Abstract:

To study the influence of drying parameters of the re-drying machine on the sensory quality stability of Henan strong-aroma type flue-cured tobacco leaves after re-drying, ‘Zhongyan 100’ C3F was used as the experimental material. The effects of different drying curves on the sensory quality of re-dried tobacco leaves were investigated to determine the optimal drying curve suitable for Henan strong-aroma type flue-cured tobacco leaves. During the production process of threshing and re-drying, drying parameters, incoming material parameters, and environmental parameters were collected, and sensory quality evaluations were conducted on the re-dried tobacco leaves. Finally, a drying parameter control model was constructed using the BP neural network algorithm to predict the results of sensory evaluation. The results showed that by applying low-temperature slow roasting technology, adopting a parabolic constant temperature mode for the drying curve, controlling the temperature difference between drying zones 1, 2, and 3 within (4.0±1.0)℃, the temperature difference between drying zones 3, 4, 5, and 6 within (2±1)℃, the total temperature of the drying zone within (380±5.0)℃, and maintaining the moisture content in the left and right cold rooms at (9.0±0.5)% with a moisture difference of ±0.8%, both the aroma quality and aroma quantity of the re-dried tobacco leaves were improved, the fineness of the smoke was enhanced, and the aftertaste was improved to varying degrees. By using the model algorithm to predict the intrinsic quality of tobacco leaves, the sensory quality of the re-dried tobacco leaves within the module was basically consistent with the sensory evaluation results predicted by the model. When parameters are adjusted, the predicted results can be used to guide production in a timely manner.

Key words: strong-flavor tobacco, re-drying, drying parameter, low-temperature slow roasting, sensory quality, stability, BP neural network