不同传统香型及产地的上部复烤烟叶中多酚类成分含量比较分析

  目的  明确不同传统香型及不同产地上部复烤烟叶中多酚类成分含量差异。  方法  采用高效液相色谱(HPLCDAD)、离子肼多级质谱(Trap-MSn)和飞行时间质谱(TOF-MS)技术建立烟叶多酚类成分的定性、定量分析方法, 测定了133批不同传统香型、不同产地烟叶样本中新绿原酸、绿原酸、隐绿原酸、芦丁等4种成分的含量, 并比较它们在不同产地、不同传统香型烟叶中的分布规律。  结果  按照传统香型划分, 清香型烟叶中4种多酚类成分总含量(26.19±4.07 mg/g)>中间香型(25.43±3.09 mg/g)>浓香型(22.23±3.75 mg/g)。新绿原酸在清香型烟叶中含量(2.39±0.46 mg/g)>中间香型(2.25±0.40 mg/g)>浓香型(1.84±0.39 mg/g); 芦丁在清香型烟叶中含量(8.74±1.41 mg/g)>中间香型(8.25±1.52 mg/g)>浓香型(5.99±1.40 mg/g)。新绿原酸和芦丁的含量与传统香型关系更为密切。  结论  烟叶种植地的生态环境(产地)对烟叶中多酚类成分含量影响较大, 而多酚类成分在烟叶香型的判别中并非决定要素。      

施钾量对不同生态区烤烟生长和钾肥利用率的影响

为研究不同生态区施钾量与烤烟生长和钾肥利用率的关系,以不施钾（CK）为对照,分别在气候和土壤差异明显的山东即墨和云南罗平开展了3个钾水平（即墨,T1 123.75 kg/hm²、T2 247.50 kg/hm²、T3 371.25 kg/hm²;罗平,T1 67.50 kg/hm²、T2 135.00 kg/hm²、T3 202.50 kg/hm²）的田间试验。结果表明：（1）施钾肥显著提高即墨烤烟茎、叶的生物量和总生物量,仅显著提高罗平烤烟烟叶生物量;T1、T2和T3显著提高烟叶生物量,即墨分别为7.62%、29.4%和31.5%,罗平分别为12.4%、19.5%和13.1%。（2）施钾肥显著提高即墨烟叶钾积累量和钾总积累量,T3效果最显著;显著提高罗平烤烟钾总积累量,T2效果最显著。（3）即墨施钾量与烤烟烟叶产量、烟叶钾含量和钾总积累量之间呈极显著正相关,而罗平则符合一元二次方程曲线;罗平T2对应施钾量下,烟叶产量、烟叶钾含量和钾总积累量与最大值（6579 kg/hm²、2.19%、230 kg/hm²）接近。（4）T2处理,即墨烟叶含钾量>3%,钾肥表观利用率（RE）和钾肥农学效率（AE）最高,分别为27.8%和2.50 kg/kg,钾肥偏生产力（PEP）降低,土壤钾素盈余显著;罗平RE高达45.3%,AE和PEP较高,分别为8.05 kg/kg和49.3 kg/kg,土壤钾素亏缺显著。综上,T2处理在提高即墨和罗平烟叶产质量和钾肥利用率以及平衡土壤钾素方面效果显著,可推荐为两地烤烟生产的适宜施钾量。     

亚慢性烟雾暴露对大鼠肺脏蛋白组学的影响

  目的  应用蛋白组学技术筛选卷烟烟气暴露诱导的大鼠肺脏差异表达蛋白,探讨长期吸烟对大鼠肺脏损伤的分子作用机制。  方法  雄性Sprague Dawley（SD）大鼠经烟雾暴露染毒90天,采用串联质谱标签（tandem mass tag,TMT）标记蛋白组学技术筛选大鼠肺脏差异表达蛋白,应用生物信息学软件对差异表达蛋白进行分析。  结果  与对照组比较,染毒组大鼠肺脏差异表达1.2倍以上的蛋白有133个,其中99个表达上调,34个表达下调。差异表达的蛋白主要参与602种生物学过程,131种细胞成分,158类分子功能,以及21条信号转导通路。  结论  雄性大鼠经烟雾暴露90天对肺脏组织的氧化还原过程和肌肉组织有影响,调控多种疾病的信号通路。      

Sherry wine vinegars: phenolic composition changes during aging

Production of Sherry wine vinegar encompasses a variable period of aging in wood. Changes in phenolic compounds during Sherry vinegar aging were studied. Samples were divided according to their aging period in wood (less than 2 years, more than 2 years) and phenolic compounds determined in a total of 38 samples by means of HPLC and Photodiode Array Detection. Results of Multiple Analysis of Variance (MANOVA) confirms that there were significant differences among both groups. Good recalling rates were achieved in both Linear Discriminant Analysis LDA (mean=92.9%) and Artificial Neural Networks trained by Back Propagation (BPANN) (mean=90.4%) whilst prediction abilities are more discrete. Applying the forward selection of variables, six variables were selected according to the Wilks' lambda criterion: gallic acid, 5-hydroxymethylfuraldehyde, coumaroyltartaric acid glycoside, p-hydroxybenzoic acid, caffeic acid and isoquercetrin. Three of these compounds (gallic acid, hydroxymethylfuraldehyde and coumaroyltartaric acid glycoside) were shown to have an increasing trend through most aging systems.