Evaluation of Flavor Type of Tobacco Blending Module: A Prediction Model Based on Near-Infrared Spectrum

Abstract

Near-infrared spectrum technology is extensively employed in assessing the quality of tobacco blending modules, which serve as the fundamental units of cigarette production. This technology provides valuable technical support for the scientific evaluation of these modules. In this study, we selected near-infrared spectral data from 238 tobacco blending module samples collected between 2017 and 2019. Combining the power of XGBoost and deep learning, we constructed a flavor prediction model based on feature variables. The XGBoost model was utilized to extract essential information from the high-dimensional near-infrared spectra, while a convolutional neural network with an attention mechanism was employed to predict the flavor type of the modules. The experimental results demonstrate that our model exhibits excellent learning and prediction capabilities, achieving an impressive 95.54% accuracy in flavor category recognition. Therefore, the proposed method of predicting flavor types based on near-infrared spectral features plays a valuable role in facilitating rapid positioning, scientific evaluation, and cigarette formulation design for tobacco blending modules, thereby assisting decision-making processes in the tobacco industry.