Exploring the impacts of traditional crafts on microbial community succession in Jiang-flavored Daqu.

Food Res Int

College of Liquor and Food Engineering, Key Laboratory of Fermentation Engineering and Biological Pharmacy of Guizhou Province, Guizhou University, Guiyang, Guizhou 550025, China. Electronic address:

Published: August 2022

The usage of traditional crafts during the fermentation process has a great influence on the quality of Jiang-flavored Daqu. This study focused on exploring the impacts of traditional crafts on Daqu microecology through high-throughput sequencing and statistical analyses. These results show that traditional crafts modulated microbial community succession through the regulation of temperature, moisture, and acidity. High temperatures (66 ± 1 °C) were conducive to the growth of microbes that produced flavor compounds, such as Bacillus and Thermoactinomyces. Low moisture content (correlated with 22 genera) and high acidity (8 genera) were mainly responsible for the decreased microbial diversity. Two flipping processes showed significant effects on the internal microecology and microbial metabolism. The first flip of Daqu may assist in regulating the microbial community structure and promoting the enrichment of flavor-functional microorganisms. The second flipping was reasonable for the enrichment of esterifying functional microorganisms, thus enhancing the esterifying power of Daqu with reduced acidity. Furthermore, analysis of the metabolic pathways at different stages of the process showed that the levels of flavor precursors and secondary metabolites within Jiang-flavored Daqu significantly increased during fermentation.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.foodres.2022.111568DOI Listing

Publication Analysis

Top Keywords

traditional crafts
16
microbial community
12
jiang-flavored daqu
12
exploring impacts
8
impacts traditional
8
community succession
8
daqu
6
microbial
5
traditional
4
crafts
4

Similar Publications

Digital PCR (dPCR) has transformed nucleic acid diagnostics by enabling the absolute quantification of rare mutations and target sequences. However, traditional dPCR detection methods, such as those involving flow cytometry and fluorescence imaging, may face challenges due to high costs, complexity, limited accuracy, and slow processing speeds. In this study, SAM-dPCR is introduced, a training-free open-source bioanalysis paradigm that offers swift and precise absolute quantification of biological samples.

View Article and Find Full Text PDF

Objective: While current multimodal approaches in the diagnosis and severity assessment of pneumonia demonstrate remarkable performance, they frequently overlook the issue of modality absence-a common challenge in clinical practice. Thus, we present the (RMT) model, crafted to bridge this gap. The RMT model aims to enhance diagnosis and severity assessment accuracy in situations with incomplete data, thereby ensuring it meets the complex needs of real-world clinical settings.

View Article and Find Full Text PDF

Clear cell renal cell carcinoma (ccRCC) characterised by its diversity and a tendency to defy standard therapeutic approaches. Amidst the advent of immunotherapy, it has become imperative to pinpoint prognostic indicators of the tumour microenvironment (TME) influence the efficacy of treatments. Employing single-cell RNA sequencing (scRNA-seq), this research delved into the diverse landscape of ccRCC, uncovering its complex underpinnings and pinpointing molecular avenues for therapeutic intervention.

View Article and Find Full Text PDF

Stimuli-responsive supramolecular assemblies have recently gained extensive attention in the biomedical field. Research focusing mainly on bioinspired functional supramolecular materials has shown great promise for potential drug delivery applications. Such materials can be engineered into 'smart' materials by utilizing various external stimuli such as pH, heat, light, and magnetic fields.

View Article and Find Full Text PDF

Background: Brain metastasis invasion pattern (BMIP) is an emerging biomarker associated with recurrence-free and overall survival in patients, and differential response to therapy in preclinical models. Currently, BMIP can only be determined from the histopathological examination of surgical specimens, precluding its use as a biomarker prior to therapy initiation. The aim of this study was to investigate the potential of machine learning (ML) approaches to develop a noninvasive magnetic resonance imaging (MRI)-based biomarker for BMIP determination.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!