摘要
通过初烤烟叶外观质量量化评分建立与主要化学成分的关系。结果表明,成熟度与两糖比和糖碱比分别呈极显著(P<0.01)和显著(P<0.05)正相关。油分与氮碱比呈极显著负相关(P<0.01),与还原糖和烟碱的含量呈极显著正相关(P<0.01)。身份与氮碱比和两糖比分别呈极显著(P<0.01)和显著(P<0.05)负相关,与还原糖和总氮呈极显著(P<0.01)正相关。叶片结构与烟碱、淀粉、蛋白质呈极显著(P<0.01)负相关,和糖碱比呈显著(P<0.05)负相关,与还原糖和两糖比分别呈极显著(P<0.01)和显著(P<0.05)正相关。色度与氮碱比呈极显著(P<0.01)负相关,与总糖呈极显著(P<0.01)正相关。初步建立了初烤烟叶外观质量与主要化学成分回归方程。
The correlation between appearance quality and main chemical components of flue-cured tobacco leaves was analyzed with 302 samples in Bijie, Guizhou Province. The results showed that highly significant （P〈0.01） and significant （P〈0.05） positive correlation were observed between maturity and the ratio of total sugar to reducing sugar, and ratio of reducing sugar to nicotine, respectively. The oil content had highly significant （P〈0.01） negative and highly significant （P〈0.01） positive correlations with the ratio of total nitrogen to nicotine, ratio of reducing sugar to nicotine, respectively. Highly significant （P〈0.01） or significant （P〈0.05） negative correlations were detected between leaf degree with the ratio of total nitrogen to nicotine, ratio of reducing sugar to nicotine respectively, while highly significant （P〈0.01） positive correlation with reducing sugar, and total nitrogen, respectively. There existed highly significant （P〈0.01） or significant negative correlation （P〈0.05） between leaf structure with nicotine, starch, protein, or the ratio of reducing sugar to nicotine, respectively, while highly significant （P〈0.01） or significant positive （P〈0.05） correlation with reducing sugar or the ratio of total sugar to reducing sugar. Tobacco leaf chroma had highly significant negative or positive correlations （P〈0.01） with the ratio of total nitrogen to nicotine, or total sugar, respectively. A regression equation was established between appearance quality and main chemical components of flue-cured tobacco leaves.
出处
《中国烟草科学》
CSCD
2008年第1期30-32,共3页
Chinese Tobacco Science
基金
贵州省烟草专卖局基金项目[黔烟科(2004)-13]
关键词
初烤烟叶
外观质量
化学成分
回归分析
flue-cured tobacco leaves
appearance quality
chemical component
regression analysis
