Cooperative Response of Pichia kudriavzevii and Saccharomyces cerevisiae to Lactic Acid Stress in Baijiu Fermentation

A flavoromics strategy for the differentiation of different types of Baijiu according to the non-volatile organic acids

Non-volatile organic acids (NVOAs) in 12 main flavor types of Baijiu were analyzed by a derivatization method combined with GC-MS and 38 NVOAs were quantified. Meanwhile, a flavoromics strategy based on the contents of NVOAs in the 12 flavor types of Baijiu was successfully used to the differentiation of Baijiu. PLS-DA models (explained variation, predictive capability) were used to consider different categories: fermentation process (0.931, 0.870), starter (0.921, 0.834), fermentation container (0.899, 0.810) and raw material (0.951, 0.909). Based on the selected categories, suitable separations were achieved, and the classification ability of these models were nearly 100%. As a result, the model demonstrated its ability to perfectly distinguish different types of Baijiu. Seventeen potential markers were identified by variable importance in projection method and were further processed using heatmap and hierarchical cluster analysis, indicating that the NVOAs had great discrimination power to differentiate Baijiu.

Deciphering Succession and Assembly Patterns of Microbial Communities in a Two-Stage Solid-State Fermentation System

Although the importance of microbiota in the natural environment and in industrial production has been widely recognized, little is known about the formation and succession patterns of the microbial community, particularly secondary succession after disturbance. Here, we choose the Xiaoqu liquor brewing process as an experimental model in which sorghum grains were first aerobically saccharified and then anaerobically fermented after being stirred and acidified to explore multistage community succession patterns. We analyzed microbial composition, physicochemical factors, and metabolites of brewing grains inoculated with two different starters, pure starter and traditional starter, respectively. Two groups showed similar succession patterns where the saccharification microbiota was mainly derived from starters, while environmental microorganisms, mainly Lactobacillaceae and Saccharomyces, dominated the fermentation microbiota regardless of the original saccharification community composition. Species replacement shaped the bacterial community, while species replacement and loss both contributed to fungal community succession in both groups. Grain acidification and hypoxia led to the succession of bacterial and fungal communities during fermentation, respectively. Despite inoculation with starters containing different microorganisms, similar microbial communities during the fermentation stage of the two groups exhibited similar metabolite composition. However, higher abundance of Rhizopus in the saccharification of the pure starter group led to more alcohols, while higher abundance of Monascus and Saccharomycopsis in the traditional starter group promoted acid and ester metabolism. These results revealed the microbial succession patterns of two-stage liquor brewing and its influence on flavor metabolism, which could be used to regulate the microbial community in food fermentation to further promote the modernization of the fermented food industry.

IMPORTANCE Revealing formation and assembly mechanisms of microbiota can help us to understand and further regulate its roles in the ecosystems. The Xiaoqu liquor brewing system is a tractable microbial ecosystem with low complexity. This two-stage microbial ecosystem can be used as an experimental model to analyze the multistage temporal succession pattern of microbial communities. Our results demonstrated the dynamic composition and succession pattern of a microbial community in the two-stage liquor brewing system. The results also revealed the microbial origins determining community composition, the ecological processes dominating microbial community succession patterns, the determinants affecting microbial community successions, and the effect of microbial community changes on metabolite synthesis. Overall, our study not only provides an insight into multistage succession patterns of microbial communities in liquor brewing systems but also provides reference for optimizing the quality of fermented products, which will be helpful to understand the succession patterns of microbial communities in other natural ecosystems.

Correlation: Between Autochthonous Microbial Diversity and Volatile Metabolites During the Fermentation of Nongxiang Daqu

Daqu is an important saccharifying and fermenting agent. It provides various microorganisms and enzymes for the fermentation of Baijiu and plays a vital role in the formation of Baijiu flavor. However, it is difficult to obtain information on microbial growth and metabolism in time for Daqu production. Therefore, the “Qu Xiang” obtained by smelling is an important index in the traditional production process to evaluate the microbial fermentation in the process of Daqu-making, “Qu Xiang” mainly represents the volatile flavor compounds in Daqu. The microbial diversity and volatile metabolites on 0, 6, 16, and 29 days of the fermentation process were measured using high-throughput sequencing and gas chromatography–mass spectrometry. Significant differences were found in the composition of the microbial community. Pseudomonas, Weissella, Bacillus, and Pelomonas were the main bacterial genera. Alternaria, Rhizopus, and Pichia are the main fungal genera. A total of 32 differential volatile metabolites were detected in samples at four time points using differential metabolic analysis. The correspondence of prevailing microorganisms with differential metabolites distinguished by Spearman correlation and two-way orthogonal partial least square analysis show that Saccharopolyspora exhibited a significant connection for the 12 differential metabolites. A significant positive correlation was observed between Rhizomucor and 13 different metabolites. These findings further understanding of the metabolism of microorganisms in Daqu fermentation and also help to control the microorganisms in the Daqu-making process, to obtain more stable Baijiu products.

Variations of Soybean Meal and Corn Mixed Substrates in Physicochemical Characteristics and Microbiota During Two-Stage Solid-State Fermentation

Corn germ meal (CGM) and corn gluten feed (CGF) are the two main corn byproducts (CBs) obtained from corn starch extraction. Due to their high fiber content, low protein content, and severe imbalance of amino acid, CBs are unable to be fully utilized by animals. In this study, the effect of microorganism, proteases, temperature, solid-liquid ratio, and time on nutritional properties of CB mixture feed (CMF) was investigated with the single-factor method and the response surface method to improve the nutritional quality and utilization of CBs. Fermentation with Pichia kudriavzevii, Lactobacillus plantarum, and neutral protease notably improved the nutritional properties of CMF under the fermentation conditions of 37°C, solid-liquid ratio (1.2:1 g/ml), and 72 h. After two-stage solid-stage fermentation, the crude protein (CP) and trichloroacetic acid-soluble protein (TCA-SP) in fermented CMF (FCMF) were increased (p < 0.05) by 14.28% and 25.53%, respectively. The in vitro digestibility of CP and total amino acids of FCMF were significantly improved to 78.53% and 74.94%, respectively. In addition, fermentation degraded fiber and provided more organic acids in the CMF. Multiple physicochemical analyses combined with high-throughput sequencing were performed to reveal the dynamic changes that occur during a two-stage solid-state fermentation process. Generally, Ascomycota became the predominant members of the community of the first-stage of fermentation, and after 36 h of anaerobic fermentation, Paenibacillus spp., Pantoea spp., and Lactobacillales were predominant. All of these processes increased the bacterial abundance and lactic acid content (p < 0.00). Our results suggest that two-stage solid-state fermentation with Pichia kudriavzevii, Lactobacillus plantarum, and protease can efficiently improve protein quality and nutrient utilization of CMF.

Microbial Community Succession and Its Environment Driving Factors During Initial Fermentation of Maotai-Flavor Baijiu

The microbial composition and environmental factors can take a great influence on community succession during the solid-state fermentation (SSF) of Maotai-flavor Baijiu. In this paper, high-throughput sequencing was used to reveal the dominant microorganisms and the evolution process of microbial community structure in the initial fermentation of Maotai-flavor Baijiu. The correlation analysis was carried out for the relationship between physicochemical factors and fermented microbes. The results showed that microorganisms were obviously enriched and the diversity of bacteria and fungi showed a downward trend during the heap fermentation process of Maotai-flavor Baijiu. However, the diversity of fungi in the pit fermentation process increased. Generally, Lactobacillus, Pichia, and Saccharomyces were the dominant microorganisms in the initial fermentation of Maotai-flavor Baijiu. According to the redundancy analysis, we found that reducing sugar was the key driving factor for microbial succession in the heap fermentation, while acidity, alcohol, and temperature were the main driving forces in pit fermentation. This study revealed the microbial succession and its related environmental factors in the initial fermentation of Maotai-flavor Baijiu, which will enrich our knowledge of the mechanism of solid-state liquor fermentation.

Mannitol and erythritol reduce the ethanol yield during Chinese Baijiu production

Chinese Baijiu is prepared using multiple microbial strains and complex metabolites by simultaneous saccharification and fermentation (SSF). Yeasts are challenged by various endogenous and exogenous factors, detrimentally affecting the ethanol yield. It is imperative to identify and control inhibitory factors. In the present study, microbial taxa and metabolites during Baijiu fermentation were evaluated to identify inhibitors of ethanol production. We found that filamentous fungi and Bacillus, contributing to saccharification, were negatively related to the ethanol content (Spearman's |ρ| > 0.5, P < 0.05). To explore how they affect ethanol production, ten filamentous fungi and three Bacillus strains were isolated. In addition to glucose and maltose, polyols were simultaneously generated by filamentous fungi and Bacillus via the hydrolysis of starch, among which mannitol and erythritol had the highest contents of up to 41.56 ± 2.01 g/kg and 16.16 ± 1.13 g/kg, respectively. The presence of mannitol and erythritol inhibited ethanol production by the functional yeasts Saccharomyces cerevisiae and Pichia kudriavzevii. The presence of 10.0 g/L mannitol significantly (P < 0.01) decreased the ethanol yield of S. cerevisiae by 12.67% (from 39.34 ± 0.02% to 32.71 ± 0.49%). These results revealed that polyols may inhibit the production of Baijiu and other fermented foods, suggesting that the origin and influence of polyols should be a focus of future research.

A novel acid-sensitive quantum dot sensor array for the identification of Chinese baijiu

The organic acid content plays important roles in the flavor and taste of Chinese baijiu. Developing a detection and discrimination technique for organic acids and employing it as a basis in baijiu classification has great practical significance. We employed 3 kinds of acid-sensitive quantum dots (QDs) to construct a fluorescence sensor array for the detection and identification of organic acids in baijiu. We report the first directional use of array sensing detection technology for the evaluation of organic acids in baijiu. Linear discriminant analysis (LDA) was successfully employed to evaluate the ability of the as-developed sensor array to classify organic acids. The Euclidean distance analysis was introduced to prove the provided sensor array possesses good quantitative detection. On this basis, our sensor array was successfully applied to distinguish 16 kinds of baijiu samples. The results were supported by principal component analysis (PCA), LDA, and systematic cluster analysis (HCA). Furthermore, Pearson correlation results indicated a strong correlation between the detection results and the organic acids in baijiu. This simple and accurate method shows potential for quality control and detection in baijiu factories and markets.