Revealing the comprehensive effect of mechanization on sauce-flavor Daqu through high-throughput sequencing and multi-dimensional metabolite profiling

• Published in: Food research international 2024-09, Vol.191, p.114645, Article 114645
• Main Authors: Mu, Yu, Huang, Ying, Li, Dong, Zhu, Zhiyu, Yu, Shirui, Xie, Feng
• Format: Article
• Language: English
• Subjects: Mechanization; Metabolomics; Microbial community; Multivariate statistical analysis; Sauce-flavor Daqu
• ISSN: 0963-9969; 1873-7145; 1873-7145
• DOI: 10.1016/j.foodres.2024.114645

[Display omitted] •Multi-dimensional correlations between mechanization and Daqu quality were revealed.•Mechanization improved the activities of microbial metabolism and various enzymes.•Mechanization promoted the conversion of non-volatile compounds to volatile compounds.•Protease and saccharification activities were respectively contributed by bacteria and fungi.•LAB and Rhizopus were closely related to the production of volatile flavor compounds. Mechanization has emerged as a focal point in the modernization of traditional enterprises, offering standardized production and labor reduction benefits. However, little is known about how mechanization affects the microbiota and metabolite profiles of Daqu. To address this gap, we conducted a comprehensive comparison between traditional and mechanical sauce-flavor Daqu using a multi-omics approach. Results showed that mechanical Daqu exhibited higher acidity, amino acid nitrogen and enzyme activity, alongside lower fat and moisture levels. Following mechanization, lactic acid bacteria (LAB), Staphylococcus, Aspergillus and Saccharomycopsis were enriched and identified as biomarkers, whereas Oceanobacillus, Monascus and Scopulariopsis were notably decreased. Furthermore, significant disparities in metabolic profiles were observed between the two types of Daqu based on GC–MS, GC-IMS, and LC-MS/MS analyses. The content of volatile compounds was significantly higher in mechanical Daqu (332.82 ± 22.69 mg/kg), while that of non-volatile compounds was higher in traditional Daqu (753.44 ± 41.82 mg/kg). Moreover, OPLS-DA models identified 44 volatile and 31 non-volatile compounds as differential metabolites. Multivariate statistical analysis indicated that bacteria and fungi primarily contributed to protease and saccharification activities, respectively. Additionally, the co-occurrence network revealed that Oceanobacillus and Scopulariopsis were closely associated with non-volatile compound formation, while LAB and Rhizopus significantly influenced volatile compound production. These findings elucidate the multi-dimensional relationship between mechanization and Daqu quality, offering insights to advance the modernization of traditional industries.