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【目的】监控加热卷烟雾化基材生产中的质量稳定性，建立快速、同时测定加热卷烟雾化基材中水分、丙二醇、甘油和烟碱含量的近红外分析方法。【方法】采用自举软收缩(Bootstrapping soft shrinkage approach, BOSS)变量选择方法筛选特征波长变量，结合偏最小二乘法(Partial least squares, PLS)分别建立片状、丝状加热卷烟雾化基材中4种化学成分近红外含量预测模型，并与其他变量选择方法进行对比。【结果】(1)基于BOSS算法优选波长变量所建立的模型预测精度最优，模型R2均大于0.95,RMSECV(Root mean square error of cross-validation，交叉验证均方根误差)均小于0.7%。(2)水分、丙二醇、甘油和烟碱的RMSEP(Root mean square error of cross-prediction，交叉预测均方根误差)均小于0.75%，且验证集RMSEP与建模集RMSECV值相近，各模型均具有较好的预测准确性。(3)各成分模型RSD(Relative standard deviation，相对标准偏差)均小于2%，模型重现性较好。
Abstract：This study aims to monitor the quality stability of heated cigarette aerosol material production and establish a rapid, simultaneous near-infrared(NIR) analysis method for determining the moisture, propylene glycol, glycerol, and nicotine content in the heated cigarette aerosol substrates. The bootstrapping soft shrinkage approach(BOSS) variable selection method was adopted to select characteristic wavelength variables. Combined with partial least squares method(PLS), prediction models for the NIR content of the four chemical components in flake and filament heated cigarette aerosol substrates were established and compared with other variable selection methods.The results showed that the prediction accuracy of the model established based on the optimized wavelength variables of the BOSS algorithm was the best, with model R2 greater than 0.95 for all models and RMSECV(Root mean square error of cross-validation) less than 0.7%. The RMSEP(Root Mean Square Error of Cross-Prediction) for moisture, propylene glycol, glycerol, and nicotine was all less than 0.75%, and the RMSEP values for the validation set were similar to those of the modeling set, indicating good predictive accuracy for all models. The RSD(Relative Standard Deviation) of each component model was less than 2%, showing good reproducibility of the models.
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基本信息:
DOI：10.16472/j.chinatobacco.2024.T0299
中图分类号:TS452
引用信息:
[1]祝浩,李蓓蓓,郭军伟等.加热卷烟雾化基材在线生产中水分、丙二醇、甘油、烟碱的快速测定[J].中国烟草学报,2025,31(03):23-30.DOI:10.16472/j.chinatobacco.2024.T0299.
基金信息: