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基于热重分析的烟草常规化学成分定量分析

Quantitative Analysis of Routine Chemical Constituents of Tobacco Based on Thermogravimetric Analysis.

作者信息

Peng Yuhan, Bi Yiming, Dai Lu, Li Haifeng, Cao Depo, Qi Qijie, Liao Fu, Zhang Ke, Shen Yudong, Du Fangqi, Wang Hui

机构信息

Technology Center, China Tobacco Zhejiang Industrial Co., Ltd., Hangzhou 310012, China.

Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China.

出版信息

ACS Omega. 2022 Jul 21;7(30):26407-26415. doi: 10.1021/acsomega.2c02243. eCollection 2022 Aug 2.

Abstract

As the most basic indexes to evaluate the quality of tobacco, the contents of routine chemical constituents in tobacco are mainly detected by continuous-flow analysis at present. However, this method suffers from complex operation, time consumption, and environmental pollution. Thus, it is necessary to establish a rapid accurate detection method. Herein, different from the ongoing research studies that mainly chose near-infrared spectroscopy as the information source for quantitative analysis of chemical components in tobacco, we proposed for the first time to use the thermogravimetric (TG) curve to characterize the chemical composition of tobacco. The quantitative analysis models of six routine chemical constituents in tobacco, including total sugar, reducing sugar, total nitrogen, total alkaloids, chlorine, and potassium, were established by the combination of TG curve and partial least squares algorithm. The accuracy of the model was confirmed by the value of root mean square error for prediction. The models can be used for the rapid accurate analysis of compound contents. Moreover, we performed an in-depth analysis of the chemical mechanism revealed by the result of the quantitative model, namely, the regression coefficient, which reflected the correlation degree between the six chemicals and different stages of the tobacco thermal decomposition process.

摘要

作为评价烟草品质的最基本指标,目前烟草中常规化学成分的含量主要通过连续流动分析法进行检测。然而,该方法存在操作复杂、耗时以及环境污染等问题。因此,有必要建立一种快速准确的检测方法。在此,不同于目前主要选择近红外光谱作为烟草化学成分定量分析信息源的研究,我们首次提出使用热重(TG)曲线来表征烟草的化学成分。通过TG曲线与偏最小二乘算法相结合,建立了烟草中总糖、还原糖、总氮、总生物碱、氯和钾六种常规化学成分的定量分析模型。通过预测的均方根误差值证实了模型的准确性。这些模型可用于化合物含量的快速准确分析。此外,我们对定量模型结果所揭示的化学机理进行了深入分析,即回归系数,其反映了这六种化学成分与烟草热分解过程不同阶段之间的相关程度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e94/9352168/27e31ca1d76d/ao2c02243_0002.jpg

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