烤烟致香成分与香气质量的相关性分析

Correlation Analysis on Aroma Components and Flavor Quality of Flue-cured Tobacco Leaves

采用数据挖掘方法中的Wrapper特征提取法得到显著影响烤烟香气质量的关键指标,并与简单相关系数结论进行对比。结果表明,影响香型的主要是香叶基丙酮、戊酸、酸性总量、巨豆三烯酮、茄酮等20种致香成分;影响香气量的主要是香叶基丙酮、2-乙酰吡咯、四甲基吡嗪、苯甲醛、茄酮、异戊酸、β-大马酮等25种致香成分;影响香气质的主要是巨豆三烯酮、异戊酸、茄酮、中性总量、新植二烯、香叶基丙酮、法尼基丙酮、4-甲基戊酸、2-甲基吡嗪、β-大马酮、吡啶等18种致香成分。经过特征提取后所建立的致香成分与香气质量的模型性能有较大地提高,并且RBF神经网络模型比分段线性回归模型的预测精度更高。

By using the Wrapper feature extraction algorithm in data mining methods, the key indicators were extracted, which significantly impacted the aroma quality of flue-cured tobacco. The method was compared with the simple correlation coefficient method. The results showed that there were 20 kinds of aroma components which mainly impacted the aroma type, such as geranylaceione, pentanoic acid, total acid, megastigmatrienone, solanone, while 25 types of aroma components which mainly impacted the volume of aroma, such as geranylacetone, 2-acetyl pyrrole, tetramethylpyrazine, benzaldehyde, solanone, isovaleric acid, β-dama-scenone. There were 18 types of aroma compontents which mainly impacted the quality of aroma, such as megastigmatrienone, isovaleric acid, solanone, total neutral, neophytadiene, geranylacetone, farnesylacetone, 3-methylpentanoic acid, tetramethylpyrazine, β-damascenone, pyridine. The performance of the model was improved greatly by using the extracted features, which were built between aroma components and aroma quality. Compared with piecewise linear regression model, the RBF neural network model has better accuracy in prediction.