基于热裂解-气相色谱-质谱法和随机森林的加热卷烟烟叶原料适用性评估
Applicability Evaluation of Heat-Not-Burn Tobacco Raw Materials Based on Thermal Pyrolysis-Gas Chromatography-Mass Spectrometry and Random Forests
摘 要
基于热裂解-气相色谱-质谱法(Py-GC-MS)和随机森林(RF),从化学成分角度分析加热卷烟烟叶原料适用性。称取过筛后的样品粉末0.90mg于样品杯中,采用Py-GC-MS对28种不同类型的加热卷烟进行检测,用MZmine软件对Py-GC-MS数据进行处理,获得含有峰强度信息的特征峰表。分别以样品的特征峰表和感官评价得分作为自变量和因变量,采用RF回归算法建立加热卷烟烟叶原料适用性模型。结果显示:RF模型在训练集上的决定系数为0.93,均方根误差为0.85,在测试集上的决定系数为0.92,均方根误差为0.96;根据NIST 2017库的定性结果,共筛选出20个特征重要性评分较高的化学成分。
热裂解-气相色谱-质谱法
随机森林
化学成分
heat-not-burn tobacco
thermal pyrolysis-gas chromatography-mass spectrometry
random forests
chemical composition
Abstract
Based on thermal pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) and random forests (RF), the applicability of heat-not-burn tobacco raw materials was analyzed from the perspective of chemical composition. The sieved sample powder (0.90 mg) was placed into the sample cup, Py-GC-MS was used to detect 28 different types of heat-not-burn tobaccos, and MZmine software was used to process the Py-GC-MS data to obtain the characteristic peak table containing intensity information. The characteristic peak table and the sensory evaluation scores of the samples were used as independent variables and dependent variables, respectively, and RF regression algorithm was used to establish the applicability model of heat-not-burn tobacco raw materials. It was shown that the coefficient of determination of RF model on the training set was 0.93, and the root mean square error was 0.85. The coefficient of determination on the testing set was 0.92, and the root mean square error was 0.96. According to the qualitative results of NIST 2017 database, 20 chemical compositions with higher feature importance scores were screened out.
中图分类号 O657.63 DOI 10.11973/lhjy-hx202301003
所属栏目 试验与研究
基金项目 中国烟草总公司云南省公司新型烟草制品烟叶原料保障重大专项“加热卷烟烟叶原料调制技术及质量评价研究与应用”(20205300002410040)
收稿日期 2022/5/9
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备注陈颐,博士,主要从事烟草调制技术研究
引用该论文: CHEN Yi,FAN Yingjie,WANG Xu,YANG Jing,ZHAO Wentao,ZHANG Zhimin. Applicability Evaluation of Heat-Not-Burn Tobacco Raw Materials Based on Thermal Pyrolysis-Gas Chromatography-Mass Spectrometry and Random Forests[J]. Physical Testing and Chemical Analysis part B:Chemical Analysis, 2023, 59(1): 21～28
陈颐,范迎杰,汪旭,杨菁,赵文涛,张志敏. 基于热裂解-气相色谱-质谱法和随机森林的加热卷烟烟叶原料适用性评估[J]. 理化检验－化学分册, 2023, 59(1): 21～28
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