通风对细支烟主流烟气常规成分及7种有害成分释放量的影响

为考察通风对细支烟主流烟气常规成分及7种有害成分释放量的影响,卷制具有不同滤嘴通风率和卷烟纸透气度的细支烟,对主流烟气常规成分和7种有害成分释放量进行分析。结果表明:①总通风率、滤嘴通风率、纸通风率与细支烟烟气常规成分、7种有害成分释放量及卷烟危害性评价指数(H)负相关;②总通风率、滤嘴通风率、纸通风率增加1%时,CO、HCN和巴豆醛释放量的降低率较高,纸通风对细支烟总粒相物、焦油、烟碱、CO、水分、HCN、NNK、苯酚和巴豆醛的释放量及H值的影响大于滤嘴通风;③滤嘴通风和纸通风对细支烟焦油、烟碱、CO、HCN、氨和苯酚的释放量及H值的影响小于常规卷烟。通过优化设计细支烟通风参数,可降低焦油和7种有害成分的释放量。     

滤嘴参数对细支烟主流烟气中代表性香味成分释放量的影响

  目的  研究滤嘴参数（滤棒压降、滤嘴通风率）对细支烟香味成分释放的影响。  方法  按单因素设计制备60个卷烟样品,采用CH2Cl2浸提、GC/MS分析测定滤片捕集的代表性香味成分。在此基础上,建立并筛选了滤嘴参数对香味成分释放量的预测模型。  结果  ① 细支烟主流烟气中代表性碱性、中性、酸性和香味成分总释放量与滤棒压降和滤嘴通风率均呈显著负相关;②滤棒压降对细支烟主流烟气香味成分释放量的影响高于常规烟,而滤嘴通风则相反。③外部验证结果显示,4个预测模型的平均预测偏差在4.23%~5.95%之间,所建立的模型预测精度良好。      

通风分配对主流烟气代表性酚类香味成分释放量的影响

卷制具有不同滤嘴通风率和卷烟纸透气度的常规卷烟和细支卷烟,考查滤嘴通风率、纸通风率及通风分配对主流烟气中9种酚类香味成分释放量的影响。结果表明：1)滤嘴通风率、纸通风率与烟气中9种酚类香味成分释放量均呈显著负相关,随着通风率的增加,分子量较小的麦芽酚等的释放量降低率较高,而分子量较大的丁香酚的释放量降低率较小;2)对于常规卷烟和细支卷烟,纸通风率对烟气中9种酚类香味成分释放量的影响大于滤嘴通风率;3)滤嘴通风率、纸通风率及总通风率对常规卷烟烟气中9种酚类香味成分释放量的影响大于细支卷烟;4)在总通风率一致的情况下,高滤嘴通风率搭配低纸通风率比低滤嘴通风率搭配高纸通风率的卷烟中9种酚类香味成分释放量要高。     

基于卷烟纸特性参数的中支烟主流烟气成分释放量预测

为准确预测中支卷烟主流烟气中焦油、烟碱和CO释放量,使用随机森林算法构建了基于卷烟纸特性参数的焦油、烟碱和CO释放量预测数学模型,并进行了焦油、烟碱和CO释放量影响因素重要性排序和相关性分析。采用构建的随机森林算法模型对5个中支卷烟样品进行了焦油、烟碱和CO释放量的预测并与实测值进行了对比,验证模型的预测能力,同时对焦油、烟碱和CO释放量检测过程中可能产生的相对扩展不确定度进行测定。结果表明：基于卷烟纸特性参数构建的随机森林算法模型对于中支卷烟样品的焦油、烟碱和CO释放量的预测,具有较好的准确性、精确度和稳定性,且模型的焦油、烟碱和CO释放量预测残差均小于检测的扩展不确定度。     

辅助材料参数对卷烟主流烟气中酸性香味成分释放量的影响

为研究辅助材料参数(卷烟纸定量、透气度,成型纸透气度,接装纸透气度和滤棒吸阻)对卷烟主流烟气中酸性香味成分释放量的影响,采用GC/MS法对不同辅材设计参数试验卷烟主流烟气中21种酸性香味成分进行了测定,并分别从单因素、多因素两方面考察了上述卷烟辅材设计参数对主流烟气酸性香味成分释放量的影响,建立了4种卷烟辅材设计参数(卷烟纸定量、成型纸透气度、接装纸透气度和滤棒吸阻)对主流烟气中十四酸、十六酸、亚油酸、亚麻酸、油酸和十八酸6种酸性香味成分及酸性香味成分总释放量的多因素预测模型。结果表明:①卷烟纸定量、成型纸透气度、接装纸透气度和滤棒吸阻4种辅材设计参数对卷烟主流烟气中酸性香味成分释放量有影响,卷烟纸透气度无影响。②随着卷烟纸定量、成型纸透气度、接装纸透气度和滤棒吸阻的增加,十四酸、十六酸、亚油酸、亚麻酸、油酸和十八酸6种酸性香味成分的释放量逐渐降低,丙酸、丁酸、2-甲基丁酸、3-甲基丁酸、戊酸、3-甲基戊酸、4-甲基戊酸、乳酸、己酸、2-呋喃甲酸、庚酸、苯甲酸、辛酸、壬酸和癸酸15种成分的释放量基本无变化。③所建立的模型具有较好的预测能力。     

卷烟纸参数对短支卷烟主流烟气常规成分释放量及感官质量的影响

为考察卷烟纸参数对短支卷烟主流烟气常规指标及感官质量的影响,制备了不同卷烟纸透气度、定量、助燃剂用量、助燃剂钾/钠比及碳酸钙含量的短支卷烟,并进行了主流烟气常规成分释放量测定及感官质量评价.结果表明:①随着卷烟纸透气度的增加,常规成分释放量均呈降低趋势,感官质量呈下降趋势.②随着卷烟纸定量的增加,焦油和烟碱释放量降低,...     

基于多因素辅材参数的短支卷烟烟气有害成分预测模型

为考察卷烟辅助材料参数对短支卷烟7种有害成分（CO、HCN、NNK、氨、B[a]P、苯酚和巴豆醛）释放量及H值（危害性指数）的影响,选取卷烟纸定量、卷烟纸透气度、卷烟纸助燃剂质量分数、卷烟纸助燃剂钾钠比、滤棒压降和接装纸透气度共6个因素,采用中心组合结合正交设计制备50个短支卷烟样品,使用样品的检测结果建立多元线性模型、二次多项式模型、多因子及互作项模型,并筛选出各指标的最优模型。通过调整卷烟辅材的参数和烟丝配方对预测模型进行了验证。结果表明：短支卷烟的7种有害成分及H值预测模型预测精度良好,8个模型的验证数据中97.5%的相对误差在±15%以内,平均绝对误差在3.4%～6.5%之间。该预测模型对短支卷烟具有良好的适用性。     

基于卷烟辅助材料参数的卷烟烟气有害成分预测模型

为考察卷烟辅助材料参数(卷烟纸克重、卷烟纸透气度、成型纸透气度、接装纸透气度和滤棒吸阻)对卷烟主流烟气有害成分释放量的影响,采用中心组合结合正交设计制备试验卷烟,利用线性回归法和逐步回归法建立卷烟辅助材料参数对主流烟气7种有害成分(CO,NNK,NH3,HCN,BaP,苯酚、巴豆醛)及2种常规化学成分(焦油、烟碱)释放量的多因素预测模型,根据统计学原理中交叉验证标准差(RMSECV)最小的原则筛选出最优预测模型。在不同卷烟配方条件下,外部验证结果表明:CO,NNK,NH3,HCN,BaP,巴豆醛、苯酚、烟碱和焦油等9种有害成分平均预测相对偏差分别为6.9%,8.7%,6.6%,11.8%,4.1%,8.2%,5.3%,4.0%和4.8%。9种有害成分预测模型预测精度良好,对于不同的配方和辅助材料具有广泛的适用性。     

细支和常规卷烟主流烟气常规成分和5种关键烤甜香味成分逐口释放量的差异分析

建立了测定5种烤甜香味成分逐口释放量的分析方法,采用相同烟丝配方卷制细支和常规卷烟,考察细支和常规卷烟主流烟气常规与5种关键烤甜香味成分逐口释放量差异。结果表明:①分析方法重复性、灵敏度和准确度较好,适合于卷烟逐口烟气中5种烤甜香味成分的定量分析;②细支卷烟常规成分逐口释放量均低于常规卷烟,总粒相物（TPM）、焦油、水分的逐口释放变化幅度大于常规卷烟,烟碱变化幅度小于常规卷烟;③细支卷烟糠醇、5-甲基糠醛和3-甲基-2-环戊烯-1-酮的逐口释放量高于常规卷烟（除第1口外）,4-羟基-2,5-二甲基-3（2H）呋喃酮（呋喃酮）和麦芽酚的逐口释放量略低于常规卷烟;抽吸中段细支卷烟糠醇和5-甲基糠醛的逐口释放稳定性优于常规卷烟,3-甲基-2-环戊烯-1-酮劣于常规卷烟,呋喃酮和麦芽酚差异不大;细支卷烟5种香味成分总量中糠醇和5-甲基糠醛所占比例高于常规卷烟,3-甲基-2-环戊烯-1-酮、呋喃酮和麦芽酚低于常规卷烟。      

烤烟型细支与常规卷烟烟气中9种主要成分的逐口释放量比较

为研究不同圆周卷烟主流烟气主要成分逐口释放的差异,对同一烤烟型配方烟丝的细支卷烟和常规卷烟主流烟气中焦油、烟碱、CO、NNK、B[a]P、苯酚、巴豆醛、HCN和NH₃9种主要成分逐口释放量进行量化分析。结果显示:①细支卷烟和常规卷烟的平均抽吸口数存在极显著差异,分别为5.49,6.72口;各成分在细支卷烟中的逐口释放量及总量均低于常规卷烟,其中焦油、烟碱、NNK、B[a]P、苯酚、HCN和NH₃成分在细支卷烟中的波动均小于常规卷烟,而CO和巴豆醛则相反;②随着抽吸口数的增大,细支卷烟和常规卷烟中焦油、烟碱、CO、NNK、B[a]P、苯酚、HCN和NH₃成分均逐步增加,仅巴豆醛第一口释放量大于第二口;③常规卷烟烟气中9种主要成分的单口烟气释放量随抽吸口数递增而增加的趋势整体上更显著于细支卷烟,而在控制焦油因素情况下,细支卷烟各成分逐口释放量与抽吸口数相关性发生变化的程度更甚于常规卷烟。     

不同焦油释放量卷烟烟气的细胞毒性和基因毒性研究

采用体外细胞微核试验方法测定对照卷烟和2种不同焦油释放量卷烟的基因毒性.将3T3细胞暴露于ISO标准抽吸条件下产生的卷烟烟气环境中,首先测试烟气的细胞毒性来决定微核试验的烟气暴露浓度,再通过测定暴露于烟气的细胞微核的增加量来测试卷烟烟气的基因毒性.结果显示随着烟气暴露浓度的增大,3种卷烟烟气引起细胞微核的机率显著增大;加入S 9匀浆液不会增加细胞微核的产生机率,相反,+S 9组的3种卷烟烟气引起的细胞微核的增加比例较-S 9组显著降低.此外3种卷烟相比,1号测试卷烟烟气的基因毒性明显高于2号测试卷烟和K2R4F对照卷烟,2号卷烟烟气的基因毒性最低.     

不同焦油释放量卷烟烟气的细胞毒性和基因毒性研究

采用体外细胞微核试验方法测定对照卷烟和2种不同焦油释放量卷烟的基因毒性。将3T3细胞暴露于ISO标准抽吸条件下产生的卷烟烟气环境中,首先测试烟气的细胞毒性来决定微核试验的烟气暴露浓度,再通过测定暴露于烟气的细胞微核的增加量来测试卷烟烟气的基因毒性。结果显示随着烟气暴露浓度的增大,3种卷烟烟气引起细胞微核的机率显著增大;加入S 9匀浆液不会增加细胞微核的产生机率,相反,+S 9组的3种卷烟烟气引起的细胞微核的增加比例较-S 9组显著降低。此外3种卷烟相比,1号测试卷烟烟气的基因毒性明显高于2号测试卷烟和K2R4F对照卷烟,2号卷烟烟气的基因毒性最低。      

综合方法对烤烟化学成分和烟气组分的相关分析

针对烟草化学成分与烟气组分间存在较复杂的非线性关系,采用主成分分析法进行相关性分析.以5个产区烤烟为样本,利用投影、主成分提取来分析影响烟气的主要化学成分.采用M5'模型树法建立烟气成分的分段线性模型.根据2种方法的结论,更有效判断烤烟化学成分对烟气成分的影响,提高对烤烟烟气成分的预测精度.     

Prediction of fabrics mechanical behaviour in uni-axial tension starting from their technical parameters

On the basis of a phenomenological study, which consists in determining the response of a fabric sample to the traction and the relaxation, we proposed a rheological model with 15 coefficients. This model of mechanical behaviour in uni-axial tension takes into account the elastic and the viscous components. To characterize the coefficients of this model, simple tension and relaxation tests were performed for 29 fabrics in the warp and the weft directions. An artificial neuronal network trained with a retro-propagation algorithm performs functional mapping between these mechanical coefficients and the technical parameters of fabric, which allowed us to predict the mechanical behaviour in tension and relaxation of fabrics starting only from their technical parameters.     

基于主成分回归的曲靖C3F等级烤烟评吸质量估算模型

为探明曲靖烟区烤烟评吸质量与主要化学成分的关系及建立烤烟评吸质量的估算方法,对150个烤烟样品的13项化学指标与评吸质量进行了相关分析和主成分回归分析.结果表明,入选的13项化学指标与烤烟评吸质量均有一定的相关性,其中还原糖、总氮、烟碱、钾含量、氯含量、挥发酸和挥发碱含量是烤烟评吸质量的主要决定因素;基于主成分回归方法建立了曲靖烟区烤烟评吸质量的估算模型,决定系数(R2)为0.723,回归方程通过显著性检验(P=0.000);曲靖烟区烤烟评吸质量可以通过烤烟化学成分含量进行估算,基于烤烟化学指标数据运用主成分回归的方法在既定区域内估算烤烟样品的评吸质量是可行的.     

基于机器学习的整体穿刺加压参数预测方法

针对整体穿刺加压密实过程中碳布回弹导致平均层高波动范围较大,影响立体织物性能的问题,提出一种基于机器学习理论的加压参数预测方法,将平均层高与加压参数之间复杂建模转换为多元回归问题,使用适合计算机运算的无约束优化迭代方法求解。基于scikit-learn类库对特征变量进行特征选择,对比6种回归模型的预测性能得分后选择K近邻回归作为基学习器,使用集成算法提升模型的预测性能。预测模型部署到生产环境后的实验结果表明:使用机器学习预测后,加压参数对整体穿刺过程平均层高均值变化的响应速度得到提高,且均值变化幅度得到降低,实验样本平均层高波动范围均值从12.0%降低至6.8%,标准差从0.008 3降低至0.006 6。     

Prediction accuracies and genetic parameters for test-day traits from genomic and pedigree-based random regression models with or without heat stress interactions

The aim of this study was to compare genetic (co)variance components and prediction accuracies of breeding values from genomic random regression models (gRRM) and pedigree-based random regression models (pRRM), both defined with or without an additional environmental gradient. The used gradient was a temperature-humidity index (THI), considered in statistical models to investigate possible genotype by environment (G×E) interactions. Data included 106,505 test-day records for milk yield (MY) and 106,274 test-day records for somatic cell score (SCS) from 12,331 genotyped Holstein Friesian daughters of 522 genotyped sires. After single nucleotide polymorphism quality control, all genotyped animals had 40,468 single nucleotide polymorphism markers. Test-day traits from recording years 2010 to 2015 were merged with temperature and humidity data from the nearest weather station. In this regard, 58 large-scale farms from the German federal states of Berlin-Brandenburg and Mecklenburg-West Pomerania were allocated to 31 weather stations. For models with a THI gradient, additive genetic variances and heritabilities for MY showed larger fluctuations in dependency of DIM and THI than for SCS. For both traits, heritabilities were smaller from the gRRM compared with estimates from the pRRM. Milk yield showed considerably larger G×E interactions than SCS. In genomic models including a THI gradient, genetic correlations between different DIM × THI combinations ranged from 0.26 to 0.94 for MY. For SCS, the lowest genetic correlation was 0.78, estimated between SCS from the last DIM class and the highest THI class. In addition, for THI × THI combinations, genetic correlations were smaller for MY compared with SCS. A 5-fold cross-validation was used to assess prediction accuracies from 4 different models. The 4 different models were gRRM and pRRM, both modeled with or without G×E interactions. Prediction accuracy was the correlation between breeding values for the prediction data set (i.e., excluding the phenotypic records from this data set) with respective breeding values considering all phenotypic information. Prediction accuracies for sires and for their daughters were largest for the gRRM considering G×E interactions. Such modeling with 2 covariates, DIM and THI, also allowed accurate predictions of genetic values at specific DIM. In comparison with a pRRM, the effect of a gRRM with G×E interactions on gain in prediction accuracies was stronger for daughters than for sires. In conclusion, we found stronger effect of THI alterations on genetic parameter estimates for MY than for SCS. Hence, gRRM considering THI especially contributed to gain in prediction accuracies for MY.     

Prediction of traditionally utilised wheat dough technological quality parameters from Mixolab values: development and evaluation of regression models

The research was conducted with the aim to investigate the possibility of Farinograph, Extensograph and Amylograph values prediction with linear and/or multiple Mixolab regression models. In total, 163 flour samples were divided based on Machalanobis distances into three sets: prediction, validation and external sample set. Determination coefficients ranged from 0.145 to 0.640 for linear regression models and from 0.279 to 0.739 for multiple regression models. Internal and external validation of developed regression models was conducted. Testing of developed models resulted in a high share of samples for which predicted values were out of the ranges of method official reproducibility and a high share of samples for which predicted values were out of the quality level range obtained by analytical measurement. It was concluded that it is impossible to develop applicable regression models for prediction of Farinograph, Extensograph and Amylograph parameters on the basis of Mixolab values from standard protocol.     

利用主成分回归分析法建立微量成分与菜籽油烟点间的预测方程

随着食用植物油适度精炼理念的提出和被广泛认可,植物油国标中对酸值、色泽等指标开始放宽,而这种放宽会导致植物油中其他的非甘油三酯组分的含量,尤其是对烟点有影响的磷脂、溶剂、过氧化物、水分及挥发物等组分发生变化,进而影响植物油的烟点。尽管针对游离脂肪酸这一将放宽的质量指标,其与烟点间数学关系已有研究,但酸值的放宽引起的其他组分的变化对烟点的影响并未能综合考量。因此,本文通过实验室模拟的方法,通过采集不同游离脂肪酸、磷脂、过氧化物、溶剂残留、水分及挥发物的菜籽油,进行复配,获得组分不同的植物油样品,测定烟点及各指标含量后,利用主成分回归的方法得到各因素与烟点间的回归方程：Ln烟点= 5.006-0.029×Ln酸值-0.041×Ln过氧化值-0.003×Ln溶残-0.039×Ln含磷量-0.044×Ln水分及挥发物,R2=0.926,并发现各因素的影响程度大小由大到小分别为水分及挥发物、过氧化值、含磷量、酸值、溶残。