Multi-omics Technology-Based Flavor Formation Mechanisms and Intelligent Quality Control Research in Strong-Flavor Baijiu

Shaoqin Shang; Yang Qing; Deping Zhang

doi:10.54097/rj6c1v61

Authors

Shaoqin Shang
Yang Qing
Deping Zhang

DOI:

https://doi.org/10.54097/rj6c1v61

Keywords:

Strong-flavor Baijiu, Multi-omics, Flavor Formation, Intelligent Quality Control, Machine Learning, Fermentation Optimization

Abstract

Strong-flavor baijiu (SFB), accounting for over 70% of China’s liquor production, owes its distinctive "rich cellar aroma and mellow sweetness" to interactions among more than 800 flavor compounds and complex microbial communities. Traditional production faces challenges including uncontrollable microbiota, imprecise flavor analysis, and subjective quality evaluation. This paper reviews the application of multi-omics technologies (flavoromics, metabolomics, metagenomics) in elucidating flavor formation mechanisms: flavoromics identifies key compounds (e.g., ethyl hexanoate as the primary ester); metabolomics decodes critical pathways (e.g., fatty acid β-oxidation in ester synthesis); metagenomics reveals functional microbiota (e.g., Clostridium and Lactobacillus). It further explores intelligent quality control systems integrating IoT sensors, machine learning (e.g., XGBoost for flavor prediction), and real-time monitoring, which have improved premium yield by 12%, production efficiency by 30%, and reduced labor costs by 40% in leading enterprises. Challenges include multi-omics data integration and high implementation costs, with future directions focusing on molecular sensory modeling, sustainability, and technology accessibility for small producers. This review highlights the shift from experience-based brewing to data-driven innovation, preserving tradition while enhancing quality and efficiency.

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