Introduction

Chinese rice wine (CRW), a traditional alcohol drink, is fermented from rice with wheat Qu and yeast. It is one of the oldest drinks in the world, along with beer and wine1. CRW is also called “liquid cake” because of its abundant nutrition and pleasant aroma2. Sorghum is a kind of natural and high-nutrition functional food that is rich in dietary fiber, protein, fat, folic acid, ferrum and other microelements. Sorghum is the main raw material for producing spirits. Chinese liquor brewed with sorghum, such as Maotai-flavored liquor, has long enjoyed a good reputation. However, with the changes in China’s policies on the liquor industry and the increase in people’s living standards, high-nutrition, low-alcohol CRWis becoming increasingly popular with consumers. Brewing CRW with sorghum not only increases the value of sorghum but also combines these two nutritious foods together.

Flavor differences can be caused by many factors, such as the raw ingredients used in the fermentation, fermentation conditions, distillation practices, and aging processes3. ** sequence42.

Operational taxonomic units (OTUs) were clustered with a 97% similarity cutoff using UPARSE (version 7.1 http://drive5.com/uparse/), and chimeric sequences were identified and removed using UCHIME. The taxonomy of each 16 S rRNA gene sequence was analyzed by an RDP Classifier algorithm (http://rdp.cme.msu.edu/) against the Silva (SSU123) 16 S rRNA database using a confidence threshold of 70%. The taxonomy of each ITS gene sequwence was analyzed by Unite (Release 6.0 http://unite.ut.ee/index.php)43.

Alpha rarefaction was performed in QIIME (version 1.7.0) using the Chao1 estimates of species abundance44, while observed species estimation of the amount of unique OTUs found in each sample and Shannon index45 were calculated. Cluster analysis was preceded by NMDS46. For the QIIME calculation, the beta diversity of the unweighted and weighted UniFrac distances was used for the UPGMA clustering and principal coordinate analysis47. To identify the differences in the bacterial communities between the two groups, similarities were analyzed using the Bray-Curtis dissimilarity distance matrices.

The metagenomic functional composition prediction analysis for all biofilm samples from 16 S data in the latest Kyoto Encyclopedia of Genes and Genomes (KEGG) database was performed using PICRUSt pipeline as described by Langille et al.48.

To associate the microbiota and flavour compounds, the significance of correlations between the microbial genera and flavour were tested(pearson correlation). The p values were adjusted by FDR using Benjamini–Hochberg method, the cutoff of adjusted p value was set as 0.05. The association network was constructed by using all significant associations, and it was displayed using R language programming.