The rapid determination of total plant alkaloids (TPA) content in tobacco products by means of near infrared (NIR) spectroscopy is becoming conventional detection method in tobacco industry. An established NIR model of TPA is anticipated to be shared by other devices as long as possible not needing to update the model. Present study developed a three-step wavelength selection method SISCW (selecting important and stable characteristic wavelengths)to build a robust NIR calibration model of TPA to improve its transferability and to prolong its use period at many devices. For this, 292 flue-cured tobacco leaf samples from more than 10 regions of China, which were harvested in 2011-2013 and tested on the primary NIR device, were used to build the model. 77 samples harvested from 2011 to 2013 were applied to verify the transferability of the model on six secondary NIR devices. 180 samples harvested from 2014 to 2020 that were divided into 7 groups according to their growing years, were used to examine the long-term application capacity of the model on the seven NIR devices. The SISCW method integrates an image processing method of scale invariant feature transformation (SIFT) with analyzing standard deviation of the sample spectra (SDSS) and water absorption coefficients to select important and stable characteristic wavelengths (SISCW). The wavelengths selected by the SISCW were recorded as Uisc. The results indicated that the partial least square (PLS) calibration model of TPA based on Uisc (SISCW-PLS) performed well on both primary and the six secondary devices when it was employed to predict TPA of the 77 samples of 2011-2013. The model has run 7 years on the primary and 3 of the secondary devices from 2014 to 2020. This emphasizes the SISCW method has great potential for improving transferability and service-life of TPA calibration model.

为考察不同类型烟叶挥发性成分的差异,采用两步顶空进样-GC-MS法分析烤烟、雪茄烟、白肋烟、香料烟和晒黄烟烟叶的挥发性成分.两步顶空进样是将静态顶空进样器与程序升温进样口耦合,采用大体积的顶空进样环提高灵敏度,同时采用程序升温进样口以避免因进样体积较大引起的色谱峰展宽.结果表明:①采用新方法共从5种类型烟叶中检出121种挥发性成分;②采用主成分分析和系统聚类分析验证了挥发性成分组成与烟叶类型的关系;③晒黄烟与香料烟的挥发性成分组成最为相似,雪茄烟挥发性成分组成明显区别于其他类型烟叶;④雪茄烟挥发性成分组成与产地有关.该分析方法简便、灵敏、可靠,可用于烟叶成分研究及品质评价.

该文使用基于光谱图像特征抽提的尺度不变特征变换(SIFT)的多步波长筛选方法建立了烟叶总还原糖(TRS)的近红外光谱(NIRS)稳健模型,实现了其在多台仪器的直接共享和长期应用.首先采用SIFT方法根据代表性主机样品光谱挑选特征光谱点集合Uc,然后从Uc中剔除样本光谱标准方差(SDSS)过低的点,挑选重要特征光谱点集合Uic,此两步波长筛选法简称为SIFT-SDSS.随后进一步从Uic中挑选对水分不敏感(Mois-ture-unsensitive,MUS)的波长点,得到重要且稳定的光谱点集合Uisc,此3步波长筛选法简称为SIFT-SDSS-MUS.从2011～2013年采集的292个主机烟叶样品中按TRS浓度区间选择80%样品作为建模集,建立不同波长集合下烟叶TRS的偏最小二乘回归(PLSR)校正模型.结果表明,基于SIFT-SDSS两步波长筛选的光谱点建立的TRS模型传递到6台从机预测另外77个2011～2013年样品的TRS时,所有从机样品的平均相对误差绝对值(MARE)均小于6%,满足企业内控要求.该模型对5台近红外仪上2014～2020年各年度样品、1台近红外仪上2014～2019年各年度样品的MARE均小于6%.而全波长模型及SIFT波长筛选方法所建模型在7台仪器上对多个不同年份下样品中TRS的MARE大于6%,难以实现长期应用.SIFT-SDSS-MUS方法所建TRS模型变量最少,但其模型传递能力和长期应用能力略逊于SIFT-SDSS模型.SIFT-SDSS所建TRS模型稳健性好、可解释性强、运算速度快,可在7台同型号近红外仪上直接共享、在6台仪器上连续应用至少6年,大大减少了烟叶TRS近红外光谱模型维护及传递的工作量.

An unprecedented and efficient synthesis of megastigmatrien-3-one from readily available starting materials is reported.The transformation includes carbonyl group protection, reduction, oxidation, addition, elimination, and isomerization processes, which allows for the formation of resultant megastigmatrien-3-one with good purity and high stereoselectivity.This method features mild conditions and operational simplicity.

Heavy metals are nondegradable in the natural environment and harmful to the ecological system and human beings, causing an increased environmental pollution problem. It is required to remove heavy metals from wastewater urgently. Up until now, various methods have been involved in the heavy metal removals, such as chemical precipitation, chemical reduction, electrochemical, membrane separation, ion exchange, biological, and adsorption methods. Among them, adsorption by graphene–based materials has attracted much more attentions for the removal of heavy metals from wastewater systems in recent years, arising due to their large specific surface area, high adsorption capacity, high removal efficiency, and good recyclability. Therefore, it is quite important to review the heavy metal removal with the graphene–based material. In this review, we have summarized the physicochemical property and preparation methods of graphene and their adsorption property to heavy metals. The influencing parameters for the removal of heavy metals by graphene–based materials have been discussed. In addition, the modification of graphene–based materials to enhance their adsorption capability for heavy metal removal is also reviewed. The heavy metal removal by modified graphene–based materials in the tobacco industry has been especially described in detail. Finally, the future trend for graphene–based materials in the field of heavy metal wastewater treatment is proposed. This knowledge will have great impacts on the field and facilitate the researchers to seek the new functionalization method for graphene–based materials with high adsorption capacity to heavy metals in the tobacco industry in the future.

为了研究卷烟烟气中香味成分逐口释放规律,采用烟气香味成分GC-MS/MS测定方法,分析了烟气中醇类、醛类、酮类、酯类等不同种类香味成分在4种加香方式(烟丝加香、丝束加香、香线加香和爆珠加香)下主流烟气中的逐口释放规律.结果表明:①丝束加香和香线加香之间相似度最高,烟丝加香与其他3种加香方式相似度最低.②香味成分加香部位、结合方式和沸点是影响逐口释放的主要因素.③烟丝加香受蒸馏段高温影响,不同沸点香味成分逐口释放有较大差异,不同沸点的同类香味成分逐口释放占比也明显不同.④爆珠加香受爆珠香精溶剂和滤嘴温度影响,香味成分逐口释放最为稳定,沸点对同类香味成分的释放量无明显影响.⑤丝束加香和香线加香方式下香味成分逐口释放随口数增加而升高,沸点对同类香味成分的释放量无明显影响.

A new strategy based on sampling error profile analysis (SEPA) combined with least absolute shrinkage and selection operator (SEPA-LASSO) was proposed. LASSO has been proven to be effective for multivariate calibration with automatic variable selection for high-dimensional data. However, in the previous research, the critical process of multivariate calibration by LASSO was an optimization of 1-norm turning parameter for a fixed sample set without considering the behaviors of variable selection by different subsets of samples. In the present work, Monte Carlo Sampling (MCS), the core of SEPA framework, is used to investigate various sub-models. Least angle regression (LAR) is used to solve LASSO, and various LAR iteration including certain number of variables could be obtained instead of choosing the numerical values of 1-norm turning parameters. SEPA-LASSO algorithm consists of plenty of loops. Under the SEPA framework and LAR algorithm, a number of LASSO sub-models with the same dimensions are built by MCS in each loop, the vote rule is used to determine the importance of variables and select them to build variable subsets. After running the loops, several subsets of variables are obtained and their error profile is used to choose the optimal subset of variables. The performance of SEPA-LASSO was evaluated by three near-infrared (NIR) datasets. The results show that the model built by SEPA-LASSO has excellent predictability and interpretability, compared with some commonly used multivariate calibration methods, such as principal component regression (PCR) and partial least squares (PLS), as well as some wavelength selection methods including LASSO, moving window partial least squares regression (MWPLSR), Monte Carlo uninformative variable elimination (MC-UVE), ordered homogeneity pursuit lasso (OHPL) and stability competitive adaptive reweighted sampling (SCARS).

With the Naive Bayesian Classifier,a pattern recognition model of the producing areas of flue-cured tobacco was built.The model features were the contents of chemical components,including total sugar,reducing sugar,total nitrogen,nicotine,total chlorine and total potassium.The accuracy of the training set,LOOCV and the test set were 91.24％,89.05％and 88.24％,while the results of the SVM and the KNN could not get the same accuracy level as the NBC.The Naive Bayesian Classifier could be applied to pattern recognition of flue-cured tobacco samples of different origins.