Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning

Ranran Kou¹Cong Wang¹Jinxia Liu²Ran Wan¹Zhe Jin²Le Zhao¹Youjie Liu²Junwei Guo¹Feng Li²Hongbo Wang¹Song Yang^1*Cong Nie^1*

• ¹Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, China
• ²China Tobacco Jilin Industrial Co., Ltd., Changchun, China

Tobacco leaf position is closely associated with its quality whose material basis is the chemical components of tobacco leaf. In recent years, near-infrared (NIR) spectroscopy combined with algorithmic models has emerged as a popular method for identifying the tobacco leaf position.However, when applied to leaf position discrimination, these models often rely on principal components derived from dimensionality-reduced spectral signals, resulting in limited interpretability and difficulty in identifying key chemical components. Chemical composition data combined with algorithmic models can also be used to discriminate tobacco leaf positions. However, the acquisition of chemical components relies on traditional instrumental analytical methods. As a result, the acquisition of chemical composition data is time-consuming and labor-intensive, involving only a limited number of compounds. The study proposes a novel approach that integrates machine learning with advanced interpretability techniques for both tobacco leaf position discrimination and analysis.Based on the 70 tobacco leaf chemical components obtained using near-infrared rapid analysis