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为实现企业库存烟叶原料烟气焦油、烟碱及CO释放量的快速检测，通过采集889种片烟的近红外光谱(NIR)，结合Kennard-Stone(K-S)算法筛选出150种代表性片烟进行建模。采用偏最小二乘法(PLS)作为建模方法，运用多元散射校正(MSC)、Savitzky-Golay(SG)平滑及一阶导数进行光谱预处理，选择特征谱段和最佳主成分数，建立了基于烟叶原料NIR的烟气焦油、烟碱及CO释放量预测模型。结果表明：(1)在显著性水平0.05时，预测结果与标准方法检测结果不存在显著性差异。(2)焦油、烟碱及CO最佳PLS预测模型的决定系数(R2)均大于0.80。(3)焦油、烟碱和CO校正均方根误差(RMSEC)分别为1.13、0.13和0.97，预测均方根误差(RMSEP)与其比值分别为1.03、1.00和0.95。(4)叶组叠加实验结果显示，各原料预测结果按照配方比例线性加和结果与叶组实测结果相当，相对偏差<10%。建立的焦油、烟碱及CO释放量的近红外预测模型准确可靠，可用于烟叶原料烟气常规化学成分释放量的快速定量预测。
Abstract：For rapid determination of tar,nicotine and CO in mainstream smoke released from tobacco leaves stored in warehouses of different tobacco enterprises,near-infrared(NIR)spectra of 889 tobacco strip samples were collected and the Kennard-Stone(K-S)algorithm was used to select 150 representative tobacco strip samples for Partial Least Squares(PLS) regression modeling. The spectra were pre-processed using Multivariate Scatter Correction(MSC),Savitzky-Golay(SG) smoothing and first order derivatives. By selecting the characteristic spectral segments and the optimal number of principal components,the prediction models for the releases of tar,nicotine and CO in cigarette smoke based on the NIR spectroscopy of tobacco leaves were established. The results showed that:1) There were no significant differences between the results of the prediction models and the standard methods at 0.05 level of significance. 2)All the coefficients of determination(R2)of the best PLS prediction models for tar,nicotine and CO releases were higher than 0.80. 3)The root mean square errors of calibration(RMSEC)for tar,nicotine and CO were 1.13,0.13 and 0.97,respectively. The ratios of the root mean square error of prediction(RMSEP)to the RMSEC for tar,nicotine and CO were 1.03,1.00 and 0.95,respectively. 4)The results of the leaf blending overlay experiment showed that the predicted results of each tobacco raw material linearly added according to the formula ratios,which closely matched the actual determined result of the leaf blending with a relative deviation of less than 10%. The established near-infrared prediction models for the releases of tar,nicotine and CO were therefore considered to be accurate,reliable and suitable for rapid quantitative prediction of the releases of routine cigarette smoke components from tobacco leaves.
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基本信息:
DOI：10.16135/j.issn1002-0861.2024.0881
中图分类号:O657.33;TS411
引用信息:
[1]王志才,张建栋,杨松等.近红外光谱法预测烟叶原料烟气常规化学成分释放量[J].烟草科技,2025,58(04):1-10.DOI:10.16135/j.issn1002-0861.2024.0881.
基金信息:
甘肃省重点研发计划-工业类项目“基于近红外光谱的卷烟产品数字化设计与维护技术研究”（22YF7GA052）; 中国烟草实业发展中心科技项目“‘兰州’品牌叶组配方模块化替代技术研究”（ZYSYQ-2024-08）; 甘肃烟草工业有限责任公司科技项目“‘兰州’品牌卷烟烟气常规成分释放量近红外预测技术研究”（KJXM-2022-02）