响应面分析法优化处理烟叶模块用酶配方的研究

Research on Optimizing Enzyme Formula for Treating Tobacco Leaf Module by Response Surface Methodology

为降解烟叶模块中的淀粉和蛋白质,以酶处理后烟叶模块的感官评吸得分为评价指标,采用响应面分析法优化了酶处理烟叶模块的最佳酶用量。结果表明:最佳酶用量为淀粉酶16U/g、糖化酶130U/g、风味蛋白酶68LAPU/g。此条件下处理后的烟叶模块感官评吸得分为59.50,与预测值59.27相近,说明响应面回归模型可靠。处理后的烟叶模块淀粉和蛋白质有所降解,降解率分别为24.47%和13.13%,感官抽吸品质明显改善。

In order to reduce the contents of starch and protein in tobacco leaf module and to improve the smoking quality,the best dose of enzyme added in tobacco leaf module was studied by response surface methodology with the sensory evaluation scores as the evaluation index. The results showed that optimum contents of amylase, glucoamylase and flavored proteinase were 16 U/g,130 U/g and 68 LAPU/g,respectively. After such optimum treatment,the sensory evaluation score reached to 59.50,very close to the predictive value of 59.27 ,which revealed the validity of the regression equation. Moreover, the content of starch and protein in tobacco leaf module respectively decreased by 24.47 % and 13.13% ,and the sensory characters were obviously enhanced.