ArticleFrontiers in chemistry2024
Quantitative analysis of pyrolysis characteristics and chemical components of tobacco materials based on machine learning.
Article in Frontiers in chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Reductive soil disinfestation alleviates continuous cropping obstacles in tobacco by reshaping microbial communities and improving soil properties in karst regions.Frontiers in microbiology · 2026Article
- Analysis of the key component puff-by-puff transport in special segments and aerosol for electrical heated tobacco product.Frontiers in chemistry · 2024Article
- Effect of plant fibers on the physical properties of slurry-processed reconstituted tobacco.Frontiers in chemistry · 2024Article
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Authors and funding
11 authors.
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Abstract
To investigate the quantitative relationship between the pyrolysis characteristics and chemical components of tobacco materials, various machine learning methods were used to establish a quantitative analysis model of tobacco. The model relates the thermal weight loss rate to 19 chemical components, and identifies the characteristic temperature intervals of the pyrolysis process that significantly relate to the chemical components. The results showed that: 1) Among various machine learning methods, partial least squares (PLS), support vector regression (SVR) and Gaussian process regression (GPR) demonstrated superior regression performance on thermogravimetric data and chemical components. 2) The PLS model showed the best performance on fitting and prediction effects, and has good generalization ability to predict the 19 chemical components. For most components, the determination coefficients
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