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Leaf aging is a significant process during herbaceous plant senescence, which is influenced by various internal and external factors. During leaf aging, chlorophyll catabolism is one of the most important metabolism pathways and results in leaf yellowing. Understanding the underlying mechanism is important for the regulation of senescence in tobacco leaf. However, there are few studies on explaining tobacco leaf senescence from the proteomics level. Here, photosynthesis experiments, cell ultrastructure, and proteomics were used to study tobacco leaves of different growth stages. We applied iTRAQ-based quantitative proteomics and parallel reaction monitoring (PRM) to determine the accumulation of proteins in aging tobacco leaves. Overall, we screened 4747 proteins. The result of KEGG pathways analysis showed that differently expressed proteins (DEPs) were involved in four pathways: metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, and starch and sucrose metabolism. This would be first report based on iTRAQ-PRM technique, in which we identified proteins related to photosynthesis showed a differently expressed during senescence stage in flue-cured tobacco plants.

In this study, aroma quality, aroma quantity, diffusiveness and offensive odor predictions were modeled using a back propagation neural network (BPNN). The model was built from chemical components and sensory quality data features of flue-cured tobacco leaf samples. The results showed that the BPNN model adapted the optimized hidden layer well and had excellent generalization ability and robustness. The coefficients of determination (R2) for the indices of tobacco aroma characteristics based on the BPNN model, namely, aroma quality, aroma quantity, diffusiveness and offensive odor, were all above 0.7. The accuracies of the model parameters (mean squared error, regression mean standard error , and mean absolute error) were better than those of the model built using stepwise regression analysis. These results indicate that the BPNN prediction model is reliable and can accurately predict the sensory aroma characteristic qualities of flue-cured tobacco leaf.