首页|New Research on Machine Learning from Zhengzhou Tobacco Research Institute Summarized (Quantitative analysis of pyrolysis characteristics and chemical components of tobacco materials based on machine learning)
New Research on Machine Learning from Zhengzhou Tobacco Research Institute Summarized (Quantitative analysis of pyrolysis characteristics and chemical components of tobacco materials based on machine learning)
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Data detailed on artificial intelligence have been presented. According to news originating from Zhengzhou, People’s Republic of China, by NewsRx correspondents, research stated, “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.” Our news reporters obtained a quote from the research from Zhengzhou Tobacco Research Institute: “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 R2 are above 0.85. While the performance of SVR and GPR models was comparable, the R2 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℃-400℃.”
Zhengzhou Tobacco Research InstituteZhengzhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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Frontiers Media Sa
