Abstract: Spectroscopic techniques have been widely applied in the field of microbial fermentation owing to their rapidity, non-invasiveness, and real-time monitoring capability. As a representative fermented beverage, baijiu requires the accurate quantification of critical components throughout its brewing process, which is crucial for quality assurance and the optimization of production parameters. Recent rapid advancements in artificial intelligence (AI) have provided powerful computational tools for enhancing spectroscopic data processing. This review systematically outlines the fundamental principles and application scenarios of spectroscopic techniques, with a specific focus on the application of machine learning and deep learning algorithms for spectral interpretation. Furthermore, it synthesizes recent developments in integrated AI-driven multi-spectral analysis for the dynamic monitoring of the baijiu fermentation process, the assessment of microbial metabolic activity, the quality control of fermentation products, and the vintage traceability of baijiu. Recent studies have covered the detection of key links in the production of baijiu such as raw materials, Jiuqu (starter culture), pit mud, fermented grains, and finished products, thereby establishing a systematic framework for the application of intelligent spectroscopic techniques in baijiu fermentation and providing scientific support for the intelligent development of the baijiu industry.

Key words: spectroscopic analysis; artificial intelligence algorithms; baijiu production; quality control; process monitoring