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Machine learning-driven identification of key chemical determinants of tobacco leaf sensory irritation
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DOI：10.3389/fchem.2026.1893276.png)
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Abstract
En 中文
IntroductionTo identify the key chemical components affecting the sensory irritation of tobacco leaves; a binary classification model for high and low irritation was constructed based on 78 chemical components and sensory evaluation scores of 353 tobacco leaf samples.MethodsFirst; the median absolute deviation (MAD) method was applied to remove outliers from the high- and low-irritation samples. Then; the ReliefF algorithm was applied for dimensionality reduction; selecting 33 core features to eliminate data redundancy. Using the selected features; a random forest (RF) algorithm was employed to build the classification model; and the optimal number of decision trees was determined to be 60.ResultsThe ReliefF-RF model achieved an accuracy of 84.38% on an independent test set; with precision; recall; and F1-score all at 86.49%; outperforming the original RF model as well as other machine learning models such as support vector machine (SVM) and k-nearest neighbors (KNN). Through feature importance evaluation; eight key chemical indicators were identified: total nitrogen; total alkaloids; cryptochlorogenic acid; oleic acid + linolenic acid; reducing sugar; sugar-nitrogen ratio; Fru-Asp; and neochlorogenic acid.DiscussionSHapley Additive exPlanations (SHAP) analysis revealed that higher levels of nitrogenous compounds were strongly associated with increased irritation; whereas elevated levels of sugar components; specific organic acids; and amino acid derivatives were associated with reduced irritation. Notably; Fru-Asp exhibited a complex non-linear response; where both extremely high and low levels contributed to higher irritation. This study provides a useful reference and data support for the targeted regulation of cigarette irritation.
Keywords:
tobacco
random forest
feature selection
chemical components
irritation
relieff
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