摘要
利用ADY(Saccharomyces cerevisiae)对能源甜菜NY0503进行酒精发酵。应用Plackett-Burman设计法从底物浓度、料液比、加菌量、营养盐、加磷量、pH、转速、发酵温度和发酵时间9个因素中筛选出加磷量、发酵温度和底物浓度为主要影响因素。应用响应面分析法求得回归方程,得出最佳工艺为加磷量1.88%、发酵温度30.66℃、底物浓度11.53%,此工艺条件下酒精的最高理论转化率为96.54%。优化后按下列条件:底物浓度12%、料液比1:1、加菌量15%、营养盐0.5、加磷量1.9%、pH5.0、转速130r/min、发酵温度31℃和发酵时间44h进行5批次验证实验,酒精的平均转化率为95.48%,与模型的理论值96.54%的差值仅占理论值的1.09%,进一步说明所建立的模型是切实可行的。
Alcohol was produced through the fermentation of energy beet NY0503 by ADY (Saccharomyces cerevisiae). Three main influencing factors including phosphorus addition level, the fermentation temperature and substrate concentration were screening out by use of Plackett-Burman design method from nine influencing factors including substrate concentration, ratio of solvent to material, addition quantity of ADY, nutrient salts, addition quantity of phosphorus, pH values, the rotation speed, fermentation temperature and fermentation time. The regression equation was obtained by response surface method and the best technical parameters were calculated as follows: 1.88 % addition quantity of phosphorus, fermentation temperature at 30.66 ℃ ,and substrate concentration was 11.53 %. Under the above conditions, the highest conversion rate of alcohol could reach up to 96.54 % theoretically. 5 batches of experimental production were carried out according to the following optimized technical conditions in practice: 12 % substrate concentration, ratio of materials to liquid was 1:1, addition level of ADY was 15 %, nutrient salts as 0.5, addition quantity ofphorphorus was 1.9 %, pH value was 5.0, rotation speed was 130 r/min, fermentation temperature was at 31 ℃,and fermentation time was 44 h. The average conversion rate of alcohol was 95.48 %, only 1.09 % difference from the theoretical value of the models, which further proved that the established model was feasible.
出处
《酿酒科技》
2009年第5期17-21,共5页
Liquor-Making Science & Technology
基金
农业部寒地作物生理生态重点开放实验室开放基金
国家科技部专项基金(NCSTE-2006-JKZX-022)
关键词
酒精发酵
能源型甜菜
工艺优化
数学模型
响应面分析
alcohol fermentation
energy beet
technological optimization
mathematical model
response surface methodology