Bioactive components and potential applications of tobacco fiber processing waste driven by artificial intelligence

Tobacco fiber processing waste (TFPW) is rich in cellulose, hemicellulose, lignin, and high-value bioactive components (nicotine, chlorogenic acid, solanesol, and polyphenols), presenting significant potential for resource utilization. Traditional disposal methods, however, suffer from severe resource wastage, high environmental pollution, and low value-added recovery. To address these challenges, this review systematically summarizes the recent progress in applying artificial intelligence (AI) technologies to the valorization of TFPW. Specifically, the integration of AI, machine learning (ML), and deep learning (DL) is comprehensively evaluated across multiple dimensions, including waste composition profiling, bioactive component extraction and separation, structural identification, functional prediction, materials design, and life cycle assessment. Leveraging these advanced tools enables TFPW utilization in medicine, agriculture, advanced materials, and sustainable energy. While conventional development approaches are often constrained by low extraction efficiency, prolonged evaluation cycles, and poor product specificity, AI can establish robust correlation models among process parameters, target component yields, extraction efficiencies, and biological activities, thereby optimizing downstream processing. Furthermore, ML and DL algorithms can predict key functionalities, including antioxidant, anti-inflammatory, antibacterial, and insecticidal activities, thereby accelerating the screening of key building blocks for bio-based materials. Ultimately, fusing AI with TFPW valorization enhances development efficiency, shortens research timelines, and advances green manufacturing. Finally, current challenges and future perspectives regarding database construction, model interpretability, and experimental validation systems are discussed.