels of volatile compounds within pit mud serve as also related to the `flavor compounds present within the an important index by which the quality of the pit mud pit mud. Indeed, several flavor compounds have been can be assessed, in addition to serving as a material basis linked to improvements in Chinese strong-flavor liquor for mutual exchange with fermented grains. Headspace quality and flavor, with the types and levels of these aro - solid-phase microextraction (HS-SPME) coupled with matic compounds being related to the aging-related pit gas chromatography-mass spectrometry (GC-MS) is an microbial community structures (Wu et al. 2022), and to east-to-conduct approach that has been widely used in spatial locations within fermentation pits (Hu et al. 2020). recent years to characterize volatile compounds present To date, several studies have explored microbial com- within pit mud and grains used in distilling processes munities present within pit mud from fermentation cel- (Liu et al. 2017; Qian et al. 2021). Studies of the spatial lars of different ages, whereas relatively few studies have distributions of aromatic compounds within fermented examined the spatial distributions of bacterial communi- grains have revealed that the levels of esters and acids are ties and the relationships between these distributions and higher in grain samples closer to the pit mud, thus dem- volatile compound accumulation in pit mud used to pro- onstrating that the pit mud plays a role in the production duce Chinese strong-flavor liquor. of these volatile compounds (Tang et al. 2012). Pit mud To date, several studies have explored microbial com- organic acid content has also been shown to increase munities present within pit mud from fermentation cel- with cellar depth, indicating a significant difference in lars of different ages, whereas relatively few studies have organic acid levels in different spatial positions within a examined the spatial distributions of bacterial communi- given fermentation pit (Lei et al. 2020; Zhang et al. 2022) ties and the relationships between these distributions and previously conducted quantitative and qualitative analy- volatile compound accumulation in pit mud used to pro- ses of aromatic compounds present within pit mud and duce Chinese strong-flavor liquor. fermented grain sample, revealing a high degree of simi- Previous studies concerning microbial community larity between the dominant aromatic compounds in pit structure have been performed based on culture-depend- mud and grain samples and the relative positions of these ent methods. However, most of the microorganisms of dominant compounds. Further analyses have demon- pit mud are uncultured or difficult to culture, and cul - strated that aromatic compound levels are higher in fer- ture-dependent method is difficult to reveal the inner mented grain samples from the sides of the fermentation pattern comprehensively and objectively. In contrast, pit relative to those in the central region, consistent with molecular biology approaches have been proven to be interactions and material exchange between the pit mud powerful tools in providing a more complete inven- and these grains in the context of liquor fermentation. tory of the microbial diversity in environmental samples Analyses of microbial communities in pit mud samples (Wang et al. 2011). By the analysis of the bands migrating collected from different locations within fermentation separately on the DGGE gels, polymerase chain reaction pits (including the bottom of the cellar and the upper, denaturing gradient gel electrophoresis (PCR-DGGE) has middle, and lower portions of the cellar walls) have succeeded in obtaining phylogenetic information about revealed marked differences in the spatial profiles of pit the microorganisms existing fields of environmental Shoubao et al. AMB Express (2023) 13:3 Page 3 of 13 ecology and microbiology (Deng et al. 2012), thus, limi- amplified V3 16 S rRNA sequences for DGGE analysis. tations of the traditional culture method can be avoided, All PCR reactions were performed in a 50 µL volume and the original status of the microbial community in the containing 5 µL 10×PCR buffer, 3.2 µL dNTP Mixture pit mud can be accurately reflected. (2.5 mM), 0.4 µL of Premix ExTaq (5 U/µL), 1 µL of each The present study was thus developed in an effort to primer (20 µM), 50 ng of template DNA, and double-dis- explore differences in the bacterial communities present tilled water (ddH O) to a final volume of 50 µL. in different spatial positions in the pit mud of a fermenta - The thermocycler program was as follows: 94 °C for tion pit used in the production of Chinese strong-flavor 5 min, followed by 30 cycles of 94 °C for 60 s, 55 °C for liquor. Additionally, an HS-SPME-GC-MS approach was 45 s, decreasing by 0.5 °C/cycle, and 72 °C for 1 min. used to evaluate volatile compound fingerprints in these Samples were then processed through 15 cycles of 94 °C different spatial locations, while correlation analyses were for 45 s, 55 °C for 45 s, 72 °C for 1 min, and a final exten - used to examine relationships between pit mud physico- sion at 72 °C for 5 min prior to holding at 16 °C. The chemical properties, bacterial community composition, amplified products were analyzed via 1% agarose gel and volatile aromatic compound levels as a means of clar- electrophoresis. ifying the spatial dynamics of microbial communities and volatile compound production within pit mud. PCR‑DGGE analyses Denaturing gradinent electrophoresis (DGGE) analyses Materials and methods were performed using a Dcode system instrument (Bio- Pit mud sample collection Rad). Briefly, PCR products were applied to 7% poly - The samples of pit mud were collected on November 18, acrylamide gels in 1×Tris acetate-EDTA buffer (TAE) 2021 from a 20-year-old fermentation pit in a strong- running buffer. DGGE analyses were performed using a flavor liquor distillery in Anhui Province, China. These denaturing gradient with denaturant (7 M urea and 40% samples were collected from four positions within the formamide) concentrations from 35 to 55%. Gels were fermentation pit, including the upper, middle, and lower separated for 5 h at 150 V 60 °C, after which they were layers of the cellar wall as well as the bottom of the pit. subjected to silver staining for 15 min (Yan et al. 2019). Sample plots were divided into 8 subplots (center and Gels were then imaged with a Bio-Rad Gel Doc XR scan- edges), with the exception of the bottom layer which was ner (Bio-Rad, USA), and bands were selected, excised separated into 9 subplots (the side center, side edges, and with a clean blade, and eluted by incubating them over- bottom middle). Approximately 100 g of pit mud was col- night in 30 µL of sterile distilled water at 4 °C to permit lected from each subplot with a sterile hollow cylindrical DNA diffusion. Samples were then stored at −20 °C. sampler (Puluody, China) to a depth of ~ 5 cm. Samples were then thoroughly mixed and stored at −20 °C in ster- DGGE profile sequence analyses ile polyethylene bags prior to analysis. Eluted samples of DNA were re-amplified with the same PCR settings and GC-clamp primers as used above, Eubacterial community analyses after which these products were analyzed via DGGE DNA extraction and PCR amplification with the sample DNA samples used in the initial analy- A total of 1 g of pit mud per sample was mixed for 5 min sis to enhance purity and to confirm sequence identity. in 25 mL of phosphate buffered saline (PBS) (0.1 mol/L, Bands in the same position as those selected above were pH 8.0), followed by centrifugation for 10 min at 600 xg excised and eluted using the same methods. Followed at 4 °C. Pellets were then rinsed three times with PBS, fol- by re-amplification with the same primers without the lowed by centrifugation at 12,000 xg for 10 min at 4 °C. GC-clamp. Purified DNA samples were introduced into The pellet was then washed three times using PBS, fol - the pMD18-T vector (Tiangen) and sequenced by San- lowed by storage at −20 °C. Samples were prepared in gon (Shanghai, China). The BLAST tool was then used to triplicate. Genomic DNA was isolated from these sam- search GenBank for these sequences to identify the clos- ples with a Soil Genomic DNA Rapid Extraction Kit est known relatives for the obtained partial 16 S rRNA (Omega) based on provided directions, after which sam- sequences. ples were analyzed via 1% (w/v) agarose gel electrophore- sis and stored at −20 °C. Volatile compound analyses The PCR-mediated amplification of bacterial 16 S Volatile acids were determined by distillation extraction- rRNA sequences was initially performed using the gas chromatography (Yan et al. 2015). A 100 g pit mud 27 F/1492R primers, after which nested PCR was per- sample and 200 mL of 60% (v/v) ethanol were transferred formed with the DGGE primers GC-338 F/518R to yield into 500 mL round-bottom flask, the flask was heated Shoubao et al. AMB Express (2023) 13:3 Page 4 of 13 and a 100 mL solution was distilled from the mixture. were analyzed with an ion chromatograph (ICS5000+, The obtained solution was analysed using a gas chro - ThermoFisher) equipped with conductivity detector matograph according to our previous reports (Yan et al. (ICS-5000 + -DC) and a CS12 column (IonPac, Ther - 2015). moFisher, 4 mm × 250 mm). A 25 µL injection volume As for other volatile compounds (esters, alcohols, was used for all analyses, with methane sulfonic acid aldehydes, ketones, alkanes, and volatile phenols), they (20 mM) as the carrier fluid at a 1 mL/min flow rate, were extracted using a solid-phase microextraction and a constant column temperature of 30 °C. extraction (SPME) head (DVB-CAR-PDMS). Briefly, 1 g pit mud samples were added to 10 mL bottles to which Results 1 mL of water, 0.25 g NaCl, and 100 µL of 2-octanol DGGE‑based bacterial community characterization (70 mg/L, internal standard) were added. The mixture Initially, a DGGE fingerprinting approach was used to was then incubated for 30 min in a 50 °C water bath. characterize the bacterial community profiles in differ - The DVB-CAR-PDMS fiber of SPME was then exposed ent pit mud samples from the upper, middle, and lower to the bottle headspace 2.0 cm from the surface of the liquid for 30 min. Once extraction was complete, this fiber was introduced into the GC injector for 5 min for thermal desorption, with desorbed volatile compounds then being analyzed and characterized. A GC-MS instrument (Agilent 6890 GC and Agilent 5975 mass selective detector (MSD); Agilent, CA, USA) was used to separate and analyze volatile compounds in these extracts with the following settings: DB-Wax column (Length of 60 m, 0.25 mm internal diameter, 0.25 μm film thickness); carrier gas: helium; flow rate: 1 mL/min; split ratio 5:1; inlet temperature: 250 °C. The oven temperature programmed from 40 °C (held 2 min), ramped at 5 °C/min to 80 °C (held for 2 min), then the temperature was increased to 230 °C at a rate of 7 °C per minute, and maintain for 8 min. MS analyses were performed with an electrospray ionization source with a 70 eV electron energy, an ion source temperature of 230 °C, and a scanning range of 28–500 amu in full scan mode. Samples were assessed in triplicate, with quantitative and qualitative analyses being conducted as reported previously by Yan et al. (2019). Analyses of pit mud physicochemical properties Pit mud moisture levels were detected via a gravimetric approach by drying samples for a minimum of 6 h at 105 °C. Pit mud pH was assessed using a pH meter and a 1:10 pit mud to boiled deionized water sample. Ammo- nium (NH -N) present within pit mud was extracted using 10% (w/v) NaCl and measured with a UV–vis spectrophotometer (UV-9000, Bejing Puxi Instruments Co., Ltd). Total carbon levels in air-dried powdered pit mud samples were assessed with an Elementar instru- Fig. 1 PCR-DGGE fingerprints based on 16 S rRNA genes amplified ment (Shanghai Yuanxi TOC-5000, China) in CHNS from bacteria found in pit mud samples collected from different mode with the burning tube being heated to 1150 °C positions within the fermentation pit. Lanes U, M, D, and B and the reducing tube being heated to 850 °C. Pure respectively correspond to pit mud samples from the upper wall, + 3− 2+ water was used to extract K , PO , soluble Mg , 4 middle wall, lower wall, and bottom layers of the cellar. Bands 2+ and soluble Ca from air-dried pit mud samples at marked by numbers were excised for sequencing, with the resultant alignment results being compiled in Table 2 a 1:10 (w/v) ratio, after which their concentrations Shoubao et al. AMB Express (2023) 13:3 Page 5 of 13 layers of the pit wall and from the bottom of the pit layers (15) (Table 1). There were no significant differ - (Fig. 1). There were notable differences in the microbial ences in evenness index values for these different pit composition of pit mud samples from these different mud samples, suggesting that they all exhibit largely spatial locations, with species richness being sufficient homogeneous ecosystems. Notably, the Shannon-Wie- to effectively separate these four samples while reveal - ner index values for samples from the lower layer of the ing that the samples from the lower layer of the pit wall pit mud wall were highest (3.42) in these PCR-DGGE exhibited the greatest number of bands (18), followed profiles, confirming the presence of a high number of by samples from the middle and upper pit mud wall different species of bacteria in these samples. Bacterial DGGE pattern for amplified 16 S rDNA V3 gene fragments revealed 15, 15, 18, and 13 bands in the upper wall, middle wall, lower wall, and bottom pit mud Table 1 Bacterial diversity indices in pit mud samples as layers, respectively, with lower wall samples thus exhibit- calculated based on the DGGE banding patterns shown in Fig. 1 ing more DGGE bands than other samples. In total, 29 Shannon‑ Wiener Evenness Richness bands were excised for sequencing, leading to the iden- tification of 24 families of Caloramator, Janthinobac- 1 3.2 0.996 15 terium, Tepidanaerobacter, Frondibacter, Clostridium, 2 2.98 0.997 15 Petrimonas, Lutaonella, Hydrogenoanaerobacterium, 3 3.42 0.997 18 Thermoclostridium, Sedimentibacter, Syntrophomonas, 4 3.07 0.995 13 Petrimonas, Proteiniphilum, Lactobacillus, Limnochorda, Table 2 BLAST Identified gene sequences of 16 S rDNA - derived bands excised from a DGGE gel a b Band no. Closest relative (NCBI accession no.) Identity (%) 1 Caloramator mitchellensis (NR_117542.1 ) 98.98 2 Janthinobacterium lividum (NR_026365.1) 100.00 3 Tepidanaerobacter acetatoxydans (NR_074537.1) 97.04 4 Frondibacter aureus (NR_134733.1) 97.83 5 Syntrophomonas curvata (NR_025752.1) 96.33 6 Petrimonas sulfuriphila (NR_042987.1) 100.00 7 Lutaonella thermophila (NR_044451.1) 95.65 8 Thermoclostridium caenicola (NR_126170.1) 95.29 9 Hydrogenoanaerobacterium saccharovorans (NR_044425.1) 95.88 10 Clostridium sporosphaeroides (NR_044835.2) 98.22 11 Sedimentibacter hydroxybenzoicus (NR_029146.1) 96.51 12 Clostridium kluyveri (NR_074447.1) 97.63 13 Petrimonas mucosa (NR_148808.1) 96.65 14 Proteiniphilum saccharofermentans (NR_148807.1) 96.24 15 Lactobacillus pasteurii (NR_117058.1) 96.41 16 Clostridium luticellarii (NR_145907.1) 100.00 17 Limnochorda pilosa (NR_136767.1) 96.18 18 Phocea massiliensis (NR_144748.1) 97.90 19 Fermentimonas caenicola (NR_148809.1) 98.19 20 Proteiniphilum acetatigenes (NR_043154.1) 100.00 21 Anaeromassilibacillus senegalensis (NR_144727.1) 96.38 22 Lactobacillus acetotolerans (NR_044699.2) 100.00 23 Christensenella massiliensis (NR_144742.1) 96.52 24 Clostridium jeddahense (NR_144697.1) 98.03 25 Clostridium limosum (LC036318.1) 98.51 26 Ardenticatena maritima (NR_113219.1) 98.88 27 Syntrophaceticus schinkii (NR_116297.1) 96.34 28 Pelomonas puraquae (JQ660112.1) 96.33 29 Atopobium rimae (NR_113038.1) 97.53 Shoubao et al. AMB Express (2023) 13:3 Page 6 of 13 Phocea, Fermentimonas, Proteiniphilum, Anaeromassili- hexanoate, ethyl heptanoate, ethyl benzoate, ethyl nona- bacillus, Christensenella, Ardenticatena, Syntrophace- noate, ethyl caprate, ethyl undecanoate, benzeneacetic ticus, Pelomonas, and Atopobium (Table 2, Additional acid, ethyl 3-phenylpropanoate, ethyl phenylacetate, file 1). Lactobacillus pasteurii (Band 15) and Limno- and ethyl myristate were observed in the lower wall pit chorda pilosa (Band 17) were found to be the dominant mud layer, with ethyl hexanoate, ethyl heptanoate, and bacteria present in the upper wall, middle wall, and bot- ethyl caprate levels being significantly higher than those tom pit mud layers, whereas Clostridium kluyveri (Band of other esters, reaching average concentrations of up 12) and Clostridium luticellarii (Band 16) exhibited the to 912.754 ± 9.185 µg/mg. This was particularly true for opposite trends and were only present in the lower wall ethyl hexanoate, which is a predominant flavor com - pit mud samples. Lactobacillus pasteurii (Band 15) and pound in Chinese strong-flavor liquor, and accounted for Lactobacillus acetotolerans (Band 22) were the primary over 70% of the total ester content. Maximal ethyl palmi- lactic acid bacteria species identified in these samples, tate levels were observed in the bottom pit mud layer. while Thermoclostridium caenicola (Band 8), Clostridium Alcohols are also the group with comparative high sporosphaeroides (Band 10), Clostridium kluyveri (Band content of volatile composition in pit mud. Total alcohol 12), Clostridium luticellarii (Band 16), Clostridium jed- content was found to be highest in the lower wall pit mud dahense (Band 24), and Clostridium limosum (Band 25) layer (61.699 ± 4.233 µg/mg), followed by the bottom were the main Clostridium species found in these differ - layer (39.321 ± 1.032 µg/mg). Of the detected alcohols, ent pit mud layers. enanthol, isobutanol, isooctanol, and 2-heptanol were present at the highest levels in the lower wall pit mud Analysis of spatial pit mud volatile compound profiles layer, whereas the highest levels of enanthol, isobutanol, Next, the concentrations and retention times (in min- isooctanol, and 2-heptanol were detected in the upper utes) for different volatile compounds found in these pit wall pit mud layer. mud samples were analyzed (Table 3). In total, 53 vola- Six different aldehydes were detected in all pit mud tile compounds were identified and quantified, including layer samples, among which nonaldehyde, benzalde- 15 acids, 13 esters, 9 alcohols, 6 aldehydes, 3 ketones, 4 hyde, 2-undecenal, pentanal, and 2-phenyl-2-butenal alkanes, and 3 volatile phenols. Marked differences in the were detected at the highest levels in the lower wall pit distributions of these volatile compounds were observed mud samples, whereas 2-heptenal exhibited the opposite across spatial locations. trend, being present at the highest levels in the upper pit Acids were the most abundant and important aroma mud layer. compounds in these pit mud samples (Table 3). Twelve Total ketone and alkane levels were highest in the acids were present at the highest levels in the lower upper wall pit mud layer, while maximum total volatile wall pit mud samples (Butyric acid, pentanoic acid, phenol levels were observed in the middle wall pit mud 2-benzylpropionic acid, 4-methyl-pentanoic acid, cap- layer. roic acid, nonanoic acid, n-decanoic acid, benzoic acid, decanoic acid, benzeneacetic acid, and tetrade- Pit mud physicochemical properties canoic acid), with caproic acid (2421.936 ± 16.321 µg/ The physicochemical properties of samples of pit mud mg) and butyric acid (469.598 ± 4.025 µg/mg) being the collected from different spatial positions are compiled in most abundant in these samples. The content of pro - Table 4. There were clear increasing trends in the levels 2+ pionic acid (69.929 ± 2.214 µg/mg) was the highest in of moisture, pH, and C a in pit mud samples from the up layer of pit mud, followed by samples from the mid- upper layer to the deepest layer of the pit corresponding dle and lower wall layers. Samples from the bottom to a physicochemical gradient within the fermentation of the pit exhibited the highest levels of 2-benzylpro- pit. The K content in the bottom layer of pit mud was pionic acid (38.215 ± 3.541 µg/mg) and octanol acid more similar to that in the lower layer of the pit mud wall (1142.957 ± 9.325 µg/mg) relative to other analyzed spa- and significantly higher than that in other pit mud sam - tial positions. ples, whereas maximum NH -N, total carbon, humus, 2+ 3− The results of this study suggest that ester levels were Mg , and PO levels were observed in the lower wall higher in the lower wall and bottom pit mud layers rela- pit mud layer followed by the bottom and middle pit mud tive to the upper wall and middle wall layers. The great - layers. est variety of esters was also observed in pit mud samples from the lower wall and bottom of the fermentation pit Correlations between bacterial communities and chemical (Table 3), with the exception of ethyl tridecanoatel, which properties was uniquely present in the upper and middle wall lay- Next, redundancy analysis (RDA) correlations were ers. The highest concentrations of ethyl valerate, ethyl assessed to evaluate the relationship between different Shoubao et al. AMB Express (2023) 13:3 Page 7 of 13 Table 3 The volatile aroma compounds detected and measured in the samples collected from different spatial positions of pit Number Aroma compounds Retention Identification Contents of volatile aroma compounds of pit mud(µg/mg) time (min) U M D B Volatile acids AC1 Propionic acid 12.383 MS, RI 69.929 ± 2.214 56.783 ± 1.254 34.543 ± 1.987 6.254 ± 0.587 AC2 Butyric acid 14.547 MS, RI 135.802 ± 5.365 358.306 ± 6.321 469.598 ± 4.025 364.421 ± 3.587 AC3 Pentanoic acid 16.761 MS, RI ND 12.452 ± 0.584 87.452 ± 5.026 79.444 ± 1.357 AC4 2-Benzylpropionic acid 17.746 MS, RI ND ND 27.673 ± 1.025 38.215 ± 3.541 AC5 4-methyl-pentanoic acid 18.351 MS, RI ND 5.353 ± 0.325 13.488 ± 0.897 9.967 ± 0.869 AC6 Caproic acid 19.468 MS, RI 308.398 ± 12.321 783.231 ± 16.214 2421.936 ± 16.321 1652.168 ± 10.214 AC7 Heptanoic acid 21.72 MS, RI 328.312 ± 6.325 210.432 ± 4.321 95.562 ± 3.351 ND AC8 Octanol acid 24.193 MS, RI 638.129 ± 8.369 907.256 ± 3.651 1006.955 ± 8.354 1142.957 ± 9.325 AC9 Nonanoic acid 26.735 MS, RI 24.469 ± 2.351 35.4226 ± 2.584 45.544 ± 2.102 20.342 ± 0.156 AC10 n-Decanoic acid 28.514 MS, RI 27.159 ± 1.695 61.789 ± 1.587 77.172 ± 1.650 29.683 ± 0.365 AC11 Benzoic acid 30.35 MS, RI 9.905 ± 0.895 22.745 ± 1. 40.110 ± 1.365 22.793 ± 0.201 AC12 Decanoic acid 31.025 MS, RI ND ND 12.919 ± 0.987 6.754 ± 0.965 AC13 Benzeneacetic acid 31.675 MS, RI ND 21.556 ± 0.202 62.141 ± 2.036 15.169 ± 1.589 AC14 Tetradecanoic acid 32.956 MS, RI ND ND 14.0171 ± 0.876 8.432 ± 0.968 AC15 n-Hexadecanoic acid 34.615 MS, RI ND 7.338 ± 0.658 8.357 ± 0.879 7.060 ± 0.756 Σ 1542.103 ± 16.325 2482.6636 ± 12.354 4417.4671 ± 25. 3403.659 ± 21.021 Esters ES1 Ethyl valerate 2.176 MS, RI ND 9.751 ± 0.768 41.516 ± 1.036 20.145 ± 1.879 ES2 Ethyl acetate 3.274 MS, RI 320.23 ± 7.558 516.276 ± 23.258 833.687 ± 22.369 670.564 ± 21.527 ES3 Ethyl caproate 4.012 MS, RI 210.21 ± 6.321 1102.194 ± 20.021 1534.301 ± 18.654 1210.365 ± 18.654 ES4 Ethyl butyrate 6.125 MS, RI 118.356 ± 8.943 469.735 ± 26.793 632.441 ± 21.786 535.436 ± 20.894 ES5 Ethyl heptanoate 6.529 MS, RI 218.620 ± 8.32 243.079 ± 2.065 428.677 ± 10.365 321.845 ± 9.587 ES6 Ethyl benzoate 9.140 MS, RI 17.566 ± 0.987 24.534 ± 1.231 48.611 ± 0.986 36.397 ± 2.654 ES7 Ethyl nonanoate 12.301 MS, RI 11.324 ± 1.032 19.631 ± 1.032 28.086 ± 0.986 23.254 ± 1.841 ES8 Ethyl lactate 12.418 MS, RI 991.863 ± 27.32 787.467 ± 11.236 357.324 ± 10.587 465.320 ± 21.695 ES9 Ethyl caprate 14.137 MS, RI ND 47.389 ± 2.365 230.889 ± 6.354 118.558 ± 1.005 ES10 Ethyl undecanoate 17.569 MS, RI ND 7.167 ± 0.865 22.000 ± 0.864 10.235 ± 0.986 ES11 Benzeneacetic acid 18.099 MS, RI ND 44.595 ± 1.036 63.735 ± 1.032 46.626 ± 0.653 ES12 Ethyl 3-phenylpro- 19.990 MS, RI 52.648 ± 1.365 70.878 ± 2.351 88.514 ± 4.235 80.603 ± 4.658 panoate ES13 Ethyl tridecanoate 20.559 MS, RI 12.576 ± 0.989 9.563 ± 0.897 ND ND ES14 Ethyl phenylacetate 22.372 MS, RI ND ND 9.684 ± 0.945 6.626 ± 0.876 ES15 Ethyl myristate 26.401 MS, RI ND 7.743 ± 0.852 23.838 ± 3.621 19.523 ± 1.894 ES16 Ethyl Palmitate 28.212 MS, RI 31.831 ± 3.025 114.508 ± 6.321 157.149 ± 9.362 183.678 ± 4.658 Σ 1985.224 3474.51 1985.224 ± 8.980 3474.51 ± 2.125 4500.452 ± 1111 3789.175 ± 17.879 Alcohols AL1 Hexanol 7.28 MS, RI 5.211 ± 0.421 11.036 ± 1.982 27.368 ± 3.659 16.124 ± 2.033 AL2 Enanthol 11.241 MS, RI 6.263 ± 0.206 3.955 ± 0.057 2.775 ± 0.082 1.187 ± 0.032 AL3 Isobutanol 11.342 MS, RI 16 0.215 ± 0.596 9.356 ± 0.163 7.625 ± 0.355 4.845 ± 0.085 AL4 Isooctanol 12.105 MS, RI 8.389 ± 0.732 4.279 ± 0.389 2.207 ± 0.303 0.875 ± 0.069 AL5 1-Butanol 12.675 MS, RI 0.786 ± 0.462 4.268 ± 0.863 7.441 ± 0.986 5.079 ± 0.458 AL6 2,3-butanediol 13.432 MS, RI ND 1.487 ± 0.354 3.096 ± 0.855 2.0154 ± 0.019 AL7 1-Pentanol 15.090 MS, RI 0.897 ± 0.101 3.653 ± 0.563 6.143 ± 0.996 5.321 ± 0.686 AL8 1-nonanol 15.966 MS, RI 7.232 ± 0.203 6.167 ± 0.202 3.147 ± 0.303 2.847 ± 0.303 AL9 2-Heptanol 16.620 MS, RI 3.827 ± 0.682 2.565 ± 0.063 1.897 ± 0.063 1.028 ± 0.013 Σ 32.605 ± 1.641 35.730 ± 2.036 61.699 ± 4.233 39.321 ± 1.032 Shoubao et al. AMB Express (2023) 13:3 Page 8 of 13 Table 3 (continued) Number Aroma compounds Retention Identification Contents of volatile aroma compounds of pit mud(µg/mg) time (min) U M D B Aldehydes AD1 2-Heptenal 7.879 MS, RI 4.715 ± 0.398 3.267 ± 0.368 1.132 ± 0.132 0.587 ± 0.0498 AD2 Nonaldehyde 9.453 MS, RI 0.765 ± 0.056 3.188 ± 0.296 6.132 ± 0.568 4.012 ± 0.365 AD3 Benzaldehyde 12.809 MS, RI 1.315 ± 0.980 2.332 ± 0.205 4.366 ± 0.396 3.254 ± 0.296 AD4 2-undecenal 17.723 MS, RI 0.875 ± 0.056 1.925 ± 0.123 3.135 ± 0.268 2.015 ± 0.158 AD5 Pentanal 18.202 MS, RI 0.765 ± 0.063 1.897 ± 0.106 2.278 ± 0.260 1.968 ± 0.103 AD6 2-phenyl-2-butenal 21.621 MS, RI 1.032 ± 0.012 1.378 ± 0.101 4.543 ± 0.385 2.014 ± 0.186 Σ 9.467 ± 0.851 13.987 ± 0.998 21.586 ± 0.968 13.85 ± 1.103 Ketones KE1 2-octanone 6.576 MS, RI 8.419 ± 0.098 3.251 ± 0.299 2.014 ± 0.203 ND KE2 2-Heptanone 9.354 MS, RI 13.902 ± 0.986 8.564 ± 0.787 3.214 ± 0.654 0.987 ± 0.088 KE3 Undecanone 17.689 MS, RI 3.564 ± 0.336 8.572 ± 0.796 13.715 ± 1.021 9.254 ± 0.087 Σ 25.885 ± 1.035 20.387 ± 0.212 18.943 ± 1.036 10.241 ± 0.098 Alkanes MS, RI AK1 Dodecamethylcyclohex- 7.784 MS, RI 6.269 ± 0.556 8.251 ± 0.774 14.340 ± 0.985 10.254 ± 1.213 asiloxane AK2 Decamethylcyclopenta- 2.239 MS, RI 24.768 ± 0.205 12.354 ± 0.105 8.657 ± 0.754 2.587 ± 0.302 siloxane AK3 Decamethyltetrasilox- 19.247 MS, RI 23.010 ± 0.271 11.587 ± 0.989 6.587 ± 0.563 3.254 ± 0.289 ane AK4 2-phenyl-2-butenal 21.556 MS, RI ND 9.564 ± 0.785 ND ND Σ 54.047 ± 4.058 41.756 ± 0.687 29.584 ± 0.587 16.095 ± 0.989 Volatile phenols VP1 Phenol 24.562 MS, RI ND 1.871 ± 0.152 3.254 ± 0.285 2.012 ± 0.156 VP2 2-Methylphenol 24.603 MS, RI 27.519 ± 1.996 35.602 ± 2.698 28.161 ± 1.365 32.985 ± 2.032 VP3 3-methylphenol 24.799 MS, RI 10.701 ± 1.023 ND ND ND Σ 22.22 ± 1.354 37.473 ± 2.654 28.415 ± 2.023 21.997 ± 1.652 Table 4 Pit mud physicochemical properties for samples collected from different spatial positions Parameter U M D B Moisture (%) 30.68 ± 2.87 34.18 ± 2.42 37.68 ± 2.58 39.32 ± 2.33 pH 5.28 ± 0.36 6.09 ± 0.25 7.30 ± 0.49 9.01 ± 0.87 NH -N (g/kg) 2.16 ± 0.19 3.77 ± 0.28 5.13 ± 0.29 4.19 ± 0.31 Total carbon (%, ads) 1.17 ± 1.29 1.32 ± 0.19 1.78 ± 0.22 1.58 ± 0.29 Humus (%, ads) 5.50 ± 0.21 9.121 ± 0.68 16.31 ± 0.89 13.58 ± 1.29 3− PO4 (mg/kg, ads) 195.28 ± 17.16 246.37 ± 18.26 385.19 ± 20.27 294.41 ± 19.38 K (mg/kg, ads) 528.29 ± 45.69 721.60 ± 31.17 1127.45 ± 48.75 1123.57 ± 80.13 2+ Mg (mg/kg, ads) 143.61 ± 56.22 186.51 ± 41.70 251.56 ± 47.10 215.70 ± 62.36 2+ Ca (mg/kg, ads) 359.31 ± 15.39 437.48 ± 20.37 509.78 ± 22.25 708.22 ± 43.22 (1) ads means air-dry samples. (2) U, M, D, and B respectively represent pit mud samples collected from up wall layer of cellar, middle wall layer of cellar, down wall layer of cellar, and bottom layer of cellar, and were sampled from the same fermentation cellar. (3) All data are presented as means ± standard deviations bacterial genera and the chemical properties of samples with a significant correlation between bacterial commu - collected from different layers of pit mud (Fig. 2). The nities and chemical properties. Pit mud samples collected first two axes in this analysis accounted for 86.10% of the from different spatial positions also exhibited clear sepa - observed variation in bacterial communities, consistent ration from one another in this RDA analysis, consistent Shoubao et al. AMB Express (2023) 13:3 Page 9 of 13 mud layers were next analyzed, revealing several correla- tions between specific pairs of bacteria and volatile com - pounds (Fig. 3). AC1 (Propionic acid) was highly correlated with Pet- rimonas sulfuriphila abundance, while AC2 (Butyric acid), AC3 (Pentanoic acid), and AC6 (Caproic acid) were strongly positively correlated with the abundance of members of the Clostridium genus (Clostridium jed- dahense, Clostridium kluyveri, Thermoclostridium caenicola, Clostridium sporosphaeroides, Clostridium limosum, Clostridium luticellarii). AC9 (Nonanoic acid) levels were significantly correlated with the abundance of Janthinobacterium lividum, Anaeromassilibacillus Fig. 2 Redundancy analysis (RDA) of bacterial communities and senegalensis, and Proteiniphilum saccharofermentans, chemical properties. Arrows denote the magnitude and directionality whereas AC10 (n-Decanoic acid) levels were positively of biogeochemical attributes associated with microbial community correlated with the abundance of Fermentimonas cae- structure. CM: Caloramator mitchellensis, JL: Janthinobacterium lividum, FA: Frondibacter aureus, SC: Syntrophomonas curvata, nicola, Janthinobacterium lividum, and Anaeromassili- PS: Petrimonas sulfuriphila, LT: Lutaonella thermophila, HS: bacillus senegalensis. Levels of AC11 (Benzoic acid) Hydrogenoanaerobacterium saccharovorans, TC: Thermoclostridium were also positively correlated with Proteiniphilum sac- caenicola, CS: Clostridium sporosphaeroides, SH: Sedimentibacter charofermentans abundance, and a positive correlation hydroxybenzoicus, CK: Clostridium kluyveri, PEM: Petrimonas mucosa, was observed between AC12 (Decanoic acid) and both PRS: Proteiniphilum saccharofermenta, LAP: Lactobacillus pasteurii, CL: Clostridium luticellarii, LP: Limnochorda pilosa, PM: Phocea massiliensis, Anaeromassilibacillus senegalensis and Proteiniphilum FC: Fermentimonas caenicola, PA: Proteiniphilum acetatigenes, saccharofermentans. AC13 (Benzeneacetic acid) was AS: Anaeromassilibacillus senegalensis, TA: Tepidanaerobacter positively correlated with Syntrophaceticus schinkii, Fer- acetatoxydans, CLL: Clostridium limosum, CJ: Clostridium jeddahense, mentimonas caenicola, and Janthinobacterium lividum. CHM: Christensenella massiliensis, LA: Lactobacillus acetotolerans, AM: In addition, AC15 (n-Hexadecanoic acid) was positively Ardenticatena maritima, SS: Syntrophaceticus schinkii, PP: Pelomonas puraquae, AR: Atopobium rimae correlated with Syntrophaceticus schinkii, Ardenticatena maritima, Fermentimonas caenicola, and Janthinobacte- rium lividum. With respect to esters, ES2 (Ethyl acetate) levels were with the marked heterogeneity of the local ecosystem in closely related to the abundance of Atopobium rimae, which Chinese strong-flavor liquor is brewed. As shown Syntrophaceticus schinkii, Fermentimonas caenicola, in Fig. 2, LA (Lactobacillus acetotolerans), AS (Anaero- Tepidanaerobacter acetatoxydans, and Limnochorda massilibacillus senegalensis), CK (Clostridium kluyveri), pilosa, while the levels of ES3 (Ethyl caproate), ES4 CL (Clostridium luticellarii), PRS (Proteiniphilum sac- (Ethyl butyrate), and ES5 (Ethyl heptanoate) all displayed charofermentans), PS (Petrimonas sulfuriphila), and positively related to the abundance of members of the CLL (Clostridium limosum), which were all found Clostridium genus. ES8 (Ethyl lactate) levels were closely in samples from the lower layer of the pit wall, were related to the abundance of Lactobacillus pasteurii and strongly positively correlated with total carbon, humus, Lactobacillus acetotolerans. Both ES9 (Ethyl caprate) and + + 2+ 3− K, NH-N, Mg , and PO , whereas CJ (Clostrid- 4 4 ES10 (Ethyl undecanoate) levels were positively corre- ium jeddahense), HS (Hydrogenoanaerobacterium sac- lated with Petrimonas sulfuriphila, whereas ES14 (Ethyl charovorans), SH (Sedimentibacter hydroxybenzoicus), phenylacetate) was significantly positively correlated with PEM (Petrimonas mucosa), AM (Ardenticatena mar- Anaeromassilibacillus senegalensis. itima), SS (Syntrophaceticus schinkii), CS (Clostridium AL1 (Hexanol) levels were significantly correlated with sporosphaeroides), FC (Fermentimonas caenicola), and JL the abundance of Clostridium jeddahense, Clostridium (Janthinobacterium lividum) were moderately positively kluyveri, Thermoclostridium caenicola, Clostridium correlated with these same chemical properties (Addi- sporosphaeroides, Clostridium limosum, and Clostridium tional file 1). luticellarii. AL3 (Isobutanol) was significantly related to Syntrophaceticus schinkii, Ardenticatena maritima, Jan- Correlations between bacterial communities and volatile thinobacterium lividum, Fermentimonas caenicola, and compounds Caloramator mitchellensis abundance. AL5 (1-Butanol) The correlative relationships between bacterial abun - was closely associated with Lactobacillus pasteurii, dance and specific volatile compounds in different pit Shoubao et al. AMB Express (2023) 13:3 Page 10 of 13 Fig. 3 Analysis of correlations between bacterial diversity and volatile compound content in different pit mud layers. *P < 0.05, **P < 0.01 Lactobacillus acetotolerans, Clostridium kluyveri, Ther - (2-phenyl-2-butenal) was significantly positively corre - moclostridium caenicola, Clostridium luticellarii, and lated with the abundance of Fermentimonas caenicola, Syntrophomonas curvata. AL6 (2,3-butanediol) levels Janthinobacterium lividum, Proteiniphilum acetatigenes, were significantly positively correlated with the abun - Phocea massiliensis, and Caloramator mitchellensis. VP2 dance of Lactobacillus pasteurii and Lutaonella ther- (2-Methylphenol) levels were also significantly correlated mophila, while AL9 (2,3-butanediol) was positively with pit mud Fermentimonas caenicola, Proteiniphi- correlated with Tepidanaerobacter acetatoxydans. lum acetatigenes, Phocea massiliensis, and Caloramator KE1 (2-octanone) levels were positively correlated mitchellensis abundance, whereas, VP3 (3-methylphe- with Lutaonella thermophila abundance, while AK4 nol) levels were closely associated with the abundance of Shoubao et al. AMB Express (2023) 13:3 Page 11 of 13 Petrimonas mucosa, Sedimentibacter hydroxybenzoicus, were present at particularly high levels in the bottom and and Hydrogenoanaerobacterium saccharovorans. lower wall pit mud layers. This may explain the observa - tion that the lower Zaopei layer yields better quality Chi- Discussion nese strong-flavor liquor relative to the middle and upper Chinese strong-flavor liquor production is characterized Zaopei layers. by its fermentation in a purpose-built pit (~ 3.3 m long, In addition to serving as a critical habitat for the 2.0 m wide, and 2.5 m deep). The bottom and walls of this microbes that facilitate the fermentation of Chinese liq- pit are covered with a microbe-rich type of clay known uor, pit mud-derived microbes are also responsible for as pit mud, with the nutrient composition of this pit mud the production of volatile flavoring compounds (Hu et al. generally being regarded as being critical to the composi- 2016). Zhang et al. (2021) found that the production of tion, stability, and evolution of the microbial community these volatile compounds in the context of fermentation found therein (Wu et al. 2022). The most critical phys - was the result of a series of metabolic reactions that were icochemical characteristics of pit mud include moisture, impacted by complex microbial community dynamics. + 3− + 2+ pH, NH -N, total carbon, humus, P O, K, Mg , and Dominant microbial species present in pit mud samples 4 4 2+ Ca content, as all of these have the potential to shape have previously been shown to include various species the growth and metabolic activity of local microbial of Clostridium, Lactobacillus, Bacillus, and methano- populations and to thereby shape the overall community gens (Ding et al. 2014). Clostridium species are gener- structure of the pit mud microflora. Prior studies have ally regarded as the primary functional bacteria in the demonstrated correlations between pit mud quality and pit mud, and their abundance is generally much higher certain properties such as pH, NH , available phospho- in older pit mud relative to newly prepared pit mud, thus 3− 2+ + 3− rus (PO ), and Ca , with pH, N H and PO levels shaping overall pit mud quality (Hu et al. 2015). In this 4 4 4 being positively correlated with pit mud quality, whereas study, Clostridium abundance was detected at high lev- 2+ Ca levels were negatively correlated with such qual- els in the lower layer of the pit mud wall wherein these ity (Zhang et al. 2020). The results of this study further microbes were found to play an important role in the pro- revealed that the physicochemical properties of pit mud duction of volatile aroma and flavor compounds. Indeed, differ in the different layers within the fermentation pit. strong correlations were observed between Clostridium Specifically, the highest levels of NH -N, total carbon, abundance and the levels of caproic acid, ethyl caproate, 2+ 3− humus, Mg , and PO were observed in the mud from ethyl butyrate, and hexanol in pit mud samples, in addi- the lower layer of the pit wall, followed by the mud from tion to a moderate correlation with butyric acid levels, the bottom layer of the pit. K levels in the bottom layer with all of these compounds being important aromatic of the pit were more similar to those in the lower layer of components present within Chinese strong-flavor liq - the pit wall and significantly increased relative to other uor. Lactic acid bacteria are another group of important 2+ pit mud layers. Maximum moisture, pH, and Ca lev- pit mud microorganisms that influence overall pit mud els were observed in the bottom pit mud layer. Together, quality. In this study, Lactobacillus species were pre- these results thus reaffirm that pit mud physicochemical sent primarily in the upper, middle, and bottom layers of properties exhibit spatially defined variations (Additional pit mud (Fig. 1). The lactic acid produced by these bac - file 2). teria can be used to synthesize ethyl lactate, which is a Li et al. (2019) previously found that the physicochemi- key flavoring compound in Chinese strong-flavor liquor cal variability present in different pit mud layers can (Gao et al. 2021). Most lactobacilli produce large quanti- contribute to differences in the composition of the local ties of lactic acid, which can also serve as a precursor for microbial community, in line with the results of the pre- lactic acetate production. Excess lactic acid production, sent study. For example, Lactobacillus acetotolerans, however, can drive ferrous lactate and calcium lactate Anaeromassilibacillus senegalensis, Proteiniphilum sac- formation, with these compounds ultimately contribut- charofermentans, Petrimonas sulfuriphila, Clostridium ing to pit mud degeneration (Hu et al. 2021). Lactoba- kluyveri, and Clostridium luticellarii, all of which were cillus abundance was found to be positively correlated located in the lower wall pit mud layer, were positively with ethyl lactate levels (Fig. 3). Moreover, Petrimonas + + 2+ correlated with total carbon, humus, K , NH-N, Mg , sulfuriphila abundance was closely associated with pro- 3− and PO (Fig. 2), whereas the abundance of Chris- pionic acid levels, Syntrophomonas curvata was related tensenella massiliensis, which was primarily located with 1-butanol levels, Proteiniphilum acetatigenes abun- in the upper wall layer, was negatively correlated with dance was significantly correlated with 2-methylphenol + 3− NH -N, total carbon, humus, and P O levels (Fig. 2). levels, Hydrogenoanaerobacterium saccharovorans abun- 4 4 This study further revealed that members of the Clostrid - dance was closely associated with 3-methylphenol lev- ium genus, which is a key functional genus in pit mud, els, and a significant positive correlation was observed Shoubao et al. AMB Express (2023) 13:3 Page 12 of 13 Funding between Caloramator mitchellensis abundance and levels Not applicable. of 2-phenyl-2-butenal. Pit mud quality is a key determinant of the quality Availability of data and materials Please contact author for data requests. and flavor of the liquor fermented therein, with such quality being a result of the physicochemical proper- Declarations ties of the pit mud as well as the flavor compounds and microbial species found therein. Here, a PCR-DGGE Ethics approval and consent to participate approach was used to analyze the microbial community Not applicable. structure in pit mud samples collected from different Consent for publication locations, with physicochemical properties and flavor Not applicable. compounds in these samples additionally being ana- Competing interests lyzed. Subsequent correlative analyses of these three The authors declare that they have no competing interest. factors revealed pit mud samples from the lower wall of the fermentation pit to contain higher levels of avail- 3− + Received: 2 November 2022 Accepted: 1 January 2023 able phosphorus (P O ), available potassium (K ), 2+ total carbon, humus, and Mg relative to other ana- lyzed samples. Moreover, the pH of the pit mud sam- ples from this layer was close to neutral, which may be References conducive to the metabolic activity of the functional Bi TR, Huang J, Zhang SY, Chen XR, Chen SQ, Mu Y, Cai XB, Qiu CF, Zhou RQ bacteria in this layer. As such, the lower wall layer of pit (2022) Difference of the microbial community and metabolite in pit mud with different age and position. Food Ferment Ind 48(2):231–237 mud was found to be of the highest quality, followed by Deng B, Shen CH, Shan XH, Zong-hua Ao ZH, Zhao JS, Shen XJ, Huang ZG the bottom pit mud layer. Studies of the volatile com- (2012) PCR-DGGE analysis on microbial communities in pit mud of cellars pounds found within pit mud further revealed that the used for different periods of time. J Inst Brew 118:120–126 Ding XF, Wu CD, Zhang LQ, Zheng J, Zhou RQ (2014) Characterization of lower wall pit mud layer contained the highest total lev- eubacterial and archaeal community diversity in the pit mud of chinese els of acids, esters, alcohols, and aldehydes. Together Luzhou-flavour liquor by nested PCR–DGGE. World J Microbiol Biotechnol these data offer new insight regarding the mecha - 30:605–612 Gao JJ, Liu GY, Li AJ, Liang CC, Ren C, Xu Y (2021) Domination of pit mud nisms underlying volatile compound accumulation in microbes in the formation of diverse flavour compounds during chinese pit mud in the context of Chinese strong-flavor liquor strong aroma-type Baijiu fermentation. LWT-Food Sci Technol 137:110442 production, providing a foundation for future efforts to Hu XL, Du H, Xu Y (2015) Identification and quantification of the caproic acid-producing bacterium Clostridium kluyveri in the fermentation of pit improve and maintain pit mud quality to enhance this mud used for chinese strong-aroma type liquor production. Int J Food fermentation process. Microbiol 214:116–122 Hu XL, Du H, Ren C, Xu Y (2016) Illuminating anaerobic microbial community and cooccurrence patterns across a quality gradient in Chinese liquor Supplementary Information fermentation pit muds. Appl Environ Microb 82(8):2506–2515 The online version contains supplementary material available at https:// doi. Hu XL, Wang KL, Yu M, Tian RJ, Fan HB, Sun JX, Zhang J, Yang X, Ma GL, Wei org/ 10. 1186/ s13568- 023- 01508-z. T (2020) Biodiversity and spatial heterogeneity of prokaryote commu- nity in strong-flavor Baijiu fermentation pit muds. Food Ferment Ind Additional file 1. Nucleotide sequences of the bands. 46(11):77–84 Hu XL, Yu M, Wang KL, Tian RJ, Yang X, Wang YL, Zhang ZG, Zhao XM, He Additional file 2: Table S1. Microorganism gray values of denaturing PX (2021) Diversity of microbial community and its correlation with gradient gel electrophoresis (DGGE) gels in pit mud samples collected physicochemical factors in Luzhou-flavor liquor pit mud. Food Res Dev from different positions within the fermentation pit. Lanes U, M, D, and B 42(2):178–185 respectively correspond to pit mud samples from the upper wall, middle Lei XJ, Yang KZ, Zhang JM, Zhang X, Luo QC, Qiao ZW, Zhao D, Zheng J (2020) wall, lower wall, and bottom layers of the cellar. Spatial distribution of aroma components in multi-grain strong-flavor liquor grains. Food Ferment Ind 46(21):48–54 Li XS, Li SL, Cao ZH, Li FQ, Yan PX, Song R, Guo SP (2018) Study on the change Acknowledgements of physicochemical indexes in different aged songhe distillery pit muds This study was financially supported by the academic funding for top talents and their correlation analysis. Liquor Mak 45(5):38–42 in disciplines (Specialties) of Anhui Provincial Higher Education Institutes Li XS, Li SL, Cao ZH, Yan PX, Guo SP, Sun JT (2019) Study on the change of (Grant gxbjZD2021087), the Major natural science research projects of Anhui physicochemical indexes and quality of wine produced in pit mud dur- Universities (Grant KJ2021ZD0117), the Key natural science research projects ing natural aging. Liquor Mak 46(3):31–35 in Anhui Universities (Grant KJ2021A0959), the innovation team of brewing Liao C, Wu SW, Huang XH, Xiao ML, Zeng TT, Xu XM (2010) Comparative industry microbial resources of Huainan normal university (Grant XJTD202005), analysis of physiochemical indexes between in functional pit mud of site and Huainan science and technology planning project (Grant 2021A2410). liquor and in common pit mud. Liquor-Making Sci Technol 2:86–90 Liu MK, Tang YM, Zhao K, Liu Y, Guo XJ, Ren DQ, Yao WC, Tian XH, Gu YF, Yi B, Author contributions Zhang XP (2017) Determination of the fungal community of pit mud YSB, PSC, and SCE designed the experimental program, participated in the in fermentation cellars for chinese strong-flavor liquor, using DGGE and examination and drafted the manuscript. YSB and JYL performed the feld Illumina MiSeq sequencing. Food Res Int 91:80–87 investigation and sample collection. 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AMB Express – Springer Journals
Published: Jan 7, 2023
Keywords: Pit mud; Bacterial community; Volatile flavor compounds; Chinese strong-flavor Baijiu
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