Quantitative analysis of pyrolysis characteristics and chemical components of tobacco materials based on machine learning.

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 R 2 are above 0.85. While the performance of SVR and GPR models was comparable, the R 2 for most chemical components were below 0.75. 3) The significant temperature intervals for various chemical components were different, and most of the affected temperature intervals were within 130°C-400°C. The results can provide a reference for the materials selection of cigarette and reveal the possible interactions of various chemical components of tobacco materials in the pyrolysis process.