Analysis of the fungal community structure and sensory quality of high-temperature Daqu via high-throughput sequencing and the PAM algorithm

The present study analysed the sensory quality and fungal community structure of three color types of high-temperature Daqu (HTD) produced in Qingzhou using electronic nose, electronic tongue, and high-throughput sequencing technology. The data were compared to the fungal data obtained from HTD produced in Xiangyang via the "partitioning around medoids" (PAM) clustering algorithm. PAM analysis indicated that all HTD samples from the two regions could be divided into two clusters. Cluster I samples were mainly characterized by Thermomyces, and cluster II samples were mainly characterized by Aspergillus and Thermoascus. Cooccurrence network analysis revealed that the correlations between fungal communities were stronger in the HTD dominated by Aspergillus and Thermoascus. The identification of key species and core operational taxonomic units demonstrated that the differences in the fungal community structure between the two HTD clusters were related to the abundance of certain fungal groups. Correlation analysis between fungal genera and sensory quality parameters showed that Thermomyces-dominated HTD had lower aftertaste-A, aftertaste-B, and organic sulfide and terpene content but a higher richness of flavor. Meanwhile, HTD dominated by Aspergillus and Thermoascus exhibited the opposite traits, and its sourness was relatively higher.

Authors

Institutions

Publication Details

Journal
FEMS Microbiology Letters
Published
2025-01-01
DOI
https://doi.org/10.1093/femsle/fnaf077
Primary Topic
Fermentation and Sensory Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of the fungal community structure and sensory quality of high-temperature Daqu via high-throughput sequencing and the PAM algorithm

Bangkun Wang, Wenchao Cai, Zhuang Guo, Chunhui Shan et al.
FEMS Microbiology Letters
Fermentation and Sensory Analysis
article

Analysis of the fungal community structure and sensory quality of high-temperature Daqu via high-throughput sequencing and the PAM algorithm

Bangkun Wang, Wenchao Cai, Zhuang Guo, Chunhui Shan, Yurong Wang, Dongying Ge, Minwan Liu
article en

Abstract

The present study analysed the sensory quality and fungal community structure of three color types of high-temperature Daqu (HTD) produced in Qingzhou using electronic nose, electronic tongue, and high-throughput sequencing technology. The data were compared to the fungal data obtained from HTD produced in Xiangyang via the "partitioning around medoids" (PAM) clustering algorithm. PAM analysis indicated that all HTD samples from the two regions could be divided into two clusters. Cluster I samples were mainly characterized by Thermomyces, and cluster II samples were mainly characterized by Aspergillus and Thermoascus. Cooccurrence network analysis revealed that the correlations between fungal communities were stronger in the HTD dominated by Aspergillus and Thermoascus. The identification of key species and core operational taxonomic units demonstrated that the differences in the fungal community structure between the two HTD clusters were related to the abundance of certain fungal groups. Correlation analysis between fungal genera and sensory quality parameters showed that Thermomyces-dominated HTD had lower aftertaste-A, aftertaste-B, and organic sulfide and terpene content but a higher richness of flavor. Meanwhile, HTD dominated by Aspergillus and Thermoascus exhibited the opposite traits, and its sourness was relatively higher.

FEMS Microbiology LettersVol. 372
Shihezi University (CN), Qingzhou City People's Hospital (CN), Hubei University of Arts and Science (CN)
Openalex Percentile: Top 77%
Fermentation and Sensory Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.