上海烟草集团

As a major cash crop, tobacco quality improvement is constrained by the limited effectiveness of conventional approaches, including traditional breeding and agronomic regulation. This review focuses on the application of metabolic engineering to enable precise and targeted modulation of tobacco quality traits. We first analyze the key factors and underlying mechanisms governing aroma, health-related components, functional metabolites, and combustion characteristics. We then summarize recent advances in metabolic engineering strategies, including pathway rewiring, metabolic flux redistribution, enzyme engineering, and regulatory-network modulation, for optimizing the biosynthesis and accumulation of target compounds such as terpenoids, polyphenols, and alkaloids. In addition, selected integrative approaches that support metabolic regulation and quality formation are briefly discussed. Finally, we highlight current challenges and outline future research directions to facilitate more precise and efficient improvement of tobacco quality.

Abstract Tobacco remains a vital global cash crop with diverse economic and industrial uses. Traditional improvement methods, such as breeding and chemical treatments, often lack long-term precision and sustainability. Conversely, metabolic engineering—combining systems and synthetic biology—enables precise modifications of tobacco's metabolic pathways, allowing control over key compounds such as terpenoids and alkaloids. This approach offers a more efficient and environmentally friendly alternative. The review explores the challenges and mechanisms associated with four major tobacco quality traits—aroma, health, functionality, and combustion—and examines corresponding metabolic engineering strategies. It concludes by summarizing key findings and suggesting future research avenues to advance the field.

The study innovatively utilized a combination of smoking machine and Cambridge filter to effectively extract the aroma compounds generated during smoking of cigarettes, and employed three different pretreatment methods to obtain optimal cigarette pretreatment procedures. In addition, the aroma components of different cigarettes were identified by different instruments such as gas chromatography-mass spectrometry. Combined with the calculation of Odor Activity Values (OAV), PCA analysis and sensory evaluation were performed on the five types of cigarettes to reveal differences and similarities in their aroma profiles. ### Competing Interest Statement The authors have declared no competing interest.

The high occupancy rate and low utilization rate of low-grade tobacco leaf resources in China have been recognized as major challenges for the tobacco industry.Excessive content of macromolecular substances in tobacco leaves is one of the key factors leading to inferior quality of tobacco leaves.Using biotechnology to degrade these macromolecular substances,balance chemical components and increase aroma substances is one of the effective ways to improve the quality of low-grade tobacco leaves.In recent years,the application of enzyme technology and microbial technology in improving the quality of tobacco leaves has become a research hotspot in the industry.The influence of macromolecular substances such as starch,protein,pectin and cellulose in tobacco leaves on quality was introduced,and the mechanisms and related research progress of enzymes and microor-ganisms in tobacco fermentation were sorted out and summarized,aiming to provide reference and inspiration for further promoting the development of this field.

This study proposes a new paradigm for identifying consumer preferences based on electroencephalogram(EEG).Using 4 types of cigarette packaging,including Panda and Zhonghua,as visual stimuli,neural response data were collected from 326 volunteers.Stable EEG was captured after 4 seconds of stimulation,and time-domain statistical features,nonlinear dynamic entropy features,and delta gamma frequency band power spectral density(PSD)were extracted.A total of 19 channel features were used to construct a 608 dimensional cross modal feature vector seven machine learning algorithms,including random forest and support vector machine,were used for binary classification modeling.Among them,random forest achieved the highest accuracy on the independent test set,reaching 78.63％The results indicate that multidimensional EEG feature fusion can accurately identify consumers'implicit preferences,providing quantifiable scientific basis for optimizing tobacco packaging design.